From 486516796bd465c8714937ab6b71c43b72035cc8 Mon Sep 17 00:00:00 2001
From: ashwinmaran <amaran@wisc.edu>
Date: Sun, 31 Mar 2024 18:37:40 -0500
Subject: [PATCH] add lectures 26, 27, 28

---
 .../26_Files_and_Directories.pdf              |  Bin 0 -> 696312 bytes
 .../26_Files_and_Directories.pptx             |  Bin 0 -> 554576 bytes
 ...Lec26_Files_and_Directories_Solution.ipynb | 1204 +++++
 ...Lec26_Files_and_Directories_Template.ipynb |  801 +++
 .../Lecture Code/Lec27_Pandas1_Solution.ipynb | 1507 ++++++
 .../Lecture Code/Lec27_Pandas1_Template.ipynb |  790 +++
 .../Lecture Code/pokemon_stats.csv            |  886 ++++
 .../Lec_28_Pandas2_Worksheet.ipynb            |  878 ++++
 .../Lecture Code/IMDB-Movie-Data.csv          |  999 ++++
 .../Lecture Code/Lec28_Pandas2_Solution.ipynb | 4590 +++++++++++++++++
 .../Lecture Code/Lec28_Pandas2_Template.ipynb | 1219 +++++
 .../28_Pandas-2/lec-28-worksheet.pdf          |  Bin 0 -> 82792 bytes
 s24/AmFam_Ashwin/28_Pandas-2/storms.csv       |    6 +
 13 files changed, 12880 insertions(+)
 create mode 100644 s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pdf
 create mode 100644 s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pptx
 create mode 100644 s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Solution.ipynb
 create mode 100644 s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Template.ipynb
 create mode 100644 s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Solution.ipynb
 create mode 100644 s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Template.ipynb
 create mode 100644 s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/pokemon_stats.csv
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/Lec_28_Pandas2_Worksheet.ipynb
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/IMDB-Movie-Data.csv
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Solution.ipynb
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Template.ipynb
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/lec-28-worksheet.pdf
 create mode 100644 s24/AmFam_Ashwin/28_Pandas-2/storms.csv

diff --git a/s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pdf b/s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pdf
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diff --git a/s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pptx b/s24/AmFam_Ashwin/26_Files_and_Directories/26_Files_and_Directories.pptx
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HcmV?d00001

diff --git a/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Solution.ipynb b/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Solution.ipynb
new file mode 100644
index 0000000..204d1e8
--- /dev/null
+++ b/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Solution.ipynb	
@@ -0,0 +1,1204 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0a: Run these!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0b:\n",
+    "\n",
+    "1. Give an example of a mutable object\n",
+    "   \n",
+    "   **list, dict, set**\n",
+    "   \n",
+    "3. Give an example of an immutable object\n",
+    "   \n",
+    "   **string, tuple**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Create a type `Fruit` that has 3 attributes (use namedtuple)\n",
+    "- name\n",
+    "- num_seeds\n",
+    "- juicyness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define your Fruit type here\n",
+    "Fruit = namedtuple(\"Fruit\", ['name', 'num_seeds', 'juicyness'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 2: Add three fruits to the list. You choose!\n",
+    "- name should be a **string**\n",
+    "- num_seeds should be an **int**\n",
+    "- juicyness should be a **float** between `[0.0, 100.0]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruits = [\n",
+    "    Fruit(\"Apple\", 20, 25.0),\n",
+    "    Fruit(\"Pineapple\", 0, 75.0),\n",
+    "    Fruit(\"Strawberry\", 100, 45.0)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 3: Sort fruits based on juicyness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Fruit(name='Apple', num_seeds=20, juicyness=25.0),\n",
+       " Fruit(name='Strawberry', num_seeds=100, juicyness=45.0),\n",
+       " Fruit(name='Pineapple', num_seeds=0, juicyness=75.0)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort the list 'fruits' and display here\n",
+    "fruits.sort(key=lambda f:f.juicyness)\n",
+    "fruits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 4: Create a list of `Fruit` objects that have `>10` seeds using list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Fruit(name='Apple', num_seeds=20, juicyness=25.0),\n",
+       " Fruit(name='Strawberry', num_seeds=100, juicyness=45.0)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "[f for f in fruits if f.num_seeds > 10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 5: Same thing as above, but just the names of the fruit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Apple', 'Strawberry']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "[f.name for f in fruits if f.num_seeds > 10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 6: Same thing as above, but if it has `juicyness > 33`, display it in uppercase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Apple', 'STRAWBERRY']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "[f.name.upper() if f.juicyness > 33 else f.name for f in fruits if f.num_seeds > 10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 7: Create a dictionary mapping each fruit's name to its total number of seeds using dict comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Apple': 20, 'Strawberry': 100, 'Pineapple': 0}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "{f.name: f.num_seeds for f in fruits}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 8: Can you change the name of a fruit?\n",
+    "\n",
+    "**No**, namedtuples are **immutable**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "can't set attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                      Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[9], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# write your code here\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m fruits[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfail\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: can't set attribute"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "fruits[0].name = \"fail\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 9: Catch any errors the user may make, but allow the user to keep entering more fruits even if there is an error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current fruits []\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter 'y' if you want to add more fruits:  y\n",
+      "Enter a name for the fruit:  Apple\n",
+      "Enter a num_seeds for the fruit:  20\n",
+      "Enter a juicyness for the fruit:  25\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current fruits [Fruit(name='Apple', num_seeds=20, juicyness=25.0)]\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter 'y' if you want to add more fruits:  y\n",
+      "Enter a name for the fruit:  Mango\n",
+      "Enter a num_seeds for the fruit:  -1\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'ValueError'>\n",
+      "num_seeds must be a non-negative number\n",
+      "Current fruits [Fruit(name='Apple', num_seeds=20, juicyness=25.0)]\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter 'y' if you want to add more fruits:  y\n",
+      "Enter a name for the fruit:  Pineapple\n",
+      "Enter a num_seeds for the fruit:  0\n",
+      "Enter a juicyness for the fruit:  150\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'ValueError'>\n",
+      "juicyness must be a float between 0.0 and 100.0\n",
+      "Current fruits [Fruit(name='Apple', num_seeds=20, juicyness=25.0)]\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter 'y' if you want to add more fruits:  y\n",
+      "Enter a name for the fruit:  Strawberry\n",
+      "Enter a num_seeds for the fruit:  0.5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'ValueError'>\n",
+      "invalid literal for int() with base 10: '0.5'\n",
+      "Current fruits [Fruit(name='Apple', num_seeds=20, juicyness=25.0)]\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter 'y' if you want to add more fruits:  n\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_fruits = []\n",
+    "while True:\n",
+    "    print(\"Current fruits\", user_fruits)\n",
+    "    add_more_fruits = input(\"Enter 'y' if you want to add more fruits: \")\n",
+    "    if add_more_fruits != \"y\":\n",
+    "        break\n",
+    "    try:\n",
+    "        fruit_name = input(\"Enter a name for the fruit: \")\n",
+    "        fruit_seeds = int(input(\"Enter a num_seeds for the fruit: \"))\n",
+    "        if fruit_seeds < 0:\n",
+    "            raise ValueError(\"num_seeds must be a non-negative number\")\n",
+    "        fruit_juicy = float(input(\"Enter a juicyness for the fruit: \"))\n",
+    "        if not (0 <= fruit_juicy <= 100):\n",
+    "            raise ValueError(\"juicyness must be a float between 0.0 and 100.0\")\n",
+    "        user_fruits.append(Fruit(fruit_name, fruit_seeds, fruit_juicy))\n",
+    "    except Exception as e:\n",
+    "        print(type(e))\n",
+    "        print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture 26:  Files and Directories\n",
+    "\n",
+    "**Learning Objectives:**\n",
+    "\n",
+    " - Use built-in functions with files:\n",
+    "   - `open`\n",
+    "   - `close`\n",
+    "   - `read`\n",
+    "   - `write`\n",
+    " - Use file-specific os module functions:\n",
+    "   - `os.listdir`\n",
+    "   - `os.mkdir`\n",
+    "   - `os.path.exists`\n",
+    "   - `os.path.isfile`\n",
+    "   - `os.path.isdir`\n",
+    "   - `os.path.join`\n",
+    "\n",
+    " - Use `try/except` blocks or a with statement to handle errors that may occur when using files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use built-in functions with files: open, close, read, write"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1a: Let's learn how to write to a file\n",
+    "\n",
+    "Write this content to the file `debug_tips.txt`:\n",
+    "\n",
+    "```\n",
+    "Debug Tips:\n",
+    "1. Write the least amount of code you can test\n",
+    "2. Don't try to write the complete solution at first\n",
+    "3. Assume from the start that things will go wrong\n",
+    "4. Always know the state of your program (print out vital information)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open a file, give it a name, prepare it for writing\n",
+    "file_obj = open(\"debug_tips.txt\", \"w\")\n",
+    "\n",
+    "# use the write method to write a string to the file\n",
+    "file_obj.write(\"Debug Tips:\\n\")\n",
+    "file_obj.write(\"1. Write the least amount of code you can test\\n\")\n",
+    "\n",
+    "# TODO: Complete the rest!\n",
+    "file_obj.write(\"2. Don't try to write the complete solution at first\\n\")\n",
+    "file_obj.write(\"3. Assume from the start that things will go wrong\\n\")\n",
+    "file_obj.write(\"4. Always know the state of your program (print out vital information)\\n\")\n",
+    "\n",
+    "# you MUST close the file for the changes to actual be stored in the file\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1b: Let's learn how to write to a file\n",
+    "\n",
+    "Write 10 random ints (from 1 to 5) to a file `nums.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "nums_path = \"nums.txt\"\n",
+    "fo = open(nums_path, \"w\")\n",
+    "for i in range(10):\n",
+    "    fo.write(str(random.randint(1,5)) + \"\\n\")\n",
+    "fo.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Overwrite the file `nums.txt` by writing 10,000 random ints (from 1 to 10) to the same file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fo = open(nums_path, \"w\")\n",
+    "for i in range(10000):\n",
+    "    fo.write(str(random.randint(1,10)) + \"\\n\")\n",
+    "fo.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: What if we want to write one more debugging tip to `debug_tips.txt`?\n",
+    "\n",
+    "We'll come back to this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_obj = open(\"debug_tips.txt\", \"w\")\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading Files"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png](attachment:Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Read the contents of `debug_tips.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5. Writing a program is not a linear path; there will be ups and downs!\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create a file object but open for READING\n",
+    "path = \"debug_tips.txt\"\n",
+    "f_obj = open(path, 'r') # \"r\" is optional\n",
+    "\n",
+    "# use the read method to read the contents of the file\n",
+    "data = f_obj.read()\n",
+    "print(data)\n",
+    "\n",
+    "# you MUST close the file\n",
+    "f_obj.close()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4a: Add the fifth debugging tip, but do not overwrite what's currently in the file!\n",
+    "\n",
+    "Read the contents of the file before opening the file in `w` mode and write the already existing text first, before writing the new text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the already existing text...\n",
+    "path = \"debug_tips.txt\"\n",
+    "f_obj = open(path, 'r', encoding='utf-8') # \"r\" is optional\n",
+    "data = f_obj.read()\n",
+    "f_obj.close()\n",
+    "\n",
+    "file_obj = open(path, \"w\", encoding='utf-8')\n",
+    "# ... and add it on to the new.\n",
+    "file_obj.write(data)\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4b: Add the fifth debugging tip, but do not overwrite what's currently in the file!\n",
+    "\n",
+    "Append the new text directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_obj = open(path, \"a\", encoding='utf-8') # \"a\" stands for 'append'\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: Read the file `debug_tips.txt` as a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['5. Writing a program is not a linear path; there will be ups and downs!\\n', '5. Writing a program is not a linear path; there will be ups and downs!\\n', '5. Writing a program is not a linear path; there will be ups and downs!\\n']\n"
+     ]
+    }
+   ],
+   "source": [
+    "file_obj = open(\"debug_tips.txt\", encoding='utf-8')\n",
+    "my_data = list(file_obj)\n",
+    "print(my_data)\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Sum all the numbers in `nums.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55094"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "path = \"nums.txt\"\n",
+    "f_obj = open(path, encoding='utf-8')\n",
+    "lines = list(f_obj)\n",
+    "nums = []\n",
+    "for line in lines:\n",
+    "    actual_num = int(line.strip())\n",
+    "    nums.append(actual_num)\n",
+    "sum(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use these file-specific os module functions:\n",
+    "\n",
+    "- `os.listdir`\n",
+    "- `os.mkdir`\n",
+    "- `os.path.exists`\n",
+    "- `os.path.isfile`\n",
+    "- `os.path.isdir`\n",
+    "- `os.path.join`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.listdir` returns a list of the contents of a directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['.ipynb_checkpoints',\n",
+       " 'debug_tips.txt',\n",
+       " 'Lec26_Files_and_Directories_Solution.ipynb',\n",
+       " 'Lec26_Files_and_Directories_Template.ipynb',\n",
+       " 'nums.txt',\n",
+       " 'points.json']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir(\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.mkdir` makes a new directory\n",
+    "\n",
+    "Similar to mkdir command in PowerShell. Note if you run the same command twice, the 2nd time you will get an error. Why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['.ipynb_checkpoints',\n",
+       " \"ashwin's folder\",\n",
+       " 'debug_tips.txt',\n",
+       " 'Lec26_Files_and_Directories_Solution.ipynb',\n",
+       " 'Lec26_Files_and_Directories_Template.ipynb',\n",
+       " 'nums.txt',\n",
+       " 'points.json']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.mkdir(\"ashwin's folder\")\n",
+    "os.listdir(\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.path.exists`, `os.path.isfile`, and `os.path.isdir` checks if a file or a directory exists inside a directory\n",
+    "\n",
+    "`os.path` is a submodule of `os`, it does not need to be imported again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(os.path.exists(\"ashwin's folder\")) # does this file/folder (at this path) exist?\n",
+    "print(os.path.isfile(\"ashwin's folder\")) # does this file (at this path) exist?\n",
+    "print(os.path.isdir(\"ashwin's folder\")) # # does this folder (at this path) exist?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.path.join` is like the list join method, which combines things into a string\n",
+    "\n",
+    "It automatically senses which OS you are using and joins them with either a `\\` or `/`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "my_folder\\file1.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = os.path.join(\"my_folder\", \"file1.txt\")\n",
+    "print(path)  # what do you get? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**There are lots of things that can go wrong when reading or writing files**\n",
+    "- file does not exist\n",
+    "- you don't have write permission\n",
+    "- not enough space on the drive to write out the entire file\n",
+    "- use a directory name in place file name (or vice versa)\n",
+    "- and more...."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: let's figure out how to handle a command to open a file that does not exist\n",
+    "\n",
+    "Does this cover all exceptions? What else could go wrong? What does this mean for opening and closing a file?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "enter the name of the file to open: points.json\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"Ashwin\": 2\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = input(\"enter the name of the file to open:\")\n",
+    "try:\n",
+    "    file_object = open(path, \"r\", encoding='utf-8')  # \"r\" is for reading, but is the default\n",
+    "    d = file_object.read()\n",
+    "    print(d)\n",
+    "    file_object.close()\n",
+    "except FileNotFoundError as e:\n",
+    "    print(type(e))\n",
+    "    print(path, \"could not be opened\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python is all about shortening code. Is there a way to shorten the process of:**\n",
+    "- opening a file\n",
+    "- handling any Errors while reading/writing\n",
+    "- closing the file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: We can use a `with` statement to shorten our code\n",
+    "\n",
+    "We don't need to close, even if there's an error!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "with open(\"some_numbers.txt\", \"w\", encoding='utf-8') as f: \n",
+    "    for i in range(10):\n",
+    "        f.write(str(random.randint(1,100)) + \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Case Studies:  Mostly finished, larger programs**\n",
+    "\n",
+    "Goal:  Run the program, read the code, make small changes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case Study 1: Score Tracker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json  # to read/write a JSON file\n",
+    "import os    # to see if score file exists\n",
+    "\n",
+    "def read_json(path):\n",
+    "    with open(path, encoding=\"utf-8\") as f:\n",
+    "        return json.load(f) # dict, list, etc\n",
+    "\n",
+    "# data is a dict, list, etc\n",
+    "def write_json(path, data):\n",
+    "    with open(path, 'w', encoding=\"utf-8\") as f:\n",
+    "        json.dump(data, f, indent=2)\n",
+    "        \n",
+    "\n",
+    "def add_point(player_name, file_name):\n",
+    "    # TOOD: Check if `file_name` exists, if so load data from it\n",
+    "    if os.path.exists(file_name):\n",
+    "        scores = read_json(file_name)\n",
+    "    else:\n",
+    "        scores = {}\n",
+    "    # TODO: Check if player is a known player and increase their score by 1\n",
+    "    if player_name in scores:\n",
+    "        scores[player_name] += 1\n",
+    "    else:\n",
+    "        scores[player_name] = 1\n",
+    "    write_json(file_name, scores)\n",
+    "\n",
+    "    # TODO: print out each person's score\n",
+    "    print(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Ashwin': 3}\n"
+     ]
+    }
+   ],
+   "source": [
+    "add_point(\"Ashwin\", \"points.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Ashwin': 3, 'Jane': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "add_point(\"Jane\", \"points.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Ashwin': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "add_point(\"Ashwin\", \"more_points.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case Study 2: Recursive File Searcher\n",
+    "\n",
+    "In an earlier lecture, we wrote a function that recursively searches a list of lists. This program takes that idea and applies it to files and directories. To understand this program, you might want to first see what directories you have.\n",
+    "\n",
+    "Try exploring your computer with `os.listdir`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "def recursive_dir_search(search_directory, search_file_name):\n",
+    "    for curr in os.listdir(search_directory):   \n",
+    "        # build a path to this current thing\n",
+    "        curr = os.path.join(search_directory, curr) \n",
+    "        \n",
+    "        # check if curr is a file\n",
+    "        if os.path.isfile(curr):\n",
+    "            # check if it contains the search name \n",
+    "            if search_file_name in curr:     # base case...no recursive call\n",
+    "                f = open(curr, encoding='utf-8')\n",
+    "                contents = f.read()      # reads the contents of the file into a string\n",
+    "                f.close()\n",
+    "                return contents\n",
+    "        else:                              # recursive case!!\n",
+    "            contents = recursive_dir_search(curr, search_file_name)\n",
+    "            if contents != None:           # we found something\n",
+    "                return contents           \n",
+    "            \n",
+    "    # finished all recursive searching and never found it   \n",
+    "    return None       \n",
+    "\n",
+    "\n",
+    "# this function is like our main program\n",
+    "def dir_search(dir_name, file_name):\n",
+    "    if not os.path.exists(dir_name):\n",
+    "        print(\"Unable to find search_directory!\")\n",
+    "    else:\n",
+    "        contents = recursive_dir_search(dir_name, file_name)\n",
+    "        if contents == None:\n",
+    "            print(file_name, \"not found\")\n",
+    "        else:\n",
+    "            print(contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "project.py not found\n"
+     ]
+    }
+   ],
+   "source": [
+    "dir_search(\".\", \"project.py\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__student__ = []\n",
+      "\n",
+      "\n",
+      "def __init__():\n",
+      "    import csv\n",
+      "    \"\"\"This function will read in the csv_file and store it in a list of dictionaries\"\"\"\n",
+      "    __student__.clear()\n",
+      "    with open('cs220_survey_data.csv', mode='r', encoding='utf-8') as csv_file:\n",
+      "        csv_reader = csv.DictReader(csv_file)\n",
+      "        for row in csv_reader:\n",
+      "            __student__.append(row)\n",
+      "\n",
+      "def count():\n",
+      "    \"\"\"This function will return the number of records in the dataset\"\"\"\n",
+      "    return len(__student__)\n",
+      "\n",
+      "\n",
+      "def get_lecture(idx):\n",
+      "    \"\"\"get_lecture(idx) returns the lecture of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Lecture']\n",
+      "\n",
+      "\n",
+      "def get_section(idx):\n",
+      "    \"\"\"get_lecture(idx) returns the section of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['section']\n",
+      "\n",
+      "\n",
+      "def get_age(idx):\n",
+      "    \"\"\"get_age(idx) returns the age of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Age']\n",
+      "\n",
+      "\n",
+      "def get_primary_major(idx):\n",
+      "    \"\"\"get_primary_major(idx) returns the primary major of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Primary major']\n",
+      "\n",
+      "\n",
+      "def get_other_majors(idx):\n",
+      "    \"\"\"get_other_majors(idx) returns the secondary major of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Other majors']\n",
+      "\n",
+      "\n",
+      "def get_zip_code(idx):\n",
+      "    \"\"\"get_zip_code(idx) returns the residential zip code of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Zip Code']\n",
+      "\n",
+      "\n",
+      "def get_pizza_topping(idx):\n",
+      "    \"\"\"get_pizza_topping(idx) returns the preferred pizza toppings of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Pizza topping']\n",
+      "\n",
+      "def get_cats_or_dogs(idx):\n",
+      "    \"\"\"get_cats_or_dogs(idx) returns whether student in row idx likes cats or dogs\"\"\"\n",
+      "    return __student__[int(idx)]['Cats or dogs']\n",
+      "\n",
+      "def get_runner(idx):\n",
+      "    \"\"\"get_runner(idx) returns whether student in row idx is a runner\"\"\"\n",
+      "    return __student__[int(idx)]['Runner']\n",
+      "\n",
+      "def get_sleep_habit(idx):\n",
+      "    \"\"\"get_sleep_habit(idx) returns the sleep habit of the student in row idx\"\"\"\n",
+      "    return __student__[int(idx)]['Sleep habit']\n",
+      "\n",
+      "def get_procrastinator(idx):\n",
+      "    \"\"\"get_procrastinator(idx) returns whether student in row idx is a procrastinator\"\"\"\n",
+      "    return __student__[int(idx)]['Procrastinator']\n",
+      "\n",
+      "\n",
+      "def get_song(idx):\n",
+      "    \"\"\"get_procrastinator(idx) returns the student in row idx favorite song\"\"\"\n",
+      "    return __student__[int(idx)]['Song']\n",
+      "\n",
+      "\n",
+      "__init__()\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "dir_search(\"..\", \"project.py\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Template.ipynb b/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Template.ipynb
new file mode 100644
index 0000000..e1fa58c
--- /dev/null
+++ b/s24/AmFam_Ashwin/26_Files_and_Directories/Lecture Code/Lec26_Files_and_Directories_Template.ipynb	
@@ -0,0 +1,801 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0a: Run these!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0b:\n",
+    "\n",
+    "1. Give an example of a mutable object \n",
+    "3. Give an example of an immutable object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Create a type `Fruit` that has 3 attributes (use namedtuple)\n",
+    "- name\n",
+    "- num_seeds\n",
+    "- juicyness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define your Fruit type here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 2: Add three fruits to the list. You choose!\n",
+    "- name should be a **string**\n",
+    "- num_seeds should be an **int**\n",
+    "- juicyness should be a **float** between `[0.0, 100.0]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruits = [\n",
+    "    ...\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 3: Sort fruits based on juicyness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# sort the list 'fruits' and display here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 4: Create a list of `Fruit` objects that have `>10` seeds using list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 5: Same thing as above, but just the names of the fruit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 6: Same thing as above, but if it has `juicyness > 33`, display it in uppercase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 7: Create a dictionary mapping each fruit's name to its total number of seeds using dict comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 8: Can you change the name of a fruit?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 9: Catch any errors the user may make, but allow the user to keep entering more fruits even if there is an error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_fruits = []\n",
+    "while True:\n",
+    "    print(\"Current fruits\", user_fruits)\n",
+    "    add_more_fruits = input(\"Enter 'y' if you want to add more fruits: \")\n",
+    "    if add_more_fruits != \"y\":\n",
+    "        break\n",
+    "    fruit_name = input(\"Enter a name for the fruit: \")\n",
+    "    fruit_seeds = int(input(\"Enter a num_seeds for the fruit: \"))\n",
+    "    fruit_juicy = float(input(\"Enter a juicyness for the fruit: \"))\n",
+    "    user_fruits.append(Fruit(fruit_name, fruit_seeds, fruit_juicy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture 26:  Files and Directories\n",
+    "\n",
+    "**Learning Objectives:**\n",
+    "\n",
+    " - Use built-in functions with files:\n",
+    "   - `open`\n",
+    "   - `close`\n",
+    "   - `read`\n",
+    "   - `write`\n",
+    " - Use file-specific os module functions:\n",
+    "   - `os.listdir`\n",
+    "   - `os.mkdir`\n",
+    "   - `os.path.exists`\n",
+    "   - `os.path.isfile`\n",
+    "   - `os.path.isdir`\n",
+    "   - `os.path.join`\n",
+    "\n",
+    " - Use `try/except` blocks or a with statement to handle errors that may occur when using files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use built-in functions with files: open, close, read, write"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1a: Let's learn how to write to a file\n",
+    "\n",
+    "Write this content to the file `debug_tips.txt`:\n",
+    "\n",
+    "```\n",
+    "Debug Tips:\n",
+    "1. Write the least amount of code you can test\n",
+    "2. Don't try to write the complete solution at first\n",
+    "3. Assume from the start that things will go wrong\n",
+    "4. Always know the state of your program (print out vital information)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# open a file, give it a name, prepare it for writing\n",
+    "file_obj = open(\"debug_tips.txt\", \"w\")\n",
+    "\n",
+    "# use the write method to write a string to the file\n",
+    "file_obj.write(\"Debug Tips:\\n\")\n",
+    "file_obj.write(\"1. Write the least amount of code you can test\\n\")\n",
+    "\n",
+    "# TODO: Complete the rest!\n",
+    "\n",
+    "# you MUST close the file for the changes to actual be stored in the file\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1b: Let's learn how to write to a file\n",
+    "\n",
+    "Write 10 random ints (from 1 to 5) to a file `nums.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "nums_path = \"nums.txt\"\n",
+    "fo = open(nums_path, \"w\")\n",
+    "for i in range(10):\n",
+    "    fo.write(str(random.randint(1,5)) + \"\\n\")\n",
+    "fo.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Overwrite the file `nums.txt` by writing 10,000 random ints (from 1 to 10) to the same file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fo = open(nums_path, \"w\")\n",
+    "for i in range(10000):\n",
+    "    fo.write(str(random.randint(1,10)) + \"\\n\")\n",
+    "fo.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: What if we want to write one more debugging tip to `debug_tips.txt`?\n",
+    "\n",
+    "We'll come back to this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_obj = open(\"debug_tips.txt\", \"w\")\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reading Files"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png](attachment:Screen%20Shot%202021-11-08%20at%208.30.02%20AM.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Read the contents of `debug_tips.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a file object but open for READING\n",
+    "path = \"debug_tips.txt\"\n",
+    "f_obj = open(path, 'r') # \"r\" is optional\n",
+    "\n",
+    "# use the read method to read the contents of the file\n",
+    "data = f_obj.read()\n",
+    "print(data)\n",
+    "\n",
+    "# you MUST close the file\n",
+    "f_obj.close()"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4a: Add the fifth debugging tip, but do not overwrite what's currently in the file!\n",
+    "\n",
+    "Read the contents of the file before opening the file in `w` mode and write the already existing text first, before writing the new text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in the already existing text...\n",
+    "path = \"debug_tips.txt\"\n",
+    "f_obj = open(path, 'r', encoding='utf-8') # \"r\" is optional\n",
+    "data = f_obj.read()\n",
+    "f_obj.close()\n",
+    "\n",
+    "file_obj = open(path, \"w\", encoding='utf-8')\n",
+    "# ... and add it on to the new.\n",
+    "file_obj.write(data)\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4b: Add the fifth debugging tip, but do not overwrite what's currently in the file!\n",
+    "\n",
+    "Append the new text directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_obj = open(path, \"a\", encoding='utf-8') # \"a\" stands for 'append'\n",
+    "file_obj.write(\"5. Writing a program is not a linear path; there will be ups and downs!\\n\")\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: Read the file `debug_tips.txt` as a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_obj = open(\"debug_tips.txt\", encoding='utf-8')\n",
+    "my_data = list(file_obj)\n",
+    "print(my_data)\n",
+    "file_obj.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Sum all the numbers in `nums.txt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use these file-specific os module functions:\n",
+    "\n",
+    "- `os.listdir`\n",
+    "- `os.mkdir`\n",
+    "- `os.path.exists`\n",
+    "- `os.path.isfile`\n",
+    "- `os.path.isdir`\n",
+    "- `os.path.join`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.listdir` returns a list of the contents of a directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.listdir(\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.mkdir` makes a new directory\n",
+    "\n",
+    "Similar to mkdir command in PowerShell. Note if you run the same command twice, the 2nd time you will get an error. Why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.mkdir(\"ashwin's folder\")\n",
+    "os.listdir(\".\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.path.exists`, `os.path.isfile`, and `os.path.isdir` checks if a file or a directory exists inside a directory\n",
+    "\n",
+    "`os.path` is a submodule of `os`, it does not need to be imported again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(os.path.exists(\"ashwin's folder\")) # does this file/folder (at this path) exist?\n",
+    "print(os.path.isfile(\"ashwin's folder\")) # does this file (at this path) exist?\n",
+    "print(os.path.isdir(\"ashwin's folder\")) # # does this folder (at this path) exist?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `os.path.join` is like the list join method, which combines things into a string\n",
+    "\n",
+    "It automatically senses which OS you are using and joins them with either a `\\\\` or `/`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = os.path.join(\"my_folder\", \"file1.txt\")\n",
+    "print(path)  # what do you get? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**There are lots of things that can go wrong when reading or writing files**\n",
+    "- file does not exist\n",
+    "- you don't have write permission\n",
+    "- not enough space on the drive to write out the entire file\n",
+    "- use a directory name in place file name (or vice versa)\n",
+    "- and more...."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: let's figure out how to handle a command to open a file that does not exist\n",
+    "\n",
+    "Does this cover all exceptions? What else could go wrong? What does this mean for opening and closing a file?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = input(\"enter the name of the file to open:\")\n",
+    "try:\n",
+    "    file_object = open(path, \"r\", encoding='utf-8')  # \"r\" is for reading, but is the default\n",
+    "    d = file_object.read()\n",
+    "    print(d)\n",
+    "    file_object.close()\n",
+    "except FileNotFoundError as e:\n",
+    "    print(type(e))\n",
+    "    print(path, \"could not be opened\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python is all about shortening code. Is there a way to shorten the process of:**\n",
+    "- opening a file\n",
+    "- handling any Errors while reading/writing\n",
+    "- closing the file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: We can use a `with` statement to shorten our code\n",
+    "\n",
+    "We don't need to close, even if there's an error!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "with open(\"some_numbers.txt\", \"w\", encoding='utf-8') as f: \n",
+    "    for i in range(10):\n",
+    "        f.write(str(random.randint(1,100)) + \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Case Studies:  Mostly finished, larger programs**\n",
+    "\n",
+    "Goal:  Run the program, read the code, make small changes\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case Study 1: Score Tracker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json  # to read/write a JSON file\n",
+    "import os    # to see if score file exists\n",
+    "\n",
+    "def read_json(path):\n",
+    "    with open(path, encoding=\"utf-8\") as f:\n",
+    "        return json.load(f) # dict, list, etc\n",
+    "\n",
+    "# data is a dict, list, etc\n",
+    "def write_json(path, data):\n",
+    "    with open(path, 'w', encoding=\"utf-8\") as f:\n",
+    "        json.dump(data, f, indent=2)\n",
+    "        \n",
+    "\n",
+    "def add_point(player_name, file_name):\n",
+    "    # TOOD: Check if `file_name` exists, if so load data from it\n",
+    "    if os.path.exists(file_name):\n",
+    "        scores = read_json(file_name)\n",
+    "    else:\n",
+    "        scores = {}\n",
+    "    # TODO: Check if player is a known player and increase their score by 1\n",
+    "    if player_name in scores:\n",
+    "        scores[player_name] += 1\n",
+    "    else:\n",
+    "        scores[player_name] = 1\n",
+    "    write_json(file_name, scores)\n",
+    "\n",
+    "    # TODO: print out each person's score\n",
+    "    print(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_point(\"Ashwin\", \"points.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_point(\"Jane\", \"points.json\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "add_point(\"Ashwin\", \"more_points.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Case Study 2: Recursive File Searcher\n",
+    "\n",
+    "In an earlier lecture, we wrote a function that recursively searches a list of lists. This program takes that idea and applies it to files and directories. To understand this program, you might want to first see what directories you have.\n",
+    "\n",
+    "Try exploring your computer with `os.listdir`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "def recursive_dir_search(search_directory, search_file_name):\n",
+    "    for curr in os.listdir(search_directory):   \n",
+    "        # build a path to this current thing\n",
+    "        curr = os.path.join(search_directory, curr) \n",
+    "        \n",
+    "        # check if curr is a file\n",
+    "        if os.path.isfile(curr):\n",
+    "            # check if it contains the search name \n",
+    "            if search_file_name in curr:     # base case...no recursive call\n",
+    "                f = open(curr, encoding='utf-8')\n",
+    "                contents = f.read()      # reads the contents of the file into a string\n",
+    "                f.close()\n",
+    "                return contents\n",
+    "        else:                              # recursive case!!\n",
+    "            contents = recursive_dir_search(curr, search_file_name)\n",
+    "            if contents != None:           # we found something\n",
+    "                return contents           \n",
+    "            \n",
+    "    # finished all recursive searching and never found it   \n",
+    "    return None       \n",
+    "\n",
+    "\n",
+    "# this function is like our main program\n",
+    "def dir_search(dir_name, file_name):\n",
+    "    if not os.path.exists(dir_name):\n",
+    "        print(\"Unable to find search_directory!\")\n",
+    "    else:\n",
+    "        contents = recursive_dir_search(dir_name, file_name)\n",
+    "        if contents == None:\n",
+    "            print(file_name, \"not found\")\n",
+    "        else:\n",
+    "            print(contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir_search(\".\", \"project.py\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir_search(\"..\", \"project.py\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Solution.ipynb b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Solution.ipynb
new file mode 100644
index 0000000..0958346
--- /dev/null
+++ b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Solution.ipynb	
@@ -0,0 +1,1507 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0: Importing Pandas!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Find the mean, median, mode, and standard deviation of the following list of scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_scores = [44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50.54545454545455 47 23 26.0513478974261\n"
+     ]
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "import statistics\n",
+    "\n",
+    "scores_mean = statistics.mean(my_scores)\n",
+    "scores_median = statistics.median(my_scores)\n",
+    "scores_mode = statistics.mode(my_scores)\n",
+    "scores_stdev = statistics.stdev(my_scores)\n",
+    "\n",
+    "print(scores_mean, scores_median, scores_mode, scores_stdev)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "- Create a pandas **Series** from a **list** or from a **dict**,\n",
+    "- Use **Series** methods `max`, `min`, `mean`, `median`, `mode`, `quantile`, `value_counts`,\n",
+    "- Extract elements from a **Series** using **Boolean indexing**,\n",
+    "- Access **Series** members using `.loc`, `.iloc`, `.items`, and slicing,\n",
+    "- Perform **Series** element-wise operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What is Pandas?**\n",
+    " - Pandas is a package of tools for doing Data Science\n",
+    " - Pandas was installed with Anaconda, so its on your computers\n",
+    " - [Learn More](https://en.wikipedia.org/wiki/Pandas_(software))\n",
+    " \n",
+    "If for some reason, you don't have pandas installed, run the following command in terminal or powershell...\n",
+    "<pre>pip install pandas</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Pandas Series is like a combination of a list and a dictionary. The word 'index' is used to describe position.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series from a `list`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     44\n",
+       "1     32\n",
+       "2     19\n",
+       "3     67\n",
+       "4     23\n",
+       "5     23\n",
+       "6     92\n",
+       "7     47\n",
+       "8     47\n",
+       "9     78\n",
+       "10    84\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores = pd.Series([44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84])\n",
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Pandas series acts a lot like a list; you can index and slice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "67"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    67\n",
+       "4    23\n",
+       "5    23\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores[3:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series calculations: mean, median, mode, quartiles, sd, count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `mean`, `median`, and `std` return the mean, median, and standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50.54545454545455\n",
+      "47.0\n",
+      "26.051347897426098\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores.mean())\n",
+    "print(scores.median())\n",
+    "print(scores.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### There could be multiple modes, so `mode` returns a Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    23\n",
+      "1    47\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores.mode())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `quantile` returns a Series of the numbers at each specified quantile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.00    92.0\n",
+      "0.75    72.5\n",
+      "0.50    47.0\n",
+      "0.25    27.5\n",
+      "0.00    19.0\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores.quantile([1.0, 0.75, 0.5, 0.25, 0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9    84.0\n",
+      "0.1    23.0\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(scores.quantile([0.9, 0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `value_counts` creates a series where the index is the data, and the value is its count in the series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    6\n",
+       "19    4\n",
+       "21    3\n",
+       "18    2\n",
+       "23    2\n",
+       "17    1\n",
+       "24    1\n",
+       "25    1\n",
+       "35    1\n",
+       "22    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ages = pd.Series([18, 19, 20, 20, 20, 17, 18, 24, 25, 35, 22, 20, 21, 21, 20, 23, 23, 19, 19, 19, 20, 21])\n",
+    "ages.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A series can be sorted by index or by values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17    1\n",
+       "18    2\n",
+       "19    4\n",
+       "20    6\n",
+       "21    3\n",
+       "22    1\n",
+       "23    2\n",
+       "24    1\n",
+       "25    1\n",
+       "35    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ages.value_counts().sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    6\n",
+       "19    4\n",
+       "21    3\n",
+       "18    2\n",
+       "23    2\n",
+       "17    1\n",
+       "24    1\n",
+       "25    1\n",
+       "35    1\n",
+       "22    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ages.value_counts().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series bar chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0, 'age'), Text(0, 0.5, 'count')]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_plot = ages.value_counts().sort_index().plot.bar(color='lightsalmon')\n",
+    "age_plot.set(xlabel=\"age\", ylabel=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: What ages are at least 21?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7     24\n",
+       "8     25\n",
+       "9     35\n",
+       "10    22\n",
+       "12    21\n",
+       "13    21\n",
+       "15    23\n",
+       "16    23\n",
+       "21    21\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "at_least_21 = ages[ages >= 21]\n",
+    "at_least_21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: What ages are exactly 18?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    18\n",
+       "6    18\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "exactly_18 = ages[ages == 18]\n",
+    "exactly_18"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Filtering\n",
+    " - `&` means `and`\n",
+    " - `|` means `or`\n",
+    " - `~` means `not`\n",
+    " - we must use `()` for compound boolean expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: What ages are in the range 18 to 20, inclusive?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     18\n",
+       "1     19\n",
+       "2     20\n",
+       "3     20\n",
+       "4     20\n",
+       "6     18\n",
+       "11    20\n",
+       "14    20\n",
+       "17    19\n",
+       "18    19\n",
+       "19    19\n",
+       "20    20\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "certain_students = ages[(ages >= 18) & (ages <= 20)]\n",
+    "certain_students"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: What percentage of students are in this age range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5454545454545454"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "len(certain_students) / len(ages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: What percentage of students are ages 18 OR 21?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.36363636363636365"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "students_18_or_21 = ages[(ages == 18) | (ages == 20)]\n",
+    "len(students_18_or_21) / len(ages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: What percentage of students are NOT 19?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8181818181818182"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "students_not_19 = ages[~(ages==19)]\n",
+    "len(students_not_19) / len(ages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One more thing....\n",
+    "\n",
+    "We can perform an operation on all values in a Series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Add 1 to everyone's age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21    6\n",
+       "20    4\n",
+       "22    3\n",
+       "19    2\n",
+       "24    2\n",
+       "18    1\n",
+       "25    1\n",
+       "26    1\n",
+       "36    1\n",
+       "23    1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ages += 1\n",
+    "ages.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using a Series to store Pokemon stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "886\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[['15',\n",
+       "  'Pidgey',\n",
+       "  '45',\n",
+       "  '40',\n",
+       "  '40',\n",
+       "  'Kanto',\n",
+       "  '35',\n",
+       "  '35',\n",
+       "  '56',\n",
+       "  'Normal',\n",
+       "  'Flying'],\n",
+       " ['16',\n",
+       "  'Pidgeotto',\n",
+       "  '60',\n",
+       "  '55',\n",
+       "  '63',\n",
+       "  'Kanto',\n",
+       "  '50',\n",
+       "  '50',\n",
+       "  '71',\n",
+       "  'Normal',\n",
+       "  'Flying'],\n",
+       " ['17',\n",
+       "  'Pidgeot',\n",
+       "  '80',\n",
+       "  '75',\n",
+       "  '83',\n",
+       "  'Kanto',\n",
+       "  '70',\n",
+       "  '70',\n",
+       "  '101',\n",
+       "  'Normal',\n",
+       "  'Flying']]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Modified from https://automatetheboringstuff.com/chapter14/\n",
+    "import csv\n",
+    "def process_csv(filename):\n",
+    "    example_file = open(filename, encoding=\"utf-8\")\n",
+    "    example_reader = csv.reader(example_file)\n",
+    "    example_data = list(example_reader)\n",
+    "    example_file.close()\n",
+    "    return example_data\n",
+    "\n",
+    "data = process_csv(\"pokemon_stats.csv\")\n",
+    "header = data[0]\n",
+    "print(len(data))\n",
+    "data = data[1:]\n",
+    "data[15:18]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4: Create a Series of all the Pokemon names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       Bulbasaur\n",
+       "1         Ivysaur\n",
+       "2        Venusaur\n",
+       "3      Charmander\n",
+       "4      Charmeleon\n",
+       "          ...    \n",
+       "880     Regieleki\n",
+       "881     Regidrago\n",
+       "882     Glastrier\n",
+       "883     Spectrier\n",
+       "884       Calyrex\n",
+       "Length: 885, dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon_list = [row[1] for row in data]\n",
+    "pokemon_names = pd.Series(pokemon_list)\n",
+    "pokemon_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5: Create a Series of all the Pokemon HPs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       45\n",
+       "1       60\n",
+       "2       80\n",
+       "3       39\n",
+       "4       58\n",
+       "      ... \n",
+       "880     80\n",
+       "881    200\n",
+       "882    100\n",
+       "883    100\n",
+       "884    100\n",
+       "Length: 885, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "hp_list = [int(row[4]) for row in data]\n",
+    "hps = pd.Series(hp_list)\n",
+    "hps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 6: Find the most common HP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "60"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "hps.mode()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 7: Find how many Pokemon have that most common HP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here \n",
+    "len(hps[hps == hps.mode()[0]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 8: How many Pokemon have HP between 50 and 75 (inclusive)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "427"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here \n",
+    "len(hps[(hps >= 50) & (hps <= 75)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: What are the names of weak Pokemon (`< 30` HP)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "47       Diglett\n",
+       "60          Abra\n",
+       "78     Magnemite\n",
+       "124     Magikarp\n",
+       "167        Pichu\n",
+       "208      Shuckle\n",
+       "275        Ralts\n",
+       "287     Shedinja\n",
+       "344       Feebas\n",
+       "350      Duskull\n",
+       "760       Wimpod\n",
+       "812      Blipbug\n",
+       "871       Dreepy\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "weak_hps_idx = hps[hps < 30].index\n",
+    "pokemon_names[weak_hps_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 9: What are the names of the Pokemon from strongest to weakest (using HP)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "237      Blissey\n",
+       "109      Chansey\n",
+       "787     Guzzlord\n",
+       "881    Regidrago\n",
+       "197    Wobbuffet\n",
+       "         ...    \n",
+       "350      Duskull\n",
+       "124     Magikarp\n",
+       "208      Shuckle\n",
+       "47       Diglett\n",
+       "287     Shedinja\n",
+       "Length: 885, dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "all_hps_desc = hps.sort_values(ascending=False)\n",
+    "pokemon_names[all_hps_desc.index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series from a `dict`\n",
+    "A Series is a cross between a list and a dict, so we can make a series from a dict as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Chris      10\n",
+      "Kiara       3\n",
+      "Mikayla     7\n",
+      "Ann         8\n",
+      "Trish       6\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "game1_points = pd.Series({\"Chris\": 10, \"Kiara\": 3, \"Mikayla\": 7, \"Ann\": 8, \"Trish\": 6})\n",
+    "print(game1_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kiara       7\n",
+      "Chris       3\n",
+      "Trish      11\n",
+      "Mikayla     2\n",
+      "Ann         5\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "game2_points = pd.Series({\"Kiara\": 7, \"Chris\": 3,  \"Trish\": 11, \"Mikayla\": 2, \"Ann\": 5})\n",
+    "print(game2_points)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pandas can perform operations on two series by matching up their indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ann        13\n",
+       "Chris      13\n",
+       "Kiara      10\n",
+       "Mikayla     9\n",
+       "Trish      17\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = game1_points  + game2_points\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: Who has the most points in total?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "17\n",
+      "Trish\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(total.max())\n",
+    "print(total.idxmax())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can use `[]` to index the name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total['Kiara']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can also use `[]` to index by the sequence number, but this should be avoided, and this feature will not be available in future versions of Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Ashwin Maran\\AppData\\Local\\Temp\\ipykernel_10356\\3410743498.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
+      "  total[2]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can have multi-indexing, slightly different from slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chris    13\n",
+       "Trish    17\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total[[\"Chris\", \"Trish\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More plotting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Trish      17\n",
+       "Ann        13\n",
+       "Chris      13\n",
+       "Kiara      10\n",
+       "Mikayla     9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_sorted = total.sort_values(ascending=False)\n",
+    "total_sorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'total points')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = total_sorted.plot.bar(color=\"green\", fontsize=16)\n",
+    "ax.set_ylabel(\"total points\", fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More things to know about Series\n",
+    "Next, we'll get into more ways to access data using `loc` and `iloc`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Chris      10\n",
+       "Kiara       3\n",
+       "Mikayla     7\n",
+       "Ann         8\n",
+       "Trish       6\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "game1_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "game1_points.iloc[2] # looks up by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "game1_points.loc[\"Mikayla\"] # looks up by pandas index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     89\n",
+       "2    104\n",
+       "3    681\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_new_series = pd.Series({1: 89, 2: 104, 3: 681}) # this can be tricky!\n",
+    "my_new_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "104"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_new_series.iloc[1] # by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "89"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_new_series.loc[1] # by index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "89"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_new_series[1] # by index!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    104\n",
+       "3    681\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_new_series[my_new_series > 100] # ... and also boolean indexing!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feel overwhelmed? Do the required reading."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Template.ipynb b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Template.ipynb
new file mode 100644
index 0000000..a943cd0
--- /dev/null
+++ b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/Lec27_Pandas1_Template.ipynb	
@@ -0,0 +1,790 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0: Importing Pandas!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Find the mean, median, mode, and standard deviation of the following list of scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_scores = [44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "- Create a pandas **Series** from a **list** or from a **dict**,\n",
+    "- Use **Series** methods `max`, `min`, `mean`, `median`, `mode`, `quantile`, `value_counts`,\n",
+    "- Extract elements from a **Series** using **Boolean indexing**,\n",
+    "- Access **Series** members using `.loc`, `.iloc`, `.items`, and slicing,\n",
+    "- Perform **Series** element-wise operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**What is Pandas?**\n",
+    " - Pandas is a package of tools for doing Data Science\n",
+    " - Pandas was installed with Anaconda, so its on your computers\n",
+    " - [Learn More](https://en.wikipedia.org/wiki/Pandas_(software))\n",
+    " \n",
+    "If for some reason, you don't have pandas installed, run the following command in terminal or powershell...\n",
+    "<pre>pip install pandas</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Pandas Series is like a combination of a list and a dictionary. The word 'index' is used to describe position.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series from a `list`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = pd.Series([44, 32, 19, 67, 23, 23, 92, 47, 47, 78, 84])\n",
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A Pandas series acts a lot like a list; you can index and slice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores[3:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Series calculations: mean, median, mode, quartiles, sd, count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `mean`, `median`, and `std` return the mean, median, and standard deviation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(scores.mean())\n",
+    "print(scores.median())\n",
+    "print(scores.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### There could be multiple modes, so `mode` returns a Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(scores.mode())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `quantile` returns a Series of the numbers at each specified quantile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(scores.quantile([1.0, 0.75, 0.5, 0.25, 0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(scores.quantile([0.9, 0.1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### `value_counts` creates a series where the index is the data, and the value is its count in the series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ages = pd.Series([18, 19, 20, 20, 20, 17, 18, 24, 25, 35, 22, 20, 21, 21, 20, 23, 23, 19, 19, 19, 20, 21])\n",
+    "ages.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A series can be sorted by index or by values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ages.value_counts().sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ages.value_counts().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series bar chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "age_plot = ages.value_counts().sort_index().plot.bar(color='lightsalmon')\n",
+    "age_plot.set(xlabel=\"age\", ylabel=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: What ages are at least 21?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "at_least_21 = ages[ages >= 21]\n",
+    "at_least_21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: What ages are exactly 18?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced Filtering\n",
+    " - `&` means `and`\n",
+    " - `|` means `or`\n",
+    " - `~` means `not`\n",
+    " - we must use `()` for compound boolean expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: What ages are in the range 18 to 20, inclusive?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "certain_students = ages[(ages >= 18) & (ages <= 20)]\n",
+    "certain_students"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: What percentage of students are in this age range?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: What percentage of students are ages 18 OR 21?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: What percentage of students are NOT 19?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One more thing....\n",
+    "\n",
+    "We can perform an operation on all values in a Series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: Add 1 to everyone's age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ages += 1\n",
+    "ages.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using a Series to store Pokemon stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Modified from https://automatetheboringstuff.com/chapter14/\n",
+    "import csv\n",
+    "def process_csv(filename):\n",
+    "    example_file = open(filename, encoding=\"utf-8\")\n",
+    "    example_reader = csv.reader(example_file)\n",
+    "    example_data = list(example_reader)\n",
+    "    example_file.close()\n",
+    "    return example_data\n",
+    "\n",
+    "data = process_csv(\"pokemon_stats.csv\")\n",
+    "header = data[0]\n",
+    "print(len(data))\n",
+    "data = data[1:]\n",
+    "data[15:18]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4: Create a Series of all the Pokemon names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pokemon_list = [row[1] for row in data]\n",
+    "pokemon_names = pd.Series(pokemon_list)\n",
+    "pokemon_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5: Create a Series of all the Pokemon HPs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 6: Find the most common HP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 7: Find how many Pokemon have that most common HP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 8: How many Pokemon have HP between 50 and 75 (inclusive)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: What are the names of weak Pokemon (`< 30` HP)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weak_hps_idx = hps[hps < 30].index\n",
+    "pokemon_names[weak_hps_idx]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 9: What are the names of the Pokemon from strongest to weakest (using HP)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Series from a `dict`\n",
+    "A Series is a cross between a list and a dict, so we can make a series from a dict as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game1_points = pd.Series({\"Chris\": 10, \"Kiara\": 3, \"Mikayla\": 7, \"Ann\": 8, \"Trish\": 6})\n",
+    "print(game1_points)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game2_points = pd.Series({\"Kiara\": 7, \"Chris\": 3,  \"Trish\": 11, \"Mikayla\": 2, \"Ann\": 5})\n",
+    "print(game2_points)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pandas can perform operations on two series by matching up their indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total = game1_points  + game2_points\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: Who has the most points in total?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(total.max())\n",
+    "print(total.idxmax())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can use `[]` to index the name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total['Kiara']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can also use `[]` to index by the sequence number, but this should be avoided, and this feature will not be available in future versions of Pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We can have multi-indexing, slightly different from slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total[[\"Chris\", \"Trish\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More plotting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_sorted = total.sort_values(ascending=False)\n",
+    "total_sorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = total_sorted.plot.bar(color=\"green\", fontsize=16)\n",
+    "ax.set_ylabel(\"total points\", fontsize=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More things to know about Series\n",
+    "Next, we'll get into more ways to access data using `loc` and `iloc`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game1_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game1_points.iloc[2] # looks up by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "game1_points.loc[\"Mikayla\"] # looks up by pandas index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_new_series = pd.Series({1: 89, 2: 104, 3: 681}) # this can be tricky!\n",
+    "my_new_series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_new_series.iloc[1] # by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_new_series.loc[1] # by index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_new_series[1] # by index!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_new_series[my_new_series > 100] # ... and also boolean indexing!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Feel overwhelmed? Do the required reading."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/pokemon_stats.csv b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/pokemon_stats.csv
new file mode 100644
index 0000000..70a1e77
--- /dev/null
+++ b/s24/AmFam_Ashwin/27_Pandas-1/Lecture Code/pokemon_stats.csv	
@@ -0,0 +1,886 @@
+,Name,Attack,Defense,HP,Region,Sp. Atk,Sp. Def,Speed,Type 1,Type 2
+0,Bulbasaur,49,49,45,Kanto,65,65,45,Grass,Poison
+1,Ivysaur,62,63,60,Kanto,80,80,60,Grass,Poison
+2,Venusaur,82,83,80,Kanto,100,100,80,Grass,Poison
+3,Charmander,52,43,39,Kanto,60,50,65,Fire,None
+4,Charmeleon,64,58,58,Kanto,80,65,80,Fire,None
+5,Charizard,84,78,78,Kanto,109,85,100,Fire,Flying
+6,Squirtle,48,65,44,Kanto,50,64,43,Water,None
+7,Wartortle,63,80,59,Kanto,65,80,58,Water,None
+8,Blastoise,83,100,79,Kanto,85,105,78,Water,None
+9,Caterpie,30,35,45,Kanto,20,20,45,Bug,None
+10,Metapod,20,55,50,Kanto,25,25,30,Bug,None
+11,Butterfree,45,50,60,Kanto,90,80,70,Bug,Flying
+12,Weedle,35,30,40,Kanto,20,20,50,Bug,Poison
+13,Kakuna,25,50,45,Kanto,25,25,35,Bug,Poison
+14,Beedrill,90,40,65,Kanto,45,80,75,Bug,Poison
+15,Pidgey,45,40,40,Kanto,35,35,56,Normal,Flying
+16,Pidgeotto,60,55,63,Kanto,50,50,71,Normal,Flying
+17,Pidgeot,80,75,83,Kanto,70,70,101,Normal,Flying
+18,Rattata,56,35,30,Kanto,25,35,72,Normal,None
+19,Raticate,81,60,55,Kanto,50,70,97,Normal,None
+20,Spearow,60,30,40,Kanto,31,31,70,Normal,Flying
+21,Fearow,90,65,65,Kanto,61,61,100,Normal,Flying
+22,Ekans,60,44,35,Kanto,40,54,55,Poison,None
+23,Arbok,95,69,60,Kanto,65,79,80,Poison,None
+24,Pikachu,55,40,35,Kanto,50,50,90,Electric,None
+25,Raichu,90,55,60,Kanto,90,80,110,Electric,None
+26,Sandshrew,75,85,50,Kanto,20,30,40,Ground,None
+27,Sandslash,100,110,75,Kanto,45,55,65,Ground,None
+28,Nidorina,62,67,70,Kanto,55,55,56,Poison,None
+29,Nidoqueen,92,87,90,Kanto,75,85,76,Poison,Ground
+30,Nidorino,72,57,61,Kanto,55,55,65,Poison,None
+31,Nidoking,102,77,81,Kanto,85,75,85,Poison,Ground
+32,Clefairy,45,48,70,Kanto,60,65,35,Fairy,None
+33,Clefable,70,73,95,Kanto,95,90,60,Fairy,None
+34,Vulpix,41,40,38,Kanto,50,65,65,Fire,None
+35,Ninetales,76,75,73,Kanto,81,100,100,Fire,None
+36,Jigglypuff,45,20,115,Kanto,45,25,20,Normal,Fairy
+37,Wigglytuff,70,45,140,Kanto,85,50,45,Normal,Fairy
+38,Zubat,45,35,40,Kanto,30,40,55,Poison,Flying
+39,Golbat,80,70,75,Kanto,65,75,90,Poison,Flying
+40,Oddish,50,55,45,Kanto,75,65,30,Grass,Poison
+41,Gloom,65,70,60,Kanto,85,75,40,Grass,Poison
+42,Vileplume,80,85,75,Kanto,110,90,50,Grass,Poison
+43,Paras,70,55,35,Kanto,45,55,25,Bug,Grass
+44,Parasect,95,80,60,Kanto,60,80,30,Bug,Grass
+45,Venonat,55,50,60,Kanto,40,55,45,Bug,Poison
+46,Venomoth,65,60,70,Kanto,90,75,90,Bug,Poison
+47,Diglett,55,25,10,Kanto,35,45,95,Ground,None
+48,Dugtrio,100,50,35,Kanto,50,70,120,Ground,None
+49,Meowth,45,35,40,Kanto,40,40,90,Normal,None
+50,Persian,70,60,65,Kanto,65,65,115,Normal,None
+51,Psyduck,52,48,50,Kanto,65,50,55,Water,None
+52,Golduck,82,78,80,Kanto,95,80,85,Water,None
+53,Mankey,80,35,40,Kanto,35,45,70,Fighting,None
+54,Primeape,105,60,65,Kanto,60,70,95,Fighting,None
+55,Growlithe,70,45,55,Kanto,70,50,60,Fire,None
+56,Arcanine,110,80,90,Kanto,100,80,95,Fire,None
+57,Poliwag,50,40,40,Kanto,40,40,90,Water,None
+58,Poliwhirl,65,65,65,Kanto,50,50,90,Water,None
+59,Poliwrath,95,95,90,Kanto,70,90,70,Water,Fighting
+60,Abra,20,15,25,Kanto,105,55,90,Psychic,None
+61,Kadabra,35,30,40,Kanto,120,70,105,Psychic,None
+62,Alakazam,50,45,55,Kanto,135,95,120,Psychic,None
+63,Machop,80,50,70,Kanto,35,35,35,Fighting,None
+64,Machoke,100,70,80,Kanto,50,60,45,Fighting,None
+65,Machamp,130,80,90,Kanto,65,85,55,Fighting,None
+66,Bellsprout,75,35,50,Kanto,70,30,40,Grass,Poison
+67,Weepinbell,90,50,65,Kanto,85,45,55,Grass,Poison
+68,Victreebel,105,65,80,Kanto,100,70,70,Grass,Poison
+69,Tentacool,40,35,40,Kanto,50,100,70,Water,Poison
+70,Tentacruel,70,65,80,Kanto,80,120,100,Water,Poison
+71,Geodude,80,100,40,Kanto,30,30,20,Rock,Ground
+72,Graveler,95,115,55,Kanto,45,45,35,Rock,Ground
+73,Golem,120,130,80,Kanto,55,65,45,Rock,Ground
+74,Ponyta,85,55,50,Kanto,65,65,90,Fire,None
+75,Rapidash,100,70,65,Kanto,80,80,105,Fire,None
+76,Slowpoke,65,65,90,Kanto,40,40,15,Water,Psychic
+77,Slowbro,75,110,95,Kanto,100,80,30,Water,Psychic
+78,Magnemite,35,70,25,Kanto,95,55,45,Electric,Steel
+79,Magneton,60,95,50,Kanto,120,70,70,Electric,Steel
+80,Doduo,85,45,35,Kanto,35,35,75,Normal,Flying
+81,Dodrio,110,70,60,Kanto,60,60,110,Normal,Flying
+82,Seel,45,55,65,Kanto,45,70,45,Water,None
+83,Dewgong,70,80,90,Kanto,70,95,70,Water,Ice
+84,Grimer,80,50,80,Kanto,40,50,25,Poison,None
+85,Muk,105,75,105,Kanto,65,100,50,Poison,None
+86,Shellder,65,100,30,Kanto,45,25,40,Water,None
+87,Cloyster,95,180,50,Kanto,85,45,70,Water,Ice
+88,Gastly,35,30,30,Kanto,100,35,80,Ghost,Poison
+89,Haunter,50,45,45,Kanto,115,55,95,Ghost,Poison
+90,Gengar,65,60,60,Kanto,130,75,110,Ghost,Poison
+91,Onix,45,160,35,Kanto,30,45,70,Rock,Ground
+92,Drowzee,48,45,60,Kanto,43,90,42,Psychic,None
+93,Hypno,73,70,85,Kanto,73,115,67,Psychic,None
+94,Krabby,105,90,30,Kanto,25,25,50,Water,None
+95,Kingler,130,115,55,Kanto,50,50,75,Water,None
+96,Voltorb,30,50,40,Kanto,55,55,100,Electric,None
+97,Electrode,50,70,60,Kanto,80,80,150,Electric,None
+98,Exeggcute,40,80,60,Kanto,60,45,40,Grass,Psychic
+99,Exeggutor,95,85,95,Kanto,125,75,55,Grass,Psychic
+100,Cubone,50,95,50,Kanto,40,50,35,Ground,None
+101,Marowak,80,110,60,Kanto,50,80,45,Ground,None
+102,Hitmonlee,120,53,50,Kanto,35,110,87,Fighting,None
+103,Hitmonchan,105,79,50,Kanto,35,110,76,Fighting,None
+104,Lickitung,55,75,90,Kanto,60,75,30,Normal,None
+105,Koffing,65,95,40,Kanto,60,45,35,Poison,None
+106,Weezing,90,120,65,Kanto,85,70,60,Poison,None
+107,Rhyhorn,85,95,80,Kanto,30,30,25,Ground,Rock
+108,Rhydon,130,120,105,Kanto,45,45,40,Ground,Rock
+109,Chansey,5,5,250,Kanto,35,105,50,Normal,None
+110,Tangela,55,115,65,Kanto,100,40,60,Grass,None
+111,Kangaskhan,95,80,105,Kanto,40,80,90,Normal,None
+112,Horsea,40,70,30,Kanto,70,25,60,Water,None
+113,Seadra,65,95,55,Kanto,95,45,85,Water,None
+114,Goldeen,67,60,45,Kanto,35,50,63,Water,None
+115,Seaking,92,65,80,Kanto,65,80,68,Water,None
+116,Staryu,45,55,30,Kanto,70,55,85,Water,None
+117,Starmie,75,85,60,Kanto,100,85,115,Water,Psychic
+118,Scyther,110,80,70,Kanto,55,80,105,Bug,Flying
+119,Jynx,50,35,65,Kanto,115,95,95,Ice,Psychic
+120,Electabuzz,83,57,65,Kanto,95,85,105,Electric,None
+121,Magmar,95,57,65,Kanto,100,85,93,Fire,None
+122,Pinsir,125,100,65,Kanto,55,70,85,Bug,None
+123,Tauros,100,95,75,Kanto,40,70,110,Normal,None
+124,Magikarp,10,55,20,Kanto,15,20,80,Water,None
+125,Gyarados,125,79,95,Kanto,60,100,81,Water,Flying
+126,Lapras,85,80,130,Kanto,85,95,60,Water,Ice
+127,Ditto,48,48,48,Kanto,48,48,48,Normal,None
+128,Eevee,55,50,55,Kanto,45,65,55,Normal,None
+129,Vaporeon,65,60,130,Kanto,110,95,65,Water,None
+130,Jolteon,65,60,65,Kanto,110,95,130,Electric,None
+131,Flareon,130,60,65,Kanto,95,110,65,Fire,None
+132,Porygon,60,70,65,Kanto,85,75,40,Normal,None
+133,Omanyte,40,100,35,Kanto,90,55,35,Rock,Water
+134,Omastar,60,125,70,Kanto,115,70,55,Rock,Water
+135,Kabuto,80,90,30,Kanto,55,45,55,Rock,Water
+136,Kabutops,115,105,60,Kanto,65,70,80,Rock,Water
+137,Aerodactyl,105,65,80,Kanto,60,75,130,Rock,Flying
+138,Snorlax,110,65,160,Kanto,65,110,30,Normal,None
+139,Articuno,85,100,90,Kanto,95,125,85,Ice,Flying
+140,Zapdos,90,85,90,Kanto,125,90,100,Electric,Flying
+141,Moltres,100,90,90,Kanto,125,85,90,Fire,Flying
+142,Dratini,64,45,41,Kanto,50,50,50,Dragon,None
+143,Dragonair,84,65,61,Kanto,70,70,70,Dragon,None
+144,Dragonite,134,95,91,Kanto,100,100,80,Dragon,Flying
+145,Mewtwo,110,90,106,Kanto,154,90,130,Psychic,None
+146,Mew,100,100,100,Kanto,100,100,100,Psychic,None
+147,Chikorita,49,65,45,Johto,49,65,45,Grass,None
+148,Bayleef,62,80,60,Johto,63,80,60,Grass,None
+149,Meganium,82,100,80,Johto,83,100,80,Grass,None
+150,Cyndaquil,52,43,39,Johto,60,50,65,Fire,None
+151,Quilava,64,58,58,Johto,80,65,80,Fire,None
+152,Typhlosion,84,78,78,Johto,109,85,100,Fire,None
+153,Totodile,65,64,50,Johto,44,48,43,Water,None
+154,Croconaw,80,80,65,Johto,59,63,58,Water,None
+155,Feraligatr,105,100,85,Johto,79,83,78,Water,None
+156,Sentret,46,34,35,Johto,35,45,20,Normal,None
+157,Furret,76,64,85,Johto,45,55,90,Normal,None
+158,Hoothoot,30,30,60,Johto,36,56,50,Normal,Flying
+159,Noctowl,50,50,100,Johto,86,96,70,Normal,Flying
+160,Ledyba,20,30,40,Johto,40,80,55,Bug,Flying
+161,Ledian,35,50,55,Johto,55,110,85,Bug,Flying
+162,Spinarak,60,40,40,Johto,40,40,30,Bug,Poison
+163,Ariados,90,70,70,Johto,60,70,40,Bug,Poison
+164,Crobat,90,80,85,Johto,70,80,130,Poison,Flying
+165,Chinchou,38,38,75,Johto,56,56,67,Water,Electric
+166,Lanturn,58,58,125,Johto,76,76,67,Water,Electric
+167,Pichu,40,15,20,Johto,35,35,60,Electric,None
+168,Cleffa,25,28,50,Johto,45,55,15,Fairy,None
+169,Igglybuff,30,15,90,Johto,40,20,15,Normal,Fairy
+170,Togepi,20,65,35,Johto,40,65,20,Fairy,None
+171,Togetic,40,85,55,Johto,80,105,40,Fairy,Flying
+172,Natu,50,45,40,Johto,70,45,70,Psychic,Flying
+173,Xatu,75,70,65,Johto,95,70,95,Psychic,Flying
+174,Mareep,40,40,55,Johto,65,45,35,Electric,None
+175,Flaaffy,55,55,70,Johto,80,60,45,Electric,None
+176,Ampharos,75,85,90,Johto,115,90,55,Electric,None
+177,Bellossom,80,95,75,Johto,90,100,50,Grass,None
+178,Marill,20,50,70,Johto,20,50,40,Water,Fairy
+179,Azumarill,50,80,100,Johto,60,80,50,Water,Fairy
+180,Sudowoodo,100,115,70,Johto,30,65,30,Rock,None
+181,Politoed,75,75,90,Johto,90,100,70,Water,None
+182,Hoppip,35,40,35,Johto,35,55,50,Grass,Flying
+183,Skiploom,45,50,55,Johto,45,65,80,Grass,Flying
+184,Jumpluff,55,70,75,Johto,55,95,110,Grass,Flying
+185,Aipom,70,55,55,Johto,40,55,85,Normal,None
+186,Sunkern,30,30,30,Johto,30,30,30,Grass,None
+187,Sunflora,75,55,75,Johto,105,85,30,Grass,None
+188,Yanma,65,45,65,Johto,75,45,95,Bug,Flying
+189,Wooper,45,45,55,Johto,25,25,15,Water,Ground
+190,Quagsire,85,85,95,Johto,65,65,35,Water,Ground
+191,Espeon,65,60,65,Johto,130,95,110,Psychic,None
+192,Umbreon,65,110,95,Johto,60,130,65,Dark,None
+193,Murkrow,85,42,60,Johto,85,42,91,Dark,Flying
+194,Slowking,75,80,95,Johto,100,110,30,Water,Psychic
+195,Misdreavus,60,60,60,Johto,85,85,85,Ghost,None
+196,Unown,72,48,48,Johto,72,48,48,Psychic,None
+197,Wobbuffet,33,58,190,Johto,33,58,33,Psychic,None
+198,Girafarig,80,65,70,Johto,90,65,85,Normal,Psychic
+199,Pineco,65,90,50,Johto,35,35,15,Bug,None
+200,Forretress,90,140,75,Johto,60,60,40,Bug,Steel
+201,Dunsparce,70,70,100,Johto,65,65,45,Normal,None
+202,Gligar,75,105,65,Johto,35,65,85,Ground,Flying
+203,Steelix,85,200,75,Johto,55,65,30,Steel,Ground
+204,Snubbull,80,50,60,Johto,40,40,30,Fairy,None
+205,Granbull,120,75,90,Johto,60,60,45,Fairy,None
+206,Qwilfish,95,85,65,Johto,55,55,85,Water,Poison
+207,Scizor,130,100,70,Johto,55,80,65,Bug,Steel
+208,Shuckle,10,230,20,Johto,10,230,5,Bug,Rock
+209,Heracross,125,75,80,Johto,40,95,85,Bug,Fighting
+210,Sneasel,95,55,55,Johto,35,75,115,Dark,Ice
+211,Teddiursa,80,50,60,Johto,50,50,40,Normal,None
+212,Ursaring,130,75,90,Johto,75,75,55,Normal,None
+213,Slugma,40,40,40,Johto,70,40,20,Fire,None
+214,Magcargo,50,120,60,Johto,90,80,30,Fire,Rock
+215,Swinub,50,40,50,Johto,30,30,50,Ice,Ground
+216,Piloswine,100,80,100,Johto,60,60,50,Ice,Ground
+217,Corsola,55,95,65,Johto,65,95,35,Water,Rock
+218,Remoraid,65,35,35,Johto,65,35,65,Water,None
+219,Octillery,105,75,75,Johto,105,75,45,Water,None
+220,Delibird,55,45,45,Johto,65,45,75,Ice,Flying
+221,Mantine,40,70,85,Johto,80,140,70,Water,Flying
+222,Skarmory,80,140,65,Johto,40,70,70,Steel,Flying
+223,Houndour,60,30,45,Johto,80,50,65,Dark,Fire
+224,Houndoom,90,50,75,Johto,110,80,95,Dark,Fire
+225,Kingdra,95,95,75,Johto,95,95,85,Water,Dragon
+226,Phanpy,60,60,90,Johto,40,40,40,Ground,None
+227,Donphan,120,120,90,Johto,60,60,50,Ground,None
+228,Porygon2,80,90,85,Johto,105,95,60,Normal,None
+229,Stantler,95,62,73,Johto,85,65,85,Normal,None
+230,Smeargle,20,35,55,Johto,20,45,75,Normal,None
+231,Tyrogue,35,35,35,Johto,35,35,35,Fighting,None
+232,Hitmontop,95,95,50,Johto,35,110,70,Fighting,None
+233,Smoochum,30,15,45,Johto,85,65,65,Ice,Psychic
+234,Elekid,63,37,45,Johto,65,55,95,Electric,None
+235,Magby,75,37,45,Johto,70,55,83,Fire,None
+236,Miltank,80,105,95,Johto,40,70,100,Normal,None
+237,Blissey,10,10,255,Johto,75,135,55,Normal,None
+238,Raikou,85,75,90,Johto,115,100,115,Electric,None
+239,Entei,115,85,115,Johto,90,75,100,Fire,None
+240,Suicune,75,115,100,Johto,90,115,85,Water,None
+241,Larvitar,64,50,50,Johto,45,50,41,Rock,Ground
+242,Pupitar,84,70,70,Johto,65,70,51,Rock,Ground
+243,Tyranitar,134,110,100,Johto,95,100,61,Rock,Dark
+244,Lugia,90,130,106,Johto,90,154,110,Psychic,Flying
+245,Ho-oh,130,90,106,Johto,110,154,90,Fire,Flying
+246,Celebi,100,100,100,Johto,100,100,100,Psychic,Grass
+247,Treecko,45,35,40,Hoenn,65,55,70,Grass,None
+248,Grovyle,65,45,50,Hoenn,85,65,95,Grass,None
+249,Sceptile,85,65,70,Hoenn,105,85,120,Grass,None
+250,Torchic,60,40,45,Hoenn,70,50,45,Fire,None
+251,Combusken,85,60,60,Hoenn,85,60,55,Fire,Fighting
+252,Blaziken,120,70,80,Hoenn,110,70,80,Fire,Fighting
+253,Mudkip,70,50,50,Hoenn,50,50,40,Water,None
+254,Marshtomp,85,70,70,Hoenn,60,70,50,Water,Ground
+255,Swampert,110,90,100,Hoenn,85,90,60,Water,Ground
+256,Poochyena,55,35,35,Hoenn,30,30,35,Dark,None
+257,Mightyena,90,70,70,Hoenn,60,60,70,Dark,None
+258,Zigzagoon,30,41,38,Hoenn,30,41,60,Normal,None
+259,Linoone,70,61,78,Hoenn,50,61,100,Normal,None
+260,Wurmple,45,35,45,Hoenn,20,30,20,Bug,None
+261,Silcoon,35,55,50,Hoenn,25,25,15,Bug,None
+262,Beautifly,70,50,60,Hoenn,100,50,65,Bug,Flying
+263,Cascoon,35,55,50,Hoenn,25,25,15,Bug,None
+264,Dustox,50,70,60,Hoenn,50,90,65,Bug,Poison
+265,Lotad,30,30,40,Hoenn,40,50,30,Water,Grass
+266,Lombre,50,50,60,Hoenn,60,70,50,Water,Grass
+267,Ludicolo,70,70,80,Hoenn,90,100,70,Water,Grass
+268,Seedot,40,50,40,Hoenn,30,30,30,Grass,None
+269,Nuzleaf,70,40,70,Hoenn,60,40,60,Grass,Dark
+270,Shiftry,100,60,90,Hoenn,90,60,80,Grass,Dark
+271,Taillow,55,30,40,Hoenn,30,30,85,Normal,Flying
+272,Swellow,85,60,60,Hoenn,75,50,125,Normal,Flying
+273,Wingull,30,30,40,Hoenn,55,30,85,Water,Flying
+274,Pelipper,50,100,60,Hoenn,95,70,65,Water,Flying
+275,Ralts,25,25,28,Hoenn,45,35,40,Psychic,Fairy
+276,Kirlia,35,35,38,Hoenn,65,55,50,Psychic,Fairy
+277,Gardevoir,65,65,68,Hoenn,125,115,80,Psychic,Fairy
+278,Surskit,30,32,40,Hoenn,50,52,65,Bug,Water
+279,Masquerain,60,62,70,Hoenn,100,82,80,Bug,Flying
+280,Shroomish,40,60,60,Hoenn,40,60,35,Grass,None
+281,Breloom,130,80,60,Hoenn,60,60,70,Grass,Fighting
+282,Slakoth,60,60,60,Hoenn,35,35,30,Normal,None
+283,Vigoroth,80,80,80,Hoenn,55,55,90,Normal,None
+284,Slaking,160,100,150,Hoenn,95,65,100,Normal,None
+285,Nincada,45,90,31,Hoenn,30,30,40,Bug,Ground
+286,Ninjask,90,45,61,Hoenn,50,50,160,Bug,Flying
+287,Shedinja,90,45,1,Hoenn,30,30,40,Bug,Ghost
+288,Whismur,51,23,64,Hoenn,51,23,28,Normal,None
+289,Loudred,71,43,84,Hoenn,71,43,48,Normal,None
+290,Exploud,91,63,104,Hoenn,91,73,68,Normal,None
+291,Makuhita,60,30,72,Hoenn,20,30,25,Fighting,None
+292,Hariyama,120,60,144,Hoenn,40,60,50,Fighting,None
+293,Azurill,20,40,50,Hoenn,20,40,20,Normal,Fairy
+294,Nosepass,45,135,30,Hoenn,45,90,30,Rock,None
+295,Skitty,45,45,50,Hoenn,35,35,50,Normal,None
+296,Delcatty,65,65,70,Hoenn,55,55,90,Normal,None
+297,Sableye,75,75,50,Hoenn,65,65,50,Dark,Ghost
+298,Mawile,85,85,50,Hoenn,55,55,50,Steel,Fairy
+299,Aron,70,100,50,Hoenn,40,40,30,Steel,Rock
+300,Lairon,90,140,60,Hoenn,50,50,40,Steel,Rock
+301,Aggron,110,180,70,Hoenn,60,60,50,Steel,Rock
+302,Meditite,40,55,30,Hoenn,40,55,60,Fighting,Psychic
+303,Medicham,60,75,60,Hoenn,60,75,80,Fighting,Psychic
+304,Electrike,45,40,40,Hoenn,65,40,65,Electric,None
+305,Manectric,75,60,70,Hoenn,105,60,105,Electric,None
+306,Plusle,50,40,60,Hoenn,85,75,95,Electric,None
+307,Minun,40,50,60,Hoenn,75,85,95,Electric,None
+308,Volbeat,73,75,65,Hoenn,47,85,85,Bug,None
+309,Illumise,47,75,65,Hoenn,73,85,85,Bug,None
+310,Roselia,60,45,50,Hoenn,100,80,65,Grass,Poison
+311,Gulpin,43,53,70,Hoenn,43,53,40,Poison,None
+312,Swalot,73,83,100,Hoenn,73,83,55,Poison,None
+313,Carvanha,90,20,45,Hoenn,65,20,65,Water,Dark
+314,Sharpedo,120,40,70,Hoenn,95,40,95,Water,Dark
+315,Wailmer,70,35,130,Hoenn,70,35,60,Water,None
+316,Wailord,90,45,170,Hoenn,90,45,60,Water,None
+317,Numel,60,40,60,Hoenn,65,45,35,Fire,Ground
+318,Camerupt,100,70,70,Hoenn,105,75,40,Fire,Ground
+319,Torkoal,85,140,70,Hoenn,85,70,20,Fire,None
+320,Spoink,25,35,60,Hoenn,70,80,60,Psychic,None
+321,Grumpig,45,65,80,Hoenn,90,110,80,Psychic,None
+322,Spinda,60,60,60,Hoenn,60,60,60,Normal,None
+323,Trapinch,100,45,45,Hoenn,45,45,10,Ground,None
+324,Vibrava,70,50,50,Hoenn,50,50,70,Ground,Dragon
+325,Flygon,100,80,80,Hoenn,80,80,100,Ground,Dragon
+326,Cacnea,85,40,50,Hoenn,85,40,35,Grass,None
+327,Cacturne,115,60,70,Hoenn,115,60,55,Grass,Dark
+328,Swablu,40,60,45,Hoenn,40,75,50,Normal,Flying
+329,Altaria,70,90,75,Hoenn,70,105,80,Dragon,Flying
+330,Zangoose,115,60,73,Hoenn,60,60,90,Normal,None
+331,Seviper,100,60,73,Hoenn,100,60,65,Poison,None
+332,Lunatone,55,65,90,Hoenn,95,85,70,Rock,Psychic
+333,Solrock,95,85,90,Hoenn,55,65,70,Rock,Psychic
+334,Barboach,48,43,50,Hoenn,46,41,60,Water,Ground
+335,Whiscash,78,73,110,Hoenn,76,71,60,Water,Ground
+336,Corphish,80,65,43,Hoenn,50,35,35,Water,None
+337,Crawdaunt,120,85,63,Hoenn,90,55,55,Water,Dark
+338,Baltoy,40,55,40,Hoenn,40,70,55,Ground,Psychic
+339,Claydol,70,105,60,Hoenn,70,120,75,Ground,Psychic
+340,Lileep,41,77,66,Hoenn,61,87,23,Rock,Grass
+341,Cradily,81,97,86,Hoenn,81,107,43,Rock,Grass
+342,Anorith,95,50,45,Hoenn,40,50,75,Rock,Bug
+343,Armaldo,125,100,75,Hoenn,70,80,45,Rock,Bug
+344,Feebas,15,20,20,Hoenn,10,55,80,Water,None
+345,Milotic,60,79,95,Hoenn,100,125,81,Water,None
+346,Castform,70,70,70,Hoenn,70,70,70,Normal,None
+347,Kecleon,90,70,60,Hoenn,60,120,40,Normal,None
+348,Shuppet,75,35,44,Hoenn,63,33,45,Ghost,None
+349,Banette,115,65,64,Hoenn,83,63,65,Ghost,None
+350,Duskull,40,90,20,Hoenn,30,90,25,Ghost,None
+351,Dusclops,70,130,40,Hoenn,60,130,25,Ghost,None
+352,Tropius,68,83,99,Hoenn,72,87,51,Grass,Flying
+353,Chimecho,50,80,75,Hoenn,95,90,65,Psychic,None
+354,Absol,130,60,65,Hoenn,75,60,75,Dark,None
+355,Wynaut,23,48,95,Hoenn,23,48,23,Psychic,None
+356,Snorunt,50,50,50,Hoenn,50,50,50,Ice,None
+357,Glalie,80,80,80,Hoenn,80,80,80,Ice,None
+358,Spheal,40,50,70,Hoenn,55,50,25,Ice,Water
+359,Sealeo,60,70,90,Hoenn,75,70,45,Ice,Water
+360,Walrein,80,90,110,Hoenn,95,90,65,Ice,Water
+361,Clamperl,64,85,35,Hoenn,74,55,32,Water,None
+362,Huntail,104,105,55,Hoenn,94,75,52,Water,None
+363,Gorebyss,84,105,55,Hoenn,114,75,52,Water,None
+364,Relicanth,90,130,100,Hoenn,45,65,55,Water,Rock
+365,Luvdisc,30,55,43,Hoenn,40,65,97,Water,None
+366,Bagon,75,60,45,Hoenn,40,30,50,Dragon,None
+367,Shelgon,95,100,65,Hoenn,60,50,50,Dragon,None
+368,Salamence,135,80,95,Hoenn,110,80,100,Dragon,Flying
+369,Beldum,55,80,40,Hoenn,35,60,30,Steel,Psychic
+370,Metang,75,100,60,Hoenn,55,80,50,Steel,Psychic
+371,Metagross,135,130,80,Hoenn,95,90,70,Steel,Psychic
+372,Regirock,100,200,80,Hoenn,50,100,50,Rock,None
+373,Regice,50,100,80,Hoenn,100,200,50,Ice,None
+374,Registeel,75,150,80,Hoenn,75,150,50,Steel,None
+375,Latias,80,90,80,Hoenn,110,130,110,Dragon,Psychic
+376,Latios,90,80,80,Hoenn,130,110,110,Dragon,Psychic
+377,Kyogre,100,90,100,Hoenn,150,140,90,Water,None
+378,Groudon,150,140,100,Hoenn,100,90,90,Ground,None
+379,Rayquaza,150,90,105,Hoenn,150,90,95,Dragon,Flying
+380,Jirachi,100,100,100,Hoenn,100,100,100,Steel,Psychic
+381,Deoxys,150,50,50,Hoenn,150,50,150,Psychic,None
+382,Turtwig,68,64,55,Sinnoh,45,55,31,Grass,None
+383,Grotle,89,85,75,Sinnoh,55,65,36,Grass,None
+384,Torterra,109,105,95,Sinnoh,75,85,56,Grass,Ground
+385,Chimchar,58,44,44,Sinnoh,58,44,61,Fire,None
+386,Monferno,78,52,64,Sinnoh,78,52,81,Fire,Fighting
+387,Infernape,104,71,76,Sinnoh,104,71,108,Fire,Fighting
+388,Piplup,51,53,53,Sinnoh,61,56,40,Water,None
+389,Prinplup,66,68,64,Sinnoh,81,76,50,Water,None
+390,Empoleon,86,88,84,Sinnoh,111,101,60,Water,Steel
+391,Starly,55,30,40,Sinnoh,30,30,60,Normal,Flying
+392,Staravia,75,50,55,Sinnoh,40,40,80,Normal,Flying
+393,Staraptor,120,70,85,Sinnoh,50,60,100,Normal,Flying
+394,Bidoof,45,40,59,Sinnoh,35,40,31,Normal,None
+395,Bibarel,85,60,79,Sinnoh,55,60,71,Normal,Water
+396,Kricketot,25,41,37,Sinnoh,25,41,25,Bug,None
+397,Kricketune,85,51,77,Sinnoh,55,51,65,Bug,None
+398,Shinx,65,34,45,Sinnoh,40,34,45,Electric,None
+399,Luxio,85,49,60,Sinnoh,60,49,60,Electric,None
+400,Luxray,120,79,80,Sinnoh,95,79,70,Electric,None
+401,Budew,30,35,40,Sinnoh,50,70,55,Grass,Poison
+402,Roserade,70,65,60,Sinnoh,125,105,90,Grass,Poison
+403,Cranidos,125,40,67,Sinnoh,30,30,58,Rock,None
+404,Rampardos,165,60,97,Sinnoh,65,50,58,Rock,None
+405,Shieldon,42,118,30,Sinnoh,42,88,30,Rock,Steel
+406,Bastiodon,52,168,60,Sinnoh,47,138,30,Rock,Steel
+407,Burmy,29,45,40,Sinnoh,29,45,36,Bug,None
+408,Wormadam,59,85,60,Sinnoh,79,105,36,Bug,Grass
+409,Mothim,94,50,70,Sinnoh,94,50,66,Bug,Flying
+410,Combee,30,42,30,Sinnoh,30,42,70,Bug,Flying
+411,Vespiquen,80,102,70,Sinnoh,80,102,40,Bug,Flying
+412,Pachirisu,45,70,60,Sinnoh,45,90,95,Electric,None
+413,Buizel,65,35,55,Sinnoh,60,30,85,Water,None
+414,Floatzel,105,55,85,Sinnoh,85,50,115,Water,None
+415,Cherubi,35,45,45,Sinnoh,62,53,35,Grass,None
+416,Cherrim,60,70,70,Sinnoh,87,78,85,Grass,None
+417,Shellos,48,48,76,Sinnoh,57,62,34,Water,None
+418,Gastrodon,83,68,111,Sinnoh,92,82,39,Water,Ground
+419,Ambipom,100,66,75,Sinnoh,60,66,115,Normal,None
+420,Drifloon,50,34,90,Sinnoh,60,44,70,Ghost,Flying
+421,Drifblim,80,44,150,Sinnoh,90,54,80,Ghost,Flying
+422,Buneary,66,44,55,Sinnoh,44,56,85,Normal,None
+423,Lopunny,76,84,65,Sinnoh,54,96,105,Normal,None
+424,Mismagius,60,60,60,Sinnoh,105,105,105,Ghost,None
+425,Honchkrow,125,52,100,Sinnoh,105,52,71,Dark,Flying
+426,Glameow,55,42,49,Sinnoh,42,37,85,Normal,None
+427,Purugly,82,64,71,Sinnoh,64,59,112,Normal,None
+428,Chingling,30,50,45,Sinnoh,65,50,45,Psychic,None
+429,Stunky,63,47,63,Sinnoh,41,41,74,Poison,Dark
+430,Skuntank,93,67,103,Sinnoh,71,61,84,Poison,Dark
+431,Bronzor,24,86,57,Sinnoh,24,86,23,Steel,Psychic
+432,Bronzong,89,116,67,Sinnoh,79,116,33,Steel,Psychic
+433,Bonsly,80,95,50,Sinnoh,10,45,10,Rock,None
+434,Happiny,5,5,100,Sinnoh,15,65,30,Normal,None
+435,Chatot,65,45,76,Sinnoh,92,42,91,Normal,Flying
+436,Spiritomb,92,108,50,Sinnoh,92,108,35,Ghost,Dark
+437,Gible,70,45,58,Sinnoh,40,45,42,Dragon,Ground
+438,Gabite,90,65,68,Sinnoh,50,55,82,Dragon,Ground
+439,Garchomp,130,95,108,Sinnoh,80,85,102,Dragon,Ground
+440,Munchlax,85,40,135,Sinnoh,40,85,5,Normal,None
+441,Riolu,70,40,40,Sinnoh,35,40,60,Fighting,None
+442,Lucario,110,70,70,Sinnoh,115,70,90,Fighting,Steel
+443,Hippopotas,72,78,68,Sinnoh,38,42,32,Ground,None
+444,Hippowdon,112,118,108,Sinnoh,68,72,47,Ground,None
+445,Skorupi,50,90,40,Sinnoh,30,55,65,Poison,Bug
+446,Drapion,90,110,70,Sinnoh,60,75,95,Poison,Dark
+447,Croagunk,61,40,48,Sinnoh,61,40,50,Poison,Fighting
+448,Toxicroak,106,65,83,Sinnoh,86,65,85,Poison,Fighting
+449,Carnivine,100,72,74,Sinnoh,90,72,46,Grass,None
+450,Finneon,49,56,49,Sinnoh,49,61,66,Water,None
+451,Lumineon,69,76,69,Sinnoh,69,86,91,Water,None
+452,Mantyke,20,50,45,Sinnoh,60,120,50,Water,Flying
+453,Snover,62,50,60,Sinnoh,62,60,40,Grass,Ice
+454,Abomasnow,92,75,90,Sinnoh,92,85,60,Grass,Ice
+455,Weavile,120,65,70,Sinnoh,45,85,125,Dark,Ice
+456,Magnezone,70,115,70,Sinnoh,130,90,60,Electric,Steel
+457,Lickilicky,85,95,110,Sinnoh,80,95,50,Normal,None
+458,Rhyperior,140,130,115,Sinnoh,55,55,40,Ground,Rock
+459,Tangrowth,100,125,100,Sinnoh,110,50,50,Grass,None
+460,Electivire,123,67,75,Sinnoh,95,85,95,Electric,None
+461,Magmortar,95,67,75,Sinnoh,125,95,83,Fire,None
+462,Togekiss,50,95,85,Sinnoh,120,115,80,Fairy,Flying
+463,Yanmega,76,86,86,Sinnoh,116,56,95,Bug,Flying
+464,Leafeon,110,130,65,Sinnoh,60,65,95,Grass,None
+465,Glaceon,60,110,65,Sinnoh,130,95,65,Ice,None
+466,Gliscor,95,125,75,Sinnoh,45,75,95,Ground,Flying
+467,Mamoswine,130,80,110,Sinnoh,70,60,80,Ice,Ground
+468,Porygon-Z,80,70,85,Sinnoh,135,75,90,Normal,None
+469,Gallade,125,65,68,Sinnoh,65,115,80,Psychic,Fighting
+470,Probopass,55,145,60,Sinnoh,75,150,40,Rock,Steel
+471,Dusknoir,100,135,45,Sinnoh,65,135,45,Ghost,None
+472,Froslass,80,70,70,Sinnoh,80,70,110,Ice,Ghost
+473,Rotom,50,77,50,Sinnoh,95,77,91,Electric,Ghost
+474,Uxie,75,130,75,Sinnoh,75,130,95,Psychic,None
+475,Mesprit,105,105,80,Sinnoh,105,105,80,Psychic,None
+476,Azelf,125,70,75,Sinnoh,125,70,115,Psychic,None
+477,Dialga,120,120,100,Sinnoh,150,100,90,Steel,Dragon
+478,Palkia,120,100,90,Sinnoh,150,120,100,Water,Dragon
+479,Heatran,90,106,91,Sinnoh,130,106,77,Fire,Steel
+480,Regigigas,160,110,110,Sinnoh,80,110,100,Normal,None
+481,Giratina,100,120,150,Sinnoh,100,120,90,Ghost,Dragon
+482,Cresselia,70,120,120,Sinnoh,75,130,85,Psychic,None
+483,Phione,80,80,80,Sinnoh,80,80,80,Water,None
+484,Manaphy,100,100,100,Sinnoh,100,100,100,Water,None
+485,Darkrai,90,90,70,Sinnoh,135,90,125,Dark,None
+486,Shaymin,100,100,100,Sinnoh,100,100,100,Grass,None
+487,Arceus,120,120,120,Sinnoh,120,120,120,Normal,None
+488,Victini,100,100,100,Unova,100,100,100,Psychic,Fire
+489,Snivy,45,55,45,Unova,45,55,63,Grass,None
+490,Servine,60,75,60,Unova,60,75,83,Grass,None
+491,Serperior,75,95,75,Unova,75,95,113,Grass,None
+492,Tepig,63,45,65,Unova,45,45,45,Fire,None
+493,Pignite,93,55,90,Unova,70,55,55,Fire,Fighting
+494,Emboar,123,65,110,Unova,100,65,65,Fire,Fighting
+495,Oshawott,55,45,55,Unova,63,45,45,Water,None
+496,Dewott,75,60,75,Unova,83,60,60,Water,None
+497,Samurott,100,85,95,Unova,108,70,70,Water,None
+498,Patrat,55,39,45,Unova,35,39,42,Normal,None
+499,Watchog,85,69,60,Unova,60,69,77,Normal,None
+500,Lillipup,60,45,45,Unova,25,45,55,Normal,None
+501,Herdier,80,65,65,Unova,35,65,60,Normal,None
+502,Stoutland,110,90,85,Unova,45,90,80,Normal,None
+503,Purrloin,50,37,41,Unova,50,37,66,Dark,None
+504,Liepard,88,50,64,Unova,88,50,106,Dark,None
+505,Pansage,53,48,50,Unova,53,48,64,Grass,None
+506,Simisage,98,63,75,Unova,98,63,101,Grass,None
+507,Pansear,53,48,50,Unova,53,48,64,Fire,None
+508,Simisear,98,63,75,Unova,98,63,101,Fire,None
+509,Panpour,53,48,50,Unova,53,48,64,Water,None
+510,Simipour,98,63,75,Unova,98,63,101,Water,None
+511,Munna,25,45,76,Unova,67,55,24,Psychic,None
+512,Musharna,55,85,116,Unova,107,95,29,Psychic,None
+513,Pidove,55,50,50,Unova,36,30,43,Normal,Flying
+514,Tranquill,77,62,62,Unova,50,42,65,Normal,Flying
+515,Unfezant,115,80,80,Unova,65,55,93,Normal,Flying
+516,Blitzle,60,32,45,Unova,50,32,76,Electric,None
+517,Zebstrika,100,63,75,Unova,80,63,116,Electric,None
+518,Roggenrola,75,85,55,Unova,25,25,15,Rock,None
+519,Boldore,105,105,70,Unova,50,40,20,Rock,None
+520,Gigalith,135,130,85,Unova,60,80,25,Rock,None
+521,Woobat,45,43,65,Unova,55,43,72,Psychic,Flying
+522,Swoobat,57,55,67,Unova,77,55,114,Psychic,Flying
+523,Drilbur,85,40,60,Unova,30,45,68,Ground,None
+524,Excadrill,135,60,110,Unova,50,65,88,Ground,Steel
+525,Audino,60,86,103,Unova,60,86,50,Normal,None
+526,Timburr,80,55,75,Unova,25,35,35,Fighting,None
+527,Gurdurr,105,85,85,Unova,40,50,40,Fighting,None
+528,Conkeldurr,140,95,105,Unova,55,65,45,Fighting,None
+529,Tympole,50,40,50,Unova,50,40,64,Water,None
+530,Palpitoad,65,55,75,Unova,65,55,69,Water,Ground
+531,Seismitoad,95,75,105,Unova,85,75,74,Water,Ground
+532,Throh,100,85,120,Unova,30,85,45,Fighting,None
+533,Sawk,125,75,75,Unova,30,75,85,Fighting,None
+534,Sewaddle,53,70,45,Unova,40,60,42,Bug,Grass
+535,Swadloon,63,90,55,Unova,50,80,42,Bug,Grass
+536,Leavanny,103,80,75,Unova,70,80,92,Bug,Grass
+537,Venipede,45,59,30,Unova,30,39,57,Bug,Poison
+538,Whirlipede,55,99,40,Unova,40,79,47,Bug,Poison
+539,Scolipede,100,89,60,Unova,55,69,112,Bug,Poison
+540,Cottonee,27,60,40,Unova,37,50,66,Grass,Fairy
+541,Whimsicott,67,85,60,Unova,77,75,116,Grass,Fairy
+542,Petilil,35,50,45,Unova,70,50,30,Grass,None
+543,Lilligant,60,75,70,Unova,110,75,90,Grass,None
+544,Basculin,92,65,70,Unova,80,55,98,Water,None
+545,Sandile,72,35,50,Unova,35,35,65,Ground,Dark
+546,Krokorok,82,45,60,Unova,45,45,74,Ground,Dark
+547,Krookodile,117,80,95,Unova,65,70,92,Ground,Dark
+548,Darumaka,90,45,70,Unova,15,45,50,Fire,None
+549,Darmanitan,140,55,105,Unova,30,55,95,Fire,None
+550,Maractus,86,67,75,Unova,106,67,60,Grass,None
+551,Dwebble,65,85,50,Unova,35,35,55,Bug,Rock
+552,Crustle,105,125,70,Unova,65,75,45,Bug,Rock
+553,Scraggy,75,70,50,Unova,35,70,48,Dark,Fighting
+554,Scrafty,90,115,65,Unova,45,115,58,Dark,Fighting
+555,Sigilyph,58,80,72,Unova,103,80,97,Psychic,Flying
+556,Yamask,30,85,38,Unova,55,65,30,Ghost,None
+557,Cofagrigus,50,145,58,Unova,95,105,30,Ghost,None
+558,Tirtouga,78,103,54,Unova,53,45,22,Water,Rock
+559,Carracosta,108,133,74,Unova,83,65,32,Water,Rock
+560,Archen,112,45,55,Unova,74,45,70,Rock,Flying
+561,Archeops,140,65,75,Unova,112,65,110,Rock,Flying
+562,Trubbish,50,62,50,Unova,40,62,65,Poison,None
+563,Garbodor,95,82,80,Unova,60,82,75,Poison,None
+564,Zorua,65,40,40,Unova,80,40,65,Dark,None
+565,Zoroark,105,60,60,Unova,120,60,105,Dark,None
+566,Minccino,50,40,55,Unova,40,40,75,Normal,None
+567,Cinccino,95,60,75,Unova,65,60,115,Normal,None
+568,Gothita,30,50,45,Unova,55,65,45,Psychic,None
+569,Gothorita,45,70,60,Unova,75,85,55,Psychic,None
+570,Gothitelle,55,95,70,Unova,95,110,65,Psychic,None
+571,Solosis,30,40,45,Unova,105,50,20,Psychic,None
+572,Duosion,40,50,65,Unova,125,60,30,Psychic,None
+573,Reuniclus,65,75,110,Unova,125,85,30,Psychic,None
+574,Ducklett,44,50,62,Unova,44,50,55,Water,Flying
+575,Swanna,87,63,75,Unova,87,63,98,Water,Flying
+576,Vanillite,50,50,36,Unova,65,60,44,Ice,None
+577,Vanillish,65,65,51,Unova,80,75,59,Ice,None
+578,Vanilluxe,95,85,71,Unova,110,95,79,Ice,None
+579,Deerling,60,50,60,Unova,40,50,75,Normal,Grass
+580,Sawsbuck,100,70,80,Unova,60,70,95,Normal,Grass
+581,Emolga,75,60,55,Unova,75,60,103,Electric,Flying
+582,Karrablast,75,45,50,Unova,40,45,60,Bug,None
+583,Escavalier,135,105,70,Unova,60,105,20,Bug,Steel
+584,Foongus,55,45,69,Unova,55,55,15,Grass,Poison
+585,Amoonguss,85,70,114,Unova,85,80,30,Grass,Poison
+586,Frillish,40,50,55,Unova,65,85,40,Water,Ghost
+587,Jellicent,60,70,100,Unova,85,105,60,Water,Ghost
+588,Alomomola,75,80,165,Unova,40,45,65,Water,None
+589,Joltik,47,50,50,Unova,57,50,65,Bug,Electric
+590,Galvantula,77,60,70,Unova,97,60,108,Bug,Electric
+591,Ferroseed,50,91,44,Unova,24,86,10,Grass,Steel
+592,Ferrothorn,94,131,74,Unova,54,116,20,Grass,Steel
+593,Klink,55,70,40,Unova,45,60,30,Steel,None
+594,Klang,80,95,60,Unova,70,85,50,Steel,None
+595,Klinklang,100,115,60,Unova,70,85,90,Steel,None
+596,Tynamo,55,40,35,Unova,45,40,60,Electric,None
+597,Eelektrik,85,70,65,Unova,75,70,40,Electric,None
+598,Eelektross,115,80,85,Unova,105,80,50,Electric,None
+599,Elgyem,55,55,55,Unova,85,55,30,Psychic,None
+600,Beheeyem,75,75,75,Unova,125,95,40,Psychic,None
+601,Litwick,30,55,50,Unova,65,55,20,Ghost,Fire
+602,Lampent,40,60,60,Unova,95,60,55,Ghost,Fire
+603,Chandelure,55,90,60,Unova,145,90,80,Ghost,Fire
+604,Axew,87,60,46,Unova,30,40,57,Dragon,None
+605,Fraxure,117,70,66,Unova,40,50,67,Dragon,None
+606,Haxorus,147,90,76,Unova,60,70,97,Dragon,None
+607,Cubchoo,70,40,55,Unova,60,40,40,Ice,None
+608,Beartic,130,80,95,Unova,70,80,50,Ice,None
+609,Cryogonal,50,50,80,Unova,95,135,105,Ice,None
+610,Shelmet,40,85,50,Unova,40,65,25,Bug,None
+611,Accelgor,70,40,80,Unova,100,60,145,Bug,None
+612,Stunfisk,66,84,109,Unova,81,99,32,Ground,Electric
+613,Mienfoo,85,50,45,Unova,55,50,65,Fighting,None
+614,Mienshao,125,60,65,Unova,95,60,105,Fighting,None
+615,Druddigon,120,90,77,Unova,60,90,48,Dragon,None
+616,Golett,74,50,59,Unova,35,50,35,Ground,Ghost
+617,Golurk,124,80,89,Unova,55,80,55,Ground,Ghost
+618,Pawniard,85,70,45,Unova,40,40,60,Dark,Steel
+619,Bisharp,125,100,65,Unova,60,70,70,Dark,Steel
+620,Bouffalant,110,95,95,Unova,40,95,55,Normal,None
+621,Rufflet,83,50,70,Unova,37,50,60,Normal,Flying
+622,Braviary,123,75,100,Unova,57,75,80,Normal,Flying
+623,Vullaby,55,75,70,Unova,45,65,60,Dark,Flying
+624,Mandibuzz,65,105,110,Unova,55,95,80,Dark,Flying
+625,Heatmor,97,66,85,Unova,105,66,65,Fire,None
+626,Durant,109,112,58,Unova,48,48,109,Bug,Steel
+627,Deino,65,50,52,Unova,45,50,38,Dark,Dragon
+628,Zweilous,85,70,72,Unova,65,70,58,Dark,Dragon
+629,Hydreigon,105,90,92,Unova,125,90,98,Dark,Dragon
+630,Larvesta,85,55,55,Unova,50,55,60,Bug,Fire
+631,Volcarona,60,65,85,Unova,135,105,100,Bug,Fire
+632,Cobalion,90,129,91,Unova,90,72,108,Steel,Fighting
+633,Terrakion,129,90,91,Unova,72,90,108,Rock,Fighting
+634,Virizion,90,72,91,Unova,90,129,108,Grass,Fighting
+635,Tornadus,115,70,79,Unova,125,80,111,Flying,None
+636,Thundurus,115,70,79,Unova,125,80,111,Electric,Flying
+637,Reshiram,120,100,100,Unova,150,120,90,Dragon,Fire
+638,Zekrom,150,120,100,Unova,120,100,90,Dragon,Electric
+639,Landorus,125,90,89,Unova,115,80,101,Ground,Flying
+640,Kyurem,130,90,125,Unova,130,90,95,Dragon,Ice
+641,Keldeo,72,90,91,Unova,129,90,108,Water,Fighting
+642,Meloetta,77,77,100,Unova,128,128,90,Normal,Psychic
+643,Genesect,120,95,71,Unova,120,95,99,Bug,Steel
+644,Chespin,61,65,56,Kalos,48,45,38,Grass,None
+645,Quilladin,78,95,61,Kalos,56,58,57,Grass,None
+646,Chesnaught,107,122,88,Kalos,74,75,64,Grass,Fighting
+647,Fennekin,45,40,40,Kalos,62,60,60,Fire,None
+648,Braixen,59,58,59,Kalos,90,70,73,Fire,None
+649,Delphox,69,72,75,Kalos,114,100,104,Fire,Psychic
+650,Froakie,56,40,41,Kalos,62,44,71,Water,None
+651,Frogadier,63,52,54,Kalos,83,56,97,Water,None
+652,Greninja,95,67,72,Kalos,103,71,122,Water,Dark
+653,Bunnelby,36,38,38,Kalos,32,36,57,Normal,None
+654,Diggersby,56,77,85,Kalos,50,77,78,Normal,Ground
+655,Fletchling,50,43,45,Kalos,40,38,62,Normal,Flying
+656,Fletchinder,73,55,62,Kalos,56,52,84,Fire,Flying
+657,Talonflame,81,71,78,Kalos,74,69,126,Fire,Flying
+658,Scatterbug,35,40,38,Kalos,27,25,35,Bug,None
+659,Spewpa,22,60,45,Kalos,27,30,29,Bug,None
+660,Vivillon,52,50,80,Kalos,90,50,89,Bug,Flying
+661,Litleo,50,58,62,Kalos,73,54,72,Fire,Normal
+662,Pyroar,68,72,86,Kalos,109,66,106,Fire,Normal
+663,Floette,45,47,54,Kalos,75,98,52,Fairy,None
+664,Florges,65,68,78,Kalos,112,154,75,Fairy,None
+665,Skiddo,65,48,66,Kalos,62,57,52,Grass,None
+666,Gogoat,100,62,123,Kalos,97,81,68,Grass,None
+667,Pancham,82,62,67,Kalos,46,48,43,Fighting,None
+668,Pangoro,124,78,95,Kalos,69,71,58,Fighting,Dark
+669,Furfrou,80,60,75,Kalos,65,90,102,Normal,None
+670,Espurr,48,54,62,Kalos,63,60,68,Psychic,None
+671,Meowstic,48,76,74,Kalos,83,81,104,Psychic,None
+672,Honedge,80,100,45,Kalos,35,37,28,Steel,Ghost
+673,Doublade,110,150,59,Kalos,45,49,35,Steel,Ghost
+674,Aegislash,50,140,60,Kalos,50,140,60,Steel,Ghost
+675,Spritzee,52,60,78,Kalos,63,65,23,Fairy,None
+676,Aromatisse,72,72,101,Kalos,99,89,29,Fairy,None
+677,Swirlix,48,66,62,Kalos,59,57,49,Fairy,None
+678,Slurpuff,80,86,82,Kalos,85,75,72,Fairy,None
+679,Inkay,54,53,53,Kalos,37,46,45,Dark,Psychic
+680,Malamar,92,88,86,Kalos,68,75,73,Dark,Psychic
+681,Binacle,52,67,42,Kalos,39,56,50,Rock,Water
+682,Barbaracle,105,115,72,Kalos,54,86,68,Rock,Water
+683,Skrelp,60,60,50,Kalos,60,60,30,Poison,Water
+684,Dragalge,75,90,65,Kalos,97,123,44,Poison,Dragon
+685,Clauncher,53,62,50,Kalos,58,63,44,Water,None
+686,Clawitzer,73,88,71,Kalos,120,89,59,Water,None
+687,Helioptile,38,33,44,Kalos,61,43,70,Electric,Normal
+688,Heliolisk,55,52,62,Kalos,109,94,109,Electric,Normal
+689,Tyrunt,89,77,58,Kalos,45,45,48,Rock,Dragon
+690,Tyrantrum,121,119,82,Kalos,69,59,71,Rock,Dragon
+691,Amaura,59,50,77,Kalos,67,63,46,Rock,Ice
+692,Aurorus,77,72,123,Kalos,99,92,58,Rock,Ice
+693,Sylveon,65,65,95,Kalos,110,130,60,Fairy,None
+694,Hawlucha,92,75,78,Kalos,74,63,118,Fighting,Flying
+695,Dedenne,58,57,67,Kalos,81,67,101,Electric,Fairy
+696,Carbink,50,150,50,Kalos,50,150,50,Rock,Fairy
+697,Goomy,50,35,45,Kalos,55,75,40,Dragon,None
+698,Sliggoo,75,53,68,Kalos,83,113,60,Dragon,None
+699,Goodra,100,70,90,Kalos,110,150,80,Dragon,None
+700,Klefki,80,91,57,Kalos,80,87,75,Steel,Fairy
+701,Phantump,70,48,43,Kalos,50,60,38,Ghost,Grass
+702,Trevenant,110,76,85,Kalos,65,82,56,Ghost,Grass
+703,Pumpkaboo,66,70,49,Kalos,44,55,51,Ghost,Grass
+704,Gourgeist,90,122,65,Kalos,58,75,84,Ghost,Grass
+705,Bergmite,69,85,55,Kalos,32,35,28,Ice,None
+706,Avalugg,117,184,95,Kalos,44,46,28,Ice,None
+707,Noibat,30,35,40,Kalos,45,40,55,Flying,Dragon
+708,Noivern,70,80,85,Kalos,97,80,123,Flying,Dragon
+709,Xerneas,131,95,126,Kalos,131,98,99,Fairy,None
+710,Yveltal,131,95,126,Kalos,131,98,99,Dark,Flying
+711,Zygarde,100,121,108,Kalos,81,95,95,Dragon,Ground
+712,Diancie,100,150,50,Kalos,100,150,50,Rock,Fairy
+713,Hoopa,110,60,80,Kalos,150,130,70,Psychic,Ghost
+714,Volcanion,110,120,80,Kalos,130,90,70,Fire,Water
+715,Rowlet,55,55,68,Alola,50,50,42,Grass,Flying
+716,Dartrix,75,75,78,Alola,70,70,52,Grass,Flying
+717,Decidueye,107,75,78,Alola,100,100,70,Grass,Ghost
+718,Litten,65,40,45,Alola,60,40,70,Fire,None
+719,Torracat,85,50,65,Alola,80,50,90,Fire,None
+720,Incineroar,115,90,95,Alola,80,90,60,Fire,Dark
+721,Popplio,54,54,50,Alola,66,56,40,Water,None
+722,Brionne,69,69,60,Alola,91,81,50,Water,None
+723,Primarina,74,74,80,Alola,126,116,60,Water,Fairy
+724,Pikipek,75,30,35,Alola,30,30,65,Normal,Flying
+725,Trumbeak,85,50,55,Alola,40,50,75,Normal,Flying
+726,Toucannon,120,75,80,Alola,75,75,60,Normal,Flying
+727,Yungoos,70,30,48,Alola,30,30,45,Normal,None
+728,Gumshoos,110,60,88,Alola,55,60,45,Normal,None
+729,Grubbin,62,45,47,Alola,55,45,46,Bug,None
+730,Charjabug,82,95,57,Alola,55,75,36,Bug,Electric
+731,Vikavolt,70,90,77,Alola,145,75,43,Bug,Electric
+732,Crabrawler,82,57,47,Alola,42,47,63,Fighting,None
+733,Crabominable,132,77,97,Alola,62,67,43,Fighting,Ice
+734,Oricorio,70,70,75,Alola,98,70,93,Fire,Flying
+735,Cutiefly,45,40,40,Alola,55,40,84,Bug,Fairy
+736,Ribombee,55,60,60,Alola,95,70,124,Bug,Fairy
+737,Rockruff,65,40,45,Alola,30,40,60,Rock,None
+738,Lycanroc,115,65,75,Alola,55,65,112,Rock,None
+739,Wishiwashi,20,20,45,Alola,25,25,40,Water,None
+740,Mareanie,53,62,50,Alola,43,52,45,Poison,Water
+741,Toxapex,63,152,50,Alola,53,142,35,Poison,Water
+742,Mudbray,100,70,70,Alola,45,55,45,Ground,None
+743,Mudsdale,125,100,100,Alola,55,85,35,Ground,None
+744,Dewpider,40,52,38,Alola,40,72,27,Water,Bug
+745,Araquanid,70,92,68,Alola,50,132,42,Water,Bug
+746,Fomantis,55,35,40,Alola,50,35,35,Grass,None
+747,Lurantis,105,90,70,Alola,80,90,45,Grass,None
+748,Morelull,35,55,40,Alola,65,75,15,Grass,Fairy
+749,Shiinotic,45,80,60,Alola,90,100,30,Grass,Fairy
+750,Salandit,44,40,48,Alola,71,40,77,Poison,Fire
+751,Salazzle,64,60,68,Alola,111,60,117,Poison,Fire
+752,Stufful,75,50,70,Alola,45,50,50,Normal,Fighting
+753,Bewear,125,80,120,Alola,55,60,60,Normal,Fighting
+754,Bounsweet,30,38,42,Alola,30,38,32,Grass,None
+755,Steenee,40,48,52,Alola,40,48,62,Grass,None
+756,Tsareena,120,98,72,Alola,50,98,72,Grass,None
+757,Comfey,52,90,51,Alola,82,110,100,Fairy,None
+758,Oranguru,60,80,90,Alola,90,110,60,Normal,Psychic
+759,Passimian,120,90,100,Alola,40,60,80,Fighting,None
+760,Wimpod,35,40,25,Alola,20,30,80,Bug,Water
+761,Golisopod,125,140,75,Alola,60,90,40,Bug,Water
+762,Sandygast,55,80,55,Alola,70,45,15,Ghost,Ground
+763,Palossand,75,110,85,Alola,100,75,35,Ghost,Ground
+764,Pyukumuku,60,130,55,Alola,30,130,5,Water,None
+765,Silvally,95,95,95,Alola,95,95,95,Normal,None
+766,Minior,60,100,60,Alola,60,100,60,Rock,Flying
+767,Komala,115,65,65,Alola,75,95,65,Normal,None
+768,Turtonator,78,135,60,Alola,91,85,36,Fire,Dragon
+769,Togedemaru,98,63,65,Alola,40,73,96,Electric,Steel
+770,Mimikyu,90,80,55,Alola,50,105,96,Ghost,Fairy
+771,Bruxish,105,70,68,Alola,70,70,92,Water,Psychic
+772,Drampa,60,85,78,Alola,135,91,36,Normal,Dragon
+773,Dhelmise,131,100,70,Alola,86,90,40,Ghost,Grass
+774,Jangmo-o,55,65,45,Alola,45,45,45,Dragon,None
+775,Hakamo-o,75,90,55,Alola,65,70,65,Dragon,Fighting
+776,Kommo-o,110,125,75,Alola,100,105,85,Dragon,Fighting
+777,Cosmog,29,31,43,Alola,29,31,37,Psychic,None
+778,Cosmoem,29,131,43,Alola,29,131,37,Psychic,None
+779,Solgaleo,137,107,137,Alola,113,89,97,Psychic,Steel
+780,Lunala,113,89,137,Alola,137,107,97,Psychic,Ghost
+781,Nihilego,53,47,109,Alola,127,131,103,Rock,Poison
+782,Buzzwole,139,139,107,Alola,53,53,79,Bug,Fighting
+783,Pheromosa,137,37,71,Alola,137,37,151,Bug,Fighting
+784,Xurkitree,89,71,83,Alola,173,71,83,Electric,None
+785,Celesteela,101,103,97,Alola,107,101,61,Steel,Flying
+786,Kartana,181,131,59,Alola,59,31,109,Grass,Steel
+787,Guzzlord,101,53,223,Alola,97,53,43,Dark,Dragon
+788,Necrozma,107,101,97,Alola,127,89,79,Psychic,None
+789,Magearna,95,115,80,Alola,130,115,65,Steel,Fairy
+790,Marshadow,125,80,90,Alola,90,90,125,Fighting,Ghost
+791,Poipole,73,67,67,Alola,73,67,73,Poison,None
+792,Naganadel,73,73,73,Alola,127,73,121,Poison,Dragon
+793,Stakataka,131,211,61,Alola,53,101,13,Rock,Steel
+794,Blacephalon,127,53,53,Alola,151,79,107,Fire,Ghost
+795,Zeraora,112,75,88,Alola,102,80,143,Electric,None
+796,Meltan,65,65,46,Alola,55,35,34,Steel,None
+797,Melmetal,143,143,135,Alola,80,65,34,Steel,None
+798,Grookey,65,50,50,Galar,40,40,65,Grass,None
+799,Thwackey,85,70,70,Galar,55,60,80,Grass,None
+800,Rillaboom,125,90,100,Galar,60,70,85,Grass,None
+801,Scorbunny,71,40,50,Galar,40,40,69,Fire,None
+802,Raboot,86,60,65,Galar,55,60,94,Fire,None
+803,Cinderace,116,75,80,Galar,65,75,119,Fire,None
+804,Sobble,40,40,50,Galar,70,40,70,Water,None
+805,Drizzile,60,55,65,Galar,95,55,90,Water,None
+806,Inteleon,85,65,70,Galar,125,65,120,Water,None
+807,Skwovet,55,55,70,Galar,35,35,25,Normal,None
+808,Greedent,95,95,120,Galar,55,75,20,Normal,None
+809,Rookidee,47,35,38,Galar,33,35,57,Flying,None
+810,Corvisquire,67,55,68,Galar,43,55,77,Flying,None
+811,Corviknight,87,105,98,Galar,53,85,67,Flying,Steel
+812,Blipbug,20,20,25,Galar,25,45,45,Bug,None
+813,Dottler,35,80,50,Galar,50,90,30,Bug,Psychic
+814,Orbeetle,45,110,60,Galar,80,120,90,Bug,Psychic
+815,Nickit,28,28,40,Galar,47,52,50,Dark,None
+816,Thievul,58,58,70,Galar,87,92,90,Dark,None
+817,Gossifleur,40,60,40,Galar,40,60,10,Grass,None
+818,Eldegoss,50,90,60,Galar,80,120,60,Grass,None
+819,Wooloo,40,55,42,Galar,40,45,48,Normal,None
+820,Dubwool,80,100,72,Galar,60,90,88,Normal,None
+821,Chewtle,64,50,50,Galar,38,38,44,Water,None
+822,Drednaw,115,90,90,Galar,48,68,74,Water,Rock
+823,Yamper,45,50,59,Galar,40,50,26,Electric,None
+824,Boltund,90,60,69,Galar,90,60,121,Electric,None
+825,Rolycoly,40,50,30,Galar,40,50,30,Rock,None
+826,Carkol,60,90,80,Galar,60,70,50,Rock,Fire
+827,Coalossal,80,120,110,Galar,80,90,30,Rock,Fire
+828,Applin,40,80,40,Galar,40,40,20,Grass,Dragon
+829,Flapple,110,80,70,Galar,95,60,70,Grass,Dragon
+830,Appletun,85,80,110,Galar,100,80,30,Grass,Dragon
+831,Silicobra,57,75,52,Galar,35,50,46,Ground,None
+832,Sandaconda,107,125,72,Galar,65,70,71,Ground,None
+833,Cramorant,85,55,70,Galar,85,95,85,Flying,Water
+834,Arrokuda,63,40,41,Galar,40,30,66,Water,None
+835,Barraskewda,123,60,61,Galar,60,50,136,Water,None
+836,Toxel,38,35,40,Galar,54,35,40,Electric,Poison
+837,Toxtricity,98,70,75,Galar,114,70,75,Electric,Poison
+838,Sizzlipede,65,45,50,Galar,50,50,45,Fire,Bug
+839,Centiskorch,115,65,100,Galar,90,90,65,Fire,Bug
+840,Clobbopus,68,60,50,Galar,50,50,32,Fighting,None
+841,Grapploct,118,90,80,Galar,70,80,42,Fighting,None
+842,Sinistea,45,45,40,Galar,74,54,50,Ghost,None
+843,Polteageist,65,65,60,Galar,134,114,70,Ghost,None
+844,Hatenna,30,45,42,Galar,56,53,39,Psychic,None
+845,Hattrem,40,65,57,Galar,86,73,49,Psychic,None
+846,Hatterene,90,95,57,Galar,136,103,29,Psychic,Fairy
+847,Impidimp,45,30,45,Galar,55,40,50,Dark,Fairy
+848,Morgrem,60,45,65,Galar,75,55,70,Dark,Fairy
+849,Grimmsnarl,120,65,95,Galar,95,75,60,Dark,Fairy
+850,Obstagoon,90,101,93,Galar,60,81,95,Dark,Normal
+851,Perrserker,110,100,70,Galar,50,60,50,Steel,None
+852,Cursola,95,50,60,Galar,145,130,30,Ghost,None
+853,Runerigus,95,145,58,Galar,50,105,30,Ground,Ghost
+854,Milcery,40,40,45,Galar,50,61,34,Fairy,None
+855,Alcremie,60,75,65,Galar,110,121,64,Fairy,None
+856,Falinks,100,100,65,Galar,70,60,75,Fighting,None
+857,Pincurchin,101,95,48,Galar,91,85,15,Electric,None
+858,Snom,25,35,30,Galar,45,30,20,Ice,Bug
+859,Frosmoth,65,60,70,Galar,125,90,65,Ice,Bug
+860,Stonjourner,125,135,100,Galar,20,20,70,Rock,None
+861,Eiscue,80,110,75,Galar,65,90,50,Ice,None
+862,Indeedee,65,55,60,Galar,105,95,95,Psychic,Normal
+863,Morpeko,95,58,58,Galar,70,58,97,Electric,Dark
+864,Cufant,80,49,72,Galar,40,49,40,Steel,None
+865,Copperajah,130,69,122,Galar,80,69,30,Steel,None
+866,Dracozolt,100,90,90,Galar,80,70,75,Electric,Dragon
+867,Arctozolt,100,90,90,Galar,90,80,55,Electric,Ice
+868,Dracovish,90,100,90,Galar,70,80,75,Water,Dragon
+869,Arctovish,90,100,90,Galar,80,90,55,Water,Ice
+870,Duraludon,95,115,70,Galar,120,50,85,Steel,Dragon
+871,Dreepy,60,30,28,Galar,40,30,82,Dragon,Ghost
+872,Drakloak,80,50,68,Galar,60,50,102,Dragon,Ghost
+873,Dragapult,120,75,88,Galar,100,75,142,Dragon,Ghost
+874,Zacian,170,115,92,Galar,80,115,148,Fairy,Steel
+875,Zamazenta,130,145,92,Galar,80,145,128,Fighting,Steel
+876,Eternatus,85,95,140,Galar,145,95,130,Poison,Dragon
+877,Kubfu,90,60,60,Galar,53,50,72,Fighting,None
+878,Urshifu,130,100,100,Galar,63,60,97,Fighting,Dark
+879,Zarude,120,105,105,Galar,70,95,105,Dark,Grass
+880,Regieleki,100,50,80,Galar,100,50,200,Electric,None
+881,Regidrago,100,50,200,Galar,100,50,80,Dragon,None
+882,Glastrier,145,130,100,Galar,65,110,30,Ice,None
+883,Spectrier,65,60,100,Galar,145,80,130,Ghost,None
+884,Calyrex,80,80,100,Galar,80,80,80,Psychic,Grass
diff --git a/s24/AmFam_Ashwin/28_Pandas-2/Lec_28_Pandas2_Worksheet.ipynb b/s24/AmFam_Ashwin/28_Pandas-2/Lec_28_Pandas2_Worksheet.ipynb
new file mode 100644
index 0000000..d53534a
--- /dev/null
+++ b/s24/AmFam_Ashwin/28_Pandas-2/Lec_28_Pandas2_Worksheet.ipynb
@@ -0,0 +1,878 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ff8cd732-d3ef-4348-8f9e-f85d8347b60d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas import Series, DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "193622e7",
+   "metadata": {},
+   "source": [
+    "## Question 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "131b9746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = Series([7,8,9], index=[-1,0,1])\n",
+    "x = Series({\"A\":1, \"B\":2, \"C\":3})\n",
+    "y = Series({\"A\":2, \"C\":12, \"D\":4})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1ac4fe7-2f5c-4596-8baf-e65826505ddb",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3bfa222f-7233-470c-ac81-633414ace644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "dbcfd0f6-ab80-4301-b93f-73f4ee1559c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8, 7)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums.loc[0], nums.iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b5532dde-7734-4158-87af-5f1034ed98a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 9)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums.loc[-1], nums.iloc[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "46978032-4cc1-4446-8a14-3d14896b25b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    0.50\n",
+       "B     NaN\n",
+       "C    0.25\n",
+       "D     NaN\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x / y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1a43c2d-77f6-4b5b-8040-ce55bc3bd746",
+   "metadata": {},
+   "source": [
+    "## Question 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e31936ed-f1bf-4a42-816e-7acaf72d9759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = Series([\"A\", \"B\", \"C\", \"D\"])\n",
+    "letters = Series([\"x\", \"y\", \"z\"], index=[1, 0, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee6319f4-c8a2-4ca1-a3c0-562c8d291ac3",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6a8113d0-a278-4211-9165-f0e9e2911b41",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "-1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                          Traceback (most recent call last)",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:414\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    413\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 414\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "\u001b[1;31mValueError\u001b[0m: -1 is not in range",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:1040\u001b[0m, in \u001b[0;36mSeries.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1037\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[key]\n\u001b[0;32m   1039\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key_is_scalar:\n\u001b[1;32m-> 1040\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_value(key)\n\u001b[0;32m   1042\u001b[0m \u001b[38;5;66;03m# Convert generator to list before going through hashable part\u001b[39;00m\n\u001b[0;32m   1043\u001b[0m \u001b[38;5;66;03m# (We will iterate through the generator there to check for slices)\u001b[39;00m\n\u001b[0;32m   1044\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\series.py:1156\u001b[0m, in \u001b[0;36mSeries._get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1153\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[label]\n\u001b[0;32m   1155\u001b[0m \u001b[38;5;66;03m# Similar to Index.get_value, but we do not fall back to positional\u001b[39;00m\n\u001b[1;32m-> 1156\u001b[0m loc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mget_loc(label)\n\u001b[0;32m   1158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(loc):\n\u001b[0;32m   1159\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_values[loc]\n",
+      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\range.py:416\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    414\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_range\u001b[38;5;241m.\u001b[39mindex(new_key)\n\u001b[0;32m    415\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 416\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m    418\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: -1"
+     ]
+    }
+   ],
+   "source": [
+    "s[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccea97ee-a9ad-460d-9b0b-ba34fd25d85a",
+   "metadata": {},
+   "source": [
+    "**Note:** `Series.loc[X]` looks for label `X` in the **index**. `Series.iloc[X]` looks for the **integer position** `X`. These names are confusing. `.iloc` supports negative indexing, but `.loc` does not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d1ebc597-4053-4a3d-9029-3c9f85d26d51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    C\n",
+       "3    D\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[-2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6bd25f2f-9ca6-49d6-9d48-bf67446d8e6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    AA\n",
+       "1    BB\n",
+       "2    CC\n",
+       "3    DD\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "68c20b9d-d918-44ab-9160-518ac59eb79b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'y'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "47d0f2ce-82ac-4736-93db-6e29a5e7e204",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Ay\n",
+       "1     Bx\n",
+       "2    NaN\n",
+       "3     Dz\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s + letters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1516ed42-fe76-48f8-bd01-40b027122ebf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    NaN\n",
+       "1     BB\n",
+       "2     CC\n",
+       "3    NaN\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s[1:] + s[:-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "880ef07f-a238-4e29-a7ec-141f5351046a",
+   "metadata": {},
+   "source": [
+    "## Question 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5885fd73-39f3-4cb7-be29-0063be34311a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = Series([-1, 1, 200, 191, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "978c4eaa-167e-4ec0-8327-e396a2fd6a4e",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c500df8d-b8f4-4d98-81aa-b6f98b61e84c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1    False\n",
+       "2    False\n",
+       "3    False\n",
+       "4    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v < 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a1284fc5-ded8-4604-b8fd-630fd33a9bc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1     True\n",
+       "2    False\n",
+       "3    False\n",
+       "4    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * v == 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2763fcb1-ff61-42ad-a116-38ca142b0feb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    200\n",
+       "3    191\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[v > 100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "73a59c5b-172e-487a-bd4f-3b3433b0f4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    200\n",
+       "4      4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[v % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4f5922de-c999-44c7-9cb0-020cd97aa375",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    1\n",
+       "4    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[(v>0) & (v<100)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efea4e6f-5ba7-4f0c-a1da-d8b2c3e13ce7",
+   "metadata": {},
+   "source": [
+    "## Question 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e886b5f8-e963-4981-b08e-cca8ecc3f35c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"storms.csv\"\n",
+    "tab = pd.read_csv(path)\n",
+    "map = DataFrame({\n",
+    " \"code\": [\"o\",\"p\",\"a\"],\n",
+    " \"where\": [\"other\",\"Pacific\",\"Atlantic\"]\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dddd34c4-0811-4674-b380-8fdc214313ff",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8d355d31-7110-4de8-84fa-ee2ad74d84bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    o\n",
+       "1    p\n",
+       "2    a\n",
+       "Name: code, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map[\"code\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "16b3d1ef-8c8a-4490-a8f8-583a3dda4215",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    o\n",
+       "1    p\n",
+       "2    a\n",
+       "Name: code, dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map.code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26236be6-8885-4929-b495-1479162349f0",
+   "metadata": {},
+   "source": [
+    "**Note:** `s.COL` is a shortcut for `s[\"COL\"]`, unless `COL` collides with a method name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b53e3d5e-5115-4f6e-85f5-9d93b94c6c62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(pandas.core.series.Series, method)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(map.code), type(map.where)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7e0d4aea-be66-463e-96fe-48c92a8525a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2017.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.year.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3bec8f0c-21a0-4bc4-9400-0e5785045e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2    False\n",
+       "3     True\n",
+       "4     True\n",
+       "Name: year, dtype: bool"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.year == 2018"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ab75fdb6-0644-4dae-8b65-440f92e0b882",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    dan\n",
+       "4    eve\n",
+       "Name: name, dtype: object"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.name[tab.year == 2018]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "705bc14f-22da-40ec-8074-35c6c2b04de3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    False\n",
+       "1    False\n",
+       "2     True\n",
+       "Name: where, dtype: bool"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map[\"where\"] == \"Atlantic\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "a13ae3f5-3e9b-47d3-8516-7443095526dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1    False\n",
+       "2    False\n",
+       "Name: where, dtype: bool"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = map[\"where\"] == \"other\"\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "da00f3e6-9738-43fc-bf89-ac45b2f780e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'o'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "code = map.code[b].item()\n",
+    "code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93757534-c08c-47f2-b27d-c36bf3b6e206",
+   "metadata": {},
+   "source": [
+    "**Note:** when a Series `s` contains exactly one one item, `s.item()` extracts it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "802a7774-fe99-4f37-ba08-e837d0255847",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    alice\n",
+       "2    cindy\n",
+       "3      dan\n",
+       "Name: name, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nms = tab.name[tab.place==code]\n",
+    "nms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd240641-aff4-4b1a-aeb5-c483df839ab7",
+   "metadata": {},
+   "source": [
+    "## Question 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79726356-b538-447f-9e5b-12ab0d9dea31",
+   "metadata": {},
+   "source": [
+    "### Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1fb1baf1-281c-4626-a6e4-c940d98e0741",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name       alice\n",
+       "year        2016\n",
+       "type     tornado\n",
+       "speed        100\n",
+       "place          o\n",
+       "Name: 0, dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4d195552-9690-44b4-96ba-2f669d1c51f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'hurricane'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc[4, \"type\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b3e5effe-0f85-4802-a760-864289a08010",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'mainland'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "map.loc[0,\"where\"] = \"mainland\"\n",
+    "place = map[\"where\"][0]\n",
+    "place"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "1e444e08-b2c1-4f36-96fe-24fd83447d97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    101\n",
+       "1    201\n",
+       "2    151\n",
+       "3    301\n",
+       "4    251\n",
+       "Name: speed, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab.loc[:, \"speed\"] += 1 \n",
+    "col = tab.speed\n",
+    "col"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/IMDB-Movie-Data.csv b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/IMDB-Movie-Data.csv
new file mode 100644
index 0000000..e797099
--- /dev/null
+++ b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/IMDB-Movie-Data.csv	
@@ -0,0 +1,999 @@
+Index,Title,Genre,Director,Cast,Year,Runtime,Rating,Revenue
+0,Guardians of the Galaxy,"Action,Adventure,Sci-Fi",James Gunn,"Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana",2014,121,8.1,333.13
+1,Prometheus,"Adventure,Mystery,Sci-Fi",Ridley Scott,"Noomi Rapace, Logan Marshall-Green, Michael         fassbender, Charlize Theron",2012,124,7,126.46M
+2,Split,"Horror,Thriller",M. Night Shyamalan,"James McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula",2016,117,7.3,138.12M
+3,Sing,"Animation,Comedy,Family",Christophe Lourdelet,"Matthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson",2016,108,7.2,270.32
+4,Suicide Squad,"Action,Adventure,Fantasy",David Ayer,"Will Smith, Jared Leto, Margot Robbie, Viola Davis",2016,123,6.2,325.02
+5,The Great Wall,"Action,Adventure,Fantasy",Yimou Zhang,"Matt Damon, Tian Jing, Willem Dafoe, Andy Lau",2016,103,6.1,45.13
+6,La La Land,"Comedy,Drama,Music",Damien Chazelle,"Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons",2016,128,8.3,151.06M
+7,Mindhorn,Comedy,Sean Foley,"Essie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh",2016,89,6.4,0
+8,The Lost City of Z,"Action,Adventure,Biography",James Gray,"Charlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland",2016,141,7.1,8.01
+9,Passengers,"Adventure,Drama,Romance",Morten Tyldum,"Jennifer Lawrence, Chris Pratt, Michael Sheen,Laurence Fishburne",2016,116,7,100.01M
+10,Fantastic Beasts and Where to Find Them,"Adventure,Family,Fantasy",David Yates,"Eddie Redmayne, Katherine Waterston, Alison Sudol,Dan Fogler",2016,133,7.5,234.02
+11,Hidden Figures,"Biography,Drama,History",Theodore Melfi,"Taraji P. Henson, Octavia Spencer, Janelle Monáe,Kevin Costner",2016,127,7.8,169.27M
+12,Rogue One,"Action,Adventure,Sci-Fi",Gareth Edwards,"Felicity Jones, Diego Luna, Alan Tudyk, Donnie Yen",2016,133,7.9,532.17
+13,Moana,"Animation,Adventure,Comedy",Ron Clements,"Auli'i Cravalho, Dwayne Johnson, Rachel House, Temuera Morrison",2016,107,7.7,248.75
+14,Colossal,"Action,Comedy,Drama",Nacho Vigalondo,"Anne Hathaway, Jason Sudeikis, Austin Stowell,Tim Blake Nelson",2016,109,6.4,2.87
+15,The Secret Life of Pets,"Animation,Adventure,Comedy",Chris Renaud,"Louis C.K., Eric Stonestreet, Kevin Hart, Lake Bell",2016,87,6.6,368.31
+16,Hacksaw Ridge,"Biography,Drama,History",Mel Gibson,"Andrew Garfield, Sam Worthington, Luke Bracey,Teresa Palmer",2016,139,8.2,67.12
+17,Jason Bourne,"Action,Thriller",Paul Greengrass,"Matt Damon, Tommy Lee Jones, Alicia Vikander,Vincent Cassel",2016,123,6.7,162.16M
+18,Lion,"Biography,Drama",Garth Davis,"Dev Patel, Nicole Kidman, Rooney Mara, Sunny Pawar",2016,118,8.1,51.69
+19,Arrival,"Drama,Mystery,Sci-Fi",Denis Villeneuve,"Amy Adams, Jeremy Renner, Forest Whitaker,Michael Stuhlbarg",2016,116,8,100.5M
+20,Gold,"Adventure,Drama,Thriller",Stephen Gaghan,"Matthew McConaughey, Edgar Ramírez, Bryce Dallas Howard, Corey Stoll",2016,120,6.7,7.22
+21,Manchester by the Sea,Drama,Kenneth Lonergan,"Casey Affleck, Michelle Williams, Kyle Chandler,Lucas Hedges",2016,137,7.9,47.7
+22,Hounds of Love,"Crime,Drama,Horror",Ben Young,"Emma Booth, Ashleigh Cummings, Stephen Curry,Susie Porter",2016,108,6.7,0
+23,Trolls,"Animation,Adventure,Comedy",Walt Dohrn,"Anna Kendrick, Justin Timberlake,Zooey Deschanel, Christopher Mintz-Plasse",2016,92,6.5,153.69M
+24,Independence Day: Resurgence,"Action,Adventure,Sci-Fi",Roland Emmerich,"Liam Hemsworth, Jeff Goldblum, Bill Pullman,Maika Monroe",2016,120,5.3,103.14M
+25,Paris pieds nus,Comedy,Dominique Abel,"Fiona Gordon, Dominique Abel,Emmanuelle Riva, Pierre Richard",2016,83,6.8,0
+26,Bahubali: The Beginning,"Action,Adventure,Drama",S.S. Rajamouli,"Prabhas, Rana Daggubati, Anushka Shetty,Tamannaah Bhatia",2015,159,8.3,6.5
+27,Dead Awake,"Horror,Thriller",Phillip Guzman,"Jocelin Donahue, Jesse Bradford, Jesse Borrego,Lori Petty",2016,99,4.7,0.01
+28,Bad Moms,Comedy,Jon Lucas,"Mila Kunis, Kathryn Hahn, Kristen Bell,Christina Applegate",2016,100,6.2,113.08M
+29,Assassin's Creed,"Action,Adventure,Drama",Justin Kurzel,"Michael      Fassbender, Marion Cotillard, Jeremy Irons,Brendan Gleeson",2016,115,5.9,54.65
+30,Why Him?,Comedy,John Hamburg,"Zoey Deutch, James Franco, Tangie Ambrose,Cedric the Entertainer",2016,111,6.3,60.31
+31,Nocturnal Animals,"Drama,Thriller",Tom Ford,"Amy Adams, Jake Gyllenhaal, Michael Shannon, Aaron Taylor-Johnson",2016,116,7.5,10.64
+32,X-Men: Apocalypse,"Action,Adventure,Sci-Fi",Bryan Singer,"James McAvoy, Michael Fassbender, Jennifer Lawrence, Nicholas Hoult",2016,144,7.1,155.33M
+33,Deadpool,"Action,Adventure,Comedy",Tim Miller,"Ryan Reynolds, Morena Baccarin, T.J. Miller, Ed Skrein",2016,108,8,363.02
+34,Resident Evil: The Final Chapter,"Action,Horror,Sci-Fi",Paul W.S. Anderson,"Milla Jovovich, Iain Glen, Ali Larter, Shawn Roberts",2016,107,5.6,26.84
+35,Captain America: Civil War,"Action,Adventure,Sci-Fi",Anthony Russo,"Chris Evans, Robert Downey Jr.,Scarlett Johansson, Sebastian Stan",2016,147,7.9,408.08
+36,Interstellar,"Adventure,Drama,Sci-Fi",Christopher Nolan,"Matthew McConaughey, Anne Hathaway, Jessica Chastain, Mackenzie Foy",2014,169,8.6,187.99M
+37,Doctor Strange,"Action,Adventure,Fantasy",Scott Derrickson,"Benedict Cumberbatch, Chiwetel Ejiofor, Rachel McAdams, Benedict Wong",2016,115,7.6,232.6
+38,The Magnificent Seven,"Action,Adventure,Western",Antoine Fuqua,"Denzel      washington, Chris Pratt, Ethan Hawke,Vincent D'Onofrio",2016,132,6.9,93.38
+39,5/25/1977,"Comedy,Drama",Patrick Read Johnson,"John Francis Daley, Austin Pendleton, Colleen Camp, Neil Flynn",2007,113,7.1,0
+40,Sausage Party,"Animation,Adventure,Comedy",Greg Tiernan,"Seth Rogen, Kristen Wiig, Jonah Hill, Alistair Abell",2016,89,6.3,97.66
+41,Moonlight,Drama,Barry Jenkins,"Mahershala Ali, Shariff Earp, Duan Sanderson, Alex R. Hibbert",2016,111,7.5,27.85
+42,Don't Fuck in the Woods,Horror,Shawn Burkett,"Brittany Blanton, Ayse Howard, Roman Jossart,Nadia White",2016,73,2.7,0
+43,The Founder,"Biography,Drama,History",John Lee Hancock,"Michael Keaton, Nick Offerman, John Carroll Lynch, Linda Cardellini",2016,115,7.2,12.79
+44,Lowriders,Drama,Ricardo de Montreuil,"Gabriel Chavarria, Demián Bichir, Theo Rossi,Tony Revolori",2016,99,6.3,4.21
+45,Pirates of the Caribbean: On Stranger Tides,"Action,Adventure,Fantasy",Rob Marshall,"Johnny Depp, Penélope Cruz, Ian McShane, Geoffrey Rush",2011,136,6.7,241.06
+46,Miss Sloane,"Drama,Thriller",John Madden,"Jessica Chastain, Mark Strong, Gugu Mbatha-Raw,Michael Stuhlbarg",2016,132,7.3,3.44
+47,Fallen,"Adventure,Drama,Fantasy",Scott Hicks,"Hermione Corfield, Addison Timlin, Joely Richardson,Jeremy Irvine",2016,91,5.6,0
+48,Star Trek Beyond,"Action,Adventure,Sci-Fi",Justin Lin,"Chris Pine, Zachary Quinto, Karl Urban, Zoe Saldana",2016,122,7.1,158.8M
+49,The Last Face,Drama,Sean Penn,"Charlize Theron, Javier Bardem, Adèle Exarchopoulos,Jared Harris",2016,130,3.7,0
+50,Star Wars: Episode VII - The Force Awakens,"Action,Adventure,Fantasy",J.J. Abrams,"Daisy Ridley, John Boyega, Oscar Isaac, Domhnall Gleeson",2015,136,8.1,936.63
+51,Underworld: Blood Wars,"Action,Adventure,Fantasy",Anna Foerster,"Kate Beckinsale, Theo James, Tobias Menzies, Lara Pulver",2016,91,5.8,30.35
+52,Mother's Day,"Comedy,Drama",Garry Marshall,"Jennifer Aniston, Kate Hudson, Julia Roberts, Jason Sudeikis",2016,118,5.6,32.46
+53,John Wick,"Action,Crime,Thriller",Chad Stahelski,"Keanu Reeves, Michael Nyqvist, Alfie Allen, Willem Dafoe",2014,101,7.2,43
+54,The Dark Knight,"Action,Crime,Drama",Christopher Nolan,"Christian Bale, Heath Ledger, Aaron Eckhart,Michael Caine",2008,152,9,533.32
+55,Silence,"Adventure,Drama,History",Martin Scorsese,"Andrew Garfield, Adam Driver, Liam Neeson,Tadanobu Asano",2016,161,7.3,7.08
+56,Don't Breathe,"Crime,Horror,Thriller",Fede Alvarez,"Stephen Lang, Jane Levy, Dylan Minnette, Daniel Zovatto",2016,88,7.2,89.21
+57,Me Before You,"Drama,Romance",Thea Sharrock,"Emilia Clarke, Sam Claflin, Janet McTeer, Charles Dance",2016,106,7.4,56.23
+58,Their Finest,"Comedy,Drama,Romance",Lone Scherfig,"Gemma Arterton, Sam Claflin, Bill Nighy, Jack Huston",2016,117,7,3.18
+59,Sully,"Biography,Drama",Clint Eastwood,"Tom Hanks, Aaron Eckhart, Laura Linney, Valerie Mahaffey",2016,96,7.5,125.07M
+60,Batman v Superman: Dawn of Justice,"Action,Adventure,Sci-Fi",Zack Snyder,"Ben Affleck, Henry Cavill, Amy Adams, Jesse Eisenberg",2016,151,6.7,330.25
+61,The Autopsy of Jane Doe,"Horror,Mystery,Thriller",André Øvredal,"Brian Cox, Emile Hirsch, Ophelia Lovibond, Michael McElhatton",2016,86,6.8,0
+62,The Girl on the Train,"Crime,Drama,Mystery",Tate Taylor,"Emily Blunt, Haley Bennett, Rebecca Ferguson, Justin Theroux",2016,112,6.5,75.31
+63,Fifty Shades of Grey,"Drama,Romance,Thriller",Sam Taylor-Johnson,"Dakota Johnson, Jamie Dornan, Jennifer Ehle,Eloise Mumford",2015,125,4.1,166.15M
+64,The Prestige,"Drama,Mystery,Sci-Fi",Christopher Nolan,"Christian Bale, Hugh Jackman, Scarlett Johansson, Michael Caine",2006,130,8.5,53.08
+65,Kingsman: The Secret Service,"Action,Adventure,Comedy",Matthew Vaughn,"Colin Firth, Taron Egerton, Samuel L. Jackson,Michael Caine",2014,129,7.7,128.25M
+66,Patriots Day,"Drama,History,Thriller",Peter Berg,"Mark Wahlberg, Michelle Monaghan, J.K. Simmons, John Goodman",2016,133,7.4,31.86
+67,Mad Max: Fury Road,"Action,Adventure,Sci-Fi",George Miller,"Tom Hardy, Charlize Theron, Nicholas Hoult, Zoë Kravitz",2015,120,8.1,153.63M
+68,Wakefield,Drama,Robin Swicord,"Bryan Cranston, Jennifer Garner, Beverly D'Angelo,Jason O'Mara",2016,106,7.5,0.01
+69,Deepwater Horizon,"Action,Drama,Thriller",Peter Berg,"Mark Wahlberg, Kurt Russell, Douglas M. Griffin, James DuMont",2016,107,7.2,61.28
+70,The Promise,"Drama,History",Terry George,"Oscar Isaac, Charlotte Le Bon, Christian Bale, Daniel Giménez Cacho",2016,133,5.9,0
+71,Allied,"Action,Drama,Romance",Robert Zemeckis,"Brad Pitt, Marion Cotillard, Jared Harris, Vincent Ebrahim",2016,124,7.1,40.07
+72,A Monster Calls,"Drama,Fantasy",J.A. Bayona,"Lewis MacDougall, Sigourney Weaver, Felicity Jones,Toby Kebbell",2016,108,7.5,3.73
+73,Collateral Beauty,"Drama,Romance",David Frankel,"Will Smith, Edward Norton, Kate Winslet, Michael Peña",2016,97,6.8,30.98
+74,Zootopia,"Animation,Adventure,Comedy",Byron Howard,"Ginnifer Goodwin, Jason Bateman, Idris Elba, Jenny Slate",2016,108,8.1,341.26
+75,Pirates of the Caribbean: At World's End,"Action,Adventure,Fantasy",Gore Verbinski,"Johnny Depp, Orlando Bloom, Keira Knightley,Geoffrey Rush",2007,169,7.1,309.4
+76,The Avengers,"Action,Sci-Fi",Joss Whedon,"Robert Downey Jr., Chris Evans, Scarlett Johansson,Jeremy Renner",2012,143,8.1,623.28
+77,Inglourious Basterds,"Adventure,Drama,War",Quentin Tarantino,"Brad Pitt, Diane Kruger, Eli Roth,Mélanie Laurent",2009,153,8.3,120.52M
+78,Pirates of the Caribbean: Dead Man's Chest,"Action,Adventure,Fantasy",Gore Verbinski,"Johnny Depp, Orlando Bloom, Keira Knightley, Jack Davenport",2006,151,7.3,423.03
+79,Ghostbusters,"Action,Comedy,Fantasy",Paul Feig,"Melissa McCarthy, Kristen Wiig, Kate McKinnon, Leslie Jones",2016,116,5.3,128.34M
+80,Inception,"Action,Adventure,Sci-Fi",Christopher Nolan,"Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen Page, Ken Watanabe",2010,148,8.8,292.57
+81,Captain Fantastic,"Comedy,Drama",Matt Ross,"Viggo Mortensen, George MacKay, Samantha Isler,Annalise Basso",2016,118,7.9,5.88
+82,The Wolf of Wall Street,"Biography,Comedy,Crime",Martin Scorsese,"Leonardo DiCaprio, Jonah Hill, Margot Robbie,Matthew McConaughey",2013,180,8.2,116.87M
+83,Gone Girl,"Crime,Drama,Mystery",David Fincher,"Ben Affleck, Rosamund Pike, Neil Patrick Harris,Tyler Perry",2014,149,8.1,167.74M
+84,Furious Seven,"Action,Crime,Thriller",James Wan,"Vin Diesel, Paul Walker, Dwayne Johnson, Jason Statham",2015,137,7.2,350.03
+85,Jurassic World,"Action,Adventure,Sci-Fi",Colin Trevorrow,"Chris Pratt, Bryce Dallas Howard, Ty Simpkins,Judy Greer",2015,124,7,652.18
+86,Live by Night,"Crime,Drama",Ben Affleck,"Ben Affleck, Elle Fanning, Brendan Gleeson, Chris Messina",2016,129,6.4,10.38
+87,Avatar,"Action,Adventure,Fantasy",James Cameron,"Sam Worthington, Zoe Saldana, Sigourney Weaver, Michelle Rodriguez",2009,162,7.8,760.51
+88,The Hateful Eight,"Crime,Drama,Mystery",Quentin Tarantino,"Samuel L. Jackson, Kurt Russell, Jennifer Jason Leigh, Walton Goggins",2015,187,7.8,54.12
+89,The Accountant,"Action,Crime,Drama",Gavin O'Connor,"Ben Affleck, Anna Kendrick, J.K. Simmons, Jon Bernthal",2016,128,7.4,86.2
+90,Prisoners,"Crime,Drama,Mystery",Denis Villeneuve,"Hugh Jackman, Jake Gyllenhaal, Viola Davis,Melissa Leo",2013,153,8.1,60.96
+91,Warcraft,"Action,Adventure,Fantasy",Duncan Jones,"Travis Fimmel, Paula Patton, Ben Foster, Dominic Cooper",2016,123,7,47.17
+92,The Help,Drama,Tate Taylor,"Emma Stone, Viola Davis, Octavia Spencer, Bryce Dallas Howard",2011,146,8.1,169.71M
+93,War Dogs,"Comedy,Crime,Drama",Todd Phillips,"Jonah Hill, Miles Teller, Steve Lantz, Gregg Weiner",2016,114,7.1,43.02
+94,Avengers: Age of Ultron,"Action,Adventure,Sci-Fi",Joss Whedon,"Robert Downey Jr., Chris Evans, Mark Ruffalo, Chris Hemsworth",2015,141,7.4,458.99
+95,The Nice Guys,"Action,Comedy,Crime",Shane Black,"Russell Crowe, Ryan Gosling, Angourie Rice, Matt Bomer",2016,116,7.4,36.25
+96,Kimi no na wa,"Animation,Drama,Fantasy",Makoto Shinkai,"Ryûnosuke Kamiki, Mone Kamishiraishi, Ryô Narita, Aoi Yuki",2016,106,8.6,4.68
+97,The Void,"Horror,Mystery,Sci-Fi",Jeremy Gillespie,"Aaron Poole, Kenneth Welsh,Daniel Fathers, Kathleen Munroe",2016,90,5.8,0.15
+98,Personal Shopper,"Drama,Mystery,Thriller",Olivier Assayas,"Kristen Stewart, Lars Eidinger, Sigrid Bouaziz,Anders Danielsen Lie",2016,105,6.3,1.29
+99,The Departed,"Crime,Drama,Thriller",Martin Scorsese,"Leonardo DiCaprio, Matt Damon, Jack Nicholson, Mark Wahlberg",2006,151,8.5,132.37M
+100,Legend,"Biography,Crime,Drama",Brian Helgeland,"Tom Hardy, Emily Browning, Taron Egerton, Paul Anderson",2015,132,7,1.87
+101,Thor,"Action,Adventure,Fantasy",Kenneth Branagh,"Chris Hemsworth, Anthony Hopkins, Natalie Portman, Tom Hiddleston",2011,115,7,181.02M
+102,The Martian,"Adventure,Drama,Sci-Fi",Ridley Scott,"Matt Damon, Jessica Chastain, Kristen Wiig, Kate Mara",2015,144,8,228.43
+103,Contratiempo,"Crime,Mystery,Thriller",Oriol Paulo,"Mario Casas, Ana Wagener, José Coronado, Bárbara Lennie",2016,106,7.9,0
+104,The Man from U.N.C.L.E.,"Action,Adventure,Comedy",Guy Ritchie,"Henry Cavill, Armie Hammer, Alicia Vikander, Elizabeth Debicki",2015,116,7.3,45.43
+105,Hell or High Water,"Crime,Drama,Thriller",David Mackenzie,"Chris Pine, Ben Foster, Jeff Bridges, Gil Birmingham",2016,102,7.7,26.86
+106,The Comedian,Comedy,Taylor Hackford,"Robert De Niro, Leslie Mann, Danny DeVito, Edie Falco",2016,120,5.4,1.66
+107,The Legend of Tarzan,"Action,Adventure,Drama",David Yates,"Alexander Skarsgård, Rory J. Saper, Christian Stevens, Christoph Waltz",2016,110,6.3,126.59M
+108,All We Had,Drama,Katie Holmes,"Eve Lindley, Richard Kind, Mark Consuelos, Katherine Reis",2016,105,5.8,0
+109,Ex Machina,"Drama,Mystery,Sci-Fi",Alex Garland,"Alicia Vikander, Domhnall Gleeson, Oscar Isaac,Sonoya Mizuno",2014,108,7.7,25.44
+110,The Belko Experiment,"Action,Horror,Thriller",Greg McLean,"John Gallagher Jr., Tony Goldwyn, Adria Arjona, John C. McGinley",2016,89,6.3,10.16
+111,12 Years a Slave,"Biography,Drama,History",Steve McQueen,"Chiwetel Ejiofor, Michael Kenneth Williams, Michael Fassbender, Brad Pitt",2013,134,8.1,56.67
+112,The Bad Batch,"Romance,Sci-Fi",Ana Lily Amirpour,"Keanu Reeves, Jason Momoa, Jim Carrey, Diego Luna",2016,118,6.1,0
+113,300,"Action,Fantasy,War",Zack Snyder,"Gerard Butler, Lena Headey, David Wenham, Dominic West",2006,117,7.7,210.59
+114,Harry Potter and the Deathly Hallows: Part 2,"Adventure,Drama,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon",2011,130,8.1,380.96
+115,Office Christmas Party,Comedy,Josh Gordon,"Jason Bateman, Olivia Munn, T.J. Miller,Jennifer Aniston",2016,105,5.8,54.73
+116,The Neon Demon,"Horror,Thriller",Nicolas Winding Refn,"Elle Fanning, Christina Hendricks, Keanu Reeves, Karl Glusman",2016,118,6.2,1.33
+117,Dangal,"Action,Biography,Drama",Nitesh Tiwari,"Aamir Khan, Sakshi Tanwar, Fatima Sana Shaikh,Sanya Malhotra",2016,161,8.8,11.15
+118,10 Cloverfield Lane,"Drama,Horror,Mystery",Dan Trachtenberg,"John Goodman, Mary Elizabeth Winstead, John Gallagher Jr., Douglas M. Griffin",2016,104,7.2,71.9
+119,Finding Dory,"Animation,Adventure,Comedy",Andrew Stanton,"Ellen DeGeneres, Albert Brooks,Ed O'Neill, Kaitlin Olson",2016,97,7.4,486.29
+120,Miss Peregrine's Home for Peculiar Children,"Adventure,Drama,Family",Tim Burton,"Eva Green, Asa Butterfield, Samuel L. Jackson, Judi Dench",2016,127,6.7,87.24
+121,Divergent,"Adventure,Mystery,Sci-Fi",Neil Burger,"Shailene Woodley, Theo James, Kate Winslet, Jai Courtney",2014,139,6.7,150.83M
+122,Mike and Dave Need Wedding Dates,"Adventure,Comedy,Romance",Jake Szymanski,"Zac Efron, Adam Devine, Anna Kendrick, Aubrey Plaza",2016,98,6,46.01
+123,Boyka: Undisputed IV,Action,Todor Chapkanov,"Scott Adkins, Teodora Duhovnikova, Alon Aboutboul, Julian Vergov",2016,86,7.4,0
+124,The Dark Knight Rises,"Action,Thriller",Christopher Nolan,"Christian Bale, Tom Hardy, Anne Hathaway,Gary Oldman",2012,164,8.5,448.13
+125,The Jungle Book,"Adventure,Drama,Family",Jon Favreau,"Neel Sethi, Bill Murray, Ben Kingsley, Idris Elba",2016,106,7.5,364
+126,Transformers: Age of Extinction,"Action,Adventure,Sci-Fi",Michael Bay,"Mark Wahlberg, Nicola Peltz, Jack Reynor, Stanley Tucci",2014,165,5.7,245.43
+127,Nerve,"Adventure,Crime,Mystery",Henry Joost,"Emma Roberts, Dave Franco, Emily Meade, Miles Heizer",2016,96,6.6,38.56
+128,Mamma Mia!,"Comedy,Family,Musical",Phyllida Lloyd,"Meryl Streep, Pierce Brosnan, Amanda Seyfried,Stellan Skarsgård",2008,108,6.4,143.7M
+129,The Revenant,"Adventure,Drama,Thriller",Alejandro González Iñárritu,"Leonardo DiCaprio, Tom Hardy, Will Poulter, Domhnall Gleeson",2015,156,8,183.64M
+130,Fences,Drama,Denzel Washington,"Denzel  Washington, Viola Davis, Stephen Henderson, Jovan Adepo",2016,139,7.3,57.64
+131,Into the Woods,"Adventure,Comedy,Drama",Rob Marshall,"Anna Kendrick, Meryl Streep, Chris Pine, Emily Blunt",2014,125,6,128M
+132,The Shallows,"Drama,Horror,Thriller",Jaume Collet-Serra,"Blake Lively, Óscar Jaenada, Angelo Josue Lozano Corzo, Brett Cullen",2016,86,6.4,55.12
+133,Whiplash,"Drama,Music",Damien Chazelle,"Miles Teller, J.K. Simmons, Melissa Benoist, Paul Reiser",2014,107,8.5,13.09
+134,Furious 6,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Dwayne Johnson, Michelle Rodriguez",2013,130,7.1,238.67
+135,The Place Beyond the Pines,"Crime,Drama,Thriller",Derek Cianfrance,"Ryan Gosling, Bradley Cooper, Eva Mendes,Craig Van Hook",2012,140,7.3,21.38
+136,No Country for Old Men,"Crime,Drama,Thriller",Ethan Coen,"Tommy Lee Jones, Javier Bardem, Josh Brolin, Woody Harrelson",2007,122,8.1,74.27
+137,The Great Gatsby,"Drama,Romance",Baz Luhrmann,"Leonardo DiCaprio, Carey Mulligan, Joel Edgerton,Tobey Maguire",2013,143,7.3,144.81M
+138,Shutter Island,"Mystery,Thriller",Martin Scorsese,"Leonardo DiCaprio, Emily Mortimer, Mark Ruffalo,Ben Kingsley",2010,138,8.1,127.97M
+139,Brimstone,"Mystery,Thriller,Western",Martin Koolhoven,"Dakota Fanning, Guy Pearce, Kit Harington,Carice van Houten",2016,148,7.1,0
+140,Star Trek,"Action,Adventure,Sci-Fi",J.J. Abrams,"Chris Pine, Zachary Quinto, Simon Pegg, Leonard Nimoy",2009,127,8,257.7
+141,Diary of a Wimpy Kid,"Comedy,Family",Thor Freudenthal,"Zachary Gordon, Robert Capron, Rachael Harris,Steve Zahn",2010,94,6.2,64
+142,The Big Short,"Biography,Comedy,Drama",Adam McKay,"Christian Bale, Steve Carell, Ryan Gosling, Brad Pitt",2015,130,7.8,70.24
+143,Room,Drama,Lenny Abrahamson,"Brie Larson, Jacob Tremblay, Sean Bridgers,Wendy Crewson",2015,118,8.2,14.68
+144,Django Unchained,"Drama,Western",Quentin Tarantino,"Jamie Foxx, Christoph Waltz, Leonardo DiCaprio,Kerry Washington",2012,165,8.4,162.8M
+145,Ah-ga-ssi,"Drama,Mystery,Romance",Chan-wook Park,"Min-hee Kim, Jung-woo Ha, Jin-woong Jo, So-ri Moon",2016,144,8.1,2.01
+146,The Edge of Seventeen,"Comedy,Drama",Kelly Fremon Craig,"Hailee Steinfeld, Haley Lu Richardson, Blake Jenner, Kyra Sedgwick",2016,104,7.4,14.26
+147,Watchmen,"Action,Drama,Mystery",Zack Snyder,"Jackie Earle Haley, Patrick Wilson, Carla Gugino,Malin Akerman",2009,162,7.6,107.5M
+148,Superbad,Comedy,Greg Mottola,"Michael Cera, Jonah Hill, Christopher Mintz-Plasse, Bill Hader",2007,113,7.6,121.46M
+149,Inferno,"Action,Adventure,Crime",Ron Howard,"Tom Hanks, Felicity Jones, Irrfan Khan, Ben Foster",2016,121,6.2,34.26
+150,The BFG,"Adventure,Family,Fantasy",Steven Spielberg,"Mark Rylance, Ruby Barnhill, Penelope Wilton,Jemaine Clement",2016,117,6.4,55.47
+151,The Hunger Games,"Adventure,Sci-Fi,Thriller",Gary Ross,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth,Stanley Tucci",2012,142,7.2,408
+152,White Girl,Drama,Elizabeth Wood,"Morgan Saylor, Brian Marc, Justin Bartha, Adrian Martinez",2016,88,5.8,0.2
+153,Sicario,"Action,Crime,Drama",Denis Villeneuve,"Emily Blunt, Josh Brolin, Benicio Del Toro, Jon Bernthal",2015,121,7.6,46.88
+154,Twin Peaks: The Missing Pieces,"Drama,Horror,Mystery",David Lynch,"Chris Isaak, Kiefer Sutherland, C.H. Evans, Sandra Kinder",2014,91,8.1,0
+155,Aliens vs Predator - Requiem,"Action,Horror,Sci-Fi",Colin Strause,"Reiko Aylesworth, Steven Pasquale,Shareeka Epps, John Ortiz",2007,94,4.7,41.8
+156,Pacific Rim,"Action,Adventure,Sci-Fi",Guillermo del Toro,"Idris Elba, Charlie Hunnam, Rinko Kikuchi,Charlie Day",2013,131,7,101.79M
+157,"Crazy, Stupid, Love.","Comedy,Drama,Romance",Glenn Ficarra,"Steve Carell, Ryan Gosling, Julianne Moore, Emma Stone",2011,118,7.4,84.24
+158,Scott Pilgrim vs. the World,"Action,Comedy,Fantasy",Edgar Wright,"Michael Cera, Mary Elizabeth Winstead, Kieran Culkin, Alison Pill",2010,112,7.5,31.49
+159,Hot Fuzz,"Action,Comedy,Mystery",Edgar Wright,"Simon Pegg, Nick Frost, Martin Freeman, Bill Nighy",2007,121,7.9,23.62
+160,Mine,"Thriller,War",Fabio Guaglione,"Armie Hammer, Annabelle Wallis,Tom Cullen, Clint Dyer",2016,106,6,0
+161,Free Fire,"Action,Comedy,Crime",Ben Wheatley,"Sharlto Copley, Brie Larson, Armie Hammer, Cillian Murphy",2016,90,7,1.8
+162,X-Men: Days of Future Past,"Action,Adventure,Sci-Fi",Bryan Singer,"Patrick Stewart, Ian McKellen, Hugh Jackman, James McAvoy",2014,132,8,233.91
+163,Jack Reacher: Never Go Back,"Action,Adventure,Crime",Edward Zwick,"Tom Cruise, Cobie Smulders, Aldis Hodge, Robert Knepper",2016,118,6.1,58.4
+164,Casino Royale,"Action,Adventure,Thriller",Martin Campbell,"Daniel Craig, Eva Green, Judi Dench, Jeffrey Wright",2006,144,8,167.01M
+165,Twilight,"Drama,Fantasy,Romance",Catherine Hardwicke,"Kristen Stewart, Robert Pattinson, Billy Burke,Sarah Clarke",2008,122,5.2,191.45M
+166,Now You See Me 2,"Action,Adventure,Comedy",Jon M. Chu,"Jesse Eisenberg, Mark Ruffalo, Woody Harrelson, Dave Franco",2016,129,6.5,65.03
+167,Woman in Gold,"Biography,Drama,History",Simon Curtis,"Helen Mirren, Ryan Reynolds, Daniel Brühl, Katie Holmes",2015,109,7.3,33.31
+168,13 Hours,"Action,Drama,History",Michael Bay,"John Krasinski, Pablo Schreiber, James Badge Dale,David Denman",2016,144,7.3,52.82
+169,Spectre,"Action,Adventure,Thriller",Sam Mendes,"Daniel Craig, Christoph Waltz, Léa Seydoux, Ralph Fiennes",2015,148,6.8,200.07
+170,Nightcrawler,"Crime,Drama,Thriller",Dan Gilroy,"Jake Gyllenhaal, Rene Russo, Bill Paxton, Riz Ahmed",2014,118,7.9,32.28
+171,Kubo and the Two Strings,"Animation,Adventure,Family",Travis Knight,"Charlize Theron, Art Parkinson, Matthew McConaughey, Ralph Fiennes",2016,101,7.9,48.02
+172,Beyond the Gates,"Adventure,Horror",Jackson Stewart,"Graham Skipper, Chase Williamson, Brea Grant,Barbara Crampton",2016,84,5.2,0
+173,Her,"Drama,Romance,Sci-Fi",Spike Jonze,"Joaquin Phoenix, Amy Adams, Scarlett Johansson,Rooney Mara",2013,126,8,25.56
+174,Frozen,"Animation,Adventure,Comedy",Chris Buck,"Kristen Bell, Idina Menzel, Jonathan Groff, Josh Gad",2013,102,7.5,400.74
+175,Tomorrowland,"Action,Adventure,Family",Brad Bird,"George Clooney, Britt Robertson, Hugh Laurie, Raffey Cassidy",2015,130,6.5,93.42
+176,Dawn of the Planet of the Apes,"Action,Adventure,Drama",Matt Reeves,"Gary Oldman, Keri Russell, Andy Serkis, Kodi Smit-McPhee",2014,130,7.6,208.54
+177,Tropic Thunder,"Action,Comedy",Ben Stiller,"Ben Stiller, Jack Black, Robert Downey Jr., Jeff Kahn",2008,107,7,110.42M
+178,The Conjuring 2,"Horror,Mystery,Thriller",James Wan,"Vera Farmiga, Patrick Wilson, Madison Wolfe, Frances O'Connor",2016,134,7.4,102.46M
+179,Ant-Man,"Action,Adventure,Comedy",Peyton Reed,"Paul Rudd, Michael Douglas, Corey Stoll, Evangeline Lilly",2015,117,7.3,180.19M
+180,Bridget Jones's Baby,"Comedy,Romance",Sharon Maguire,"Renée Zellweger, Gemma Jones, Jim Broadbent,Sally Phillips",2016,118,6.7,24.09
+181,The VVitch: A New-England Folktale,"Horror,Mystery",Robert Eggers,"Anya Taylor-Joy, Ralph Ineson, Kate Dickie, Julian Richings",2015,92,6.8,25.14
+182,Cinderella,"Drama,Family,Fantasy",Kenneth Branagh,"Lily James, Cate Blanchett, Richard Madden,Helena Bonham Carter",2015,105,7,201.15
+183,Realive,Sci-Fi,Mateo Gil,"Tom Hughes, Charlotte Le Bon, Oona Chaplin, Barry Ward",2016,112,5.9,0
+184,Forushande,"Drama,Thriller",Asghar Farhadi,"Taraneh Alidoosti, Shahab Hosseini, Babak Karimi,Farid Sajjadi Hosseini",2016,124,8,3.4
+185,Love,"Drama,Romance",Gaspar Noé,"Aomi Muyock, Karl Glusman, Klara Kristin, Juan Saavedra",2015,135,6,0
+186,Billy Lynn's Long Halftime Walk,"Drama,War",Ang Lee,"Joe Alwyn, Garrett Hedlund, Arturo Castro, Mason Lee",2016,113,6.3,1.72
+187,Crimson Peak,"Drama,Fantasy,Horror",Guillermo del Toro,"Mia Wasikowska, Jessica Chastain, Tom Hiddleston, Charlie Hunnam",2015,119,6.6,31.06
+188,Drive,"Crime,Drama",Nicolas Winding Refn,"Ryan Gosling, Carey Mulligan, Bryan Cranston, Albert Brooks",2011,100,7.8,35.05
+189,Trainwreck,"Comedy,Drama,Romance",Judd Apatow,"Amy Schumer, Bill Hader, Brie Larson, Colin Quinn",2015,125,6.3,110.01M
+190,The Light Between Oceans,"Drama,Romance",Derek Cianfrance,"Michael       Fassbender, Alicia Vikander, Rachel Weisz, Florence Clery",2016,133,7.2,12.53
+191,Below Her Mouth,Drama,April Mullen,"Erika Linder, Natalie Krill, Sebastian Pigott, Mayko Nguyen",2016,94,5.6,0
+192,Spotlight,"Crime,Drama,History",Tom McCarthy,"Mark Ruffalo, Michael Keaton, Rachel McAdams, Liev Schreiber",2015,128,8.1,44.99
+193,Morgan,"Horror,Sci-Fi,Thriller",Luke Scott,"Kate Mara, Anya Taylor-Joy, Rose Leslie, Michael Yare",2016,92,5.8,3.91
+194,Warrior,"Action,Drama,Sport",Gavin O'Connor,"Tom Hardy, Nick Nolte, Joel Edgerton, Jennifer Morrison",2011,140,8.2,13.65
+195,Captain America: The First Avenger,"Action,Adventure,Sci-Fi",Joe Johnston,"Chris Evans, Hugo Weaving, Samuel L. Jackson,Hayley Atwell",2011,124,6.9,176.64M
+196,Hacker,"Crime,Drama,Thriller",Akan Satayev,"Callan McAuliffe, Lorraine Nicholson, Daniel Eric Gold, Clifton Collins Jr.",2016,95,6.3,0
+197,Into the Wild,"Adventure,Biography,Drama",Sean Penn,"Emile Hirsch, Vince Vaughn, Catherine Keener, Marcia Gay Harden",2007,148,8.1,18.35
+198,The Imitation Game,"Biography,Drama,Thriller",Morten Tyldum,"Benedict Cumberbatch, Keira Knightley, Matthew Goode, Allen Leech",2014,114,8.1,91.12
+199,Central Intelligence,"Action,Comedy,Crime",Rawson Marshall Thurber,"Dwayne Johnson, Kevin Hart, Danielle Nicolet, Amy Ryan",2016,107,6.3,127.38M
+200,Edge of Tomorrow,"Action,Adventure,Sci-Fi",Doug Liman,"Tom Cruise, Emily Blunt, Bill Paxton, Brendan Gleeson",2014,113,7.9,100.19M
+201,A Cure for Wellness,"Drama,Fantasy,Horror",Gore Verbinski,"Dane DeHaan, Jason Isaacs, Mia Goth, Ivo Nandi",2016,146,6.5,8.1
+202,Snowden,"Biography,Drama,Thriller",Oliver Stone,"Joseph Gordon-Levitt, Shailene Woodley, Melissa Leo,Zachary Quinto",2016,134,7.3,21.48
+203,Iron Man,"Action,Adventure,Sci-Fi",Jon Favreau,"Robert Downey Jr., Gwyneth Paltrow, Terrence Howard, Jeff Bridges",2008,126,7.9,318.3
+204,Allegiant,"Action,Adventure,Mystery",Robert Schwentke,"Shailene Woodley, Theo James, Jeff Daniels,Naomi Watts",2016,120,5.7,66
+205,X: First Class,"Action,Adventure,Sci-Fi",Matthew Vaughn,"James McAvoy, Michael          Fassbender, Jennifer Lawrence, Kevin Bacon",2011,132,7.8,146.41M
+206,Raw (II),"Drama,Horror",Julia Ducournau,"Garance Marillier, Ella Rumpf, Rabah Nait Oufella,Laurent Lucas",2016,99,7.5,0.51
+207,Paterson,"Comedy,Drama,Romance",Jim Jarmusch,"Adam Driver, Golshifteh Farahani, Nellie, Rizwan Manji",2016,118,7.5,2.14
+208,Bridesmaids,"Comedy,Romance",Paul Feig,"Kristen Wiig, Maya Rudolph, Rose Byrne, Terry Crews",2011,125,6.8,169.08M
+209,The Girl with All the Gifts,"Drama,Horror,Thriller",Colm McCarthy,"Gemma Arterton, Glenn Close, Dominique Tipper,Paddy Considine",2016,111,6.7,0
+210,San Andreas,"Action,Adventure,Drama",Brad Peyton,"Dwayne Johnson, Carla Gugino, Alexandra Daddario,Colton Haynes",2015,114,6.1,155.18M
+211,Spring Breakers,Drama,Harmony Korine,"Vanessa Hudgens, Selena Gomez, Ashley Benson,Rachel Korine",2012,94,5.3,14.12
+212,Transformers,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson",2007,144,7.1,318.76
+213,Old Boy,"Action,Drama,Mystery",Spike Lee,"Josh Brolin, Elizabeth Olsen, Samuel L. Jackson, Sharlto Copley",2013,104,5.8,0
+214,Thor: The Dark World,"Action,Adventure,Fantasy",Alan Taylor,"Chris Hemsworth, Natalie Portman, Tom Hiddleston,Stellan Skarsgård",2013,112,7,206.36
+215,Gods of Egypt,"Action,Adventure,Fantasy",Alex Proyas,"Brenton Thwaites, Nikolaj Coster-Waldau, Gerard Butler, Chadwick Boseman",2016,126,5.5,31.14
+216,Captain America: The Winter Soldier,"Action,Adventure,Sci-Fi",Anthony Russo,"Chris Evans, Samuel L. Jackson,Scarlett Johansson, Robert Redford",2014,136,7.8,259.75
+217,Monster Trucks,"Action,Adventure,Comedy",Chris Wedge,"Lucas Till, Jane Levy, Thomas Lennon, Barry Pepper",2016,104,5.7,33.04
+218,A Dark Song,"Drama,Horror",Liam Gavin,"Mark Huberman, Susan Loughnane, Steve Oram,Catherine Walker",2016,100,6.1,0
+219,Kick-Ass,"Action,Comedy",Matthew Vaughn,"Aaron Taylor-Johnson, Nicolas Cage, Chloë Grace Moretz, Garrett M. Brown",2010,117,7.7,48.04
+220,Hardcore Henry,"Action,Adventure,Sci-Fi",Ilya Naishuller,"Sharlto Copley, Tim Roth, Haley Bennett, Danila Kozlovsky",2015,96,6.7,9.24
+221,Cars,"Animation,Adventure,Comedy",John Lasseter,"Owen Wilson, Bonnie Hunt, Paul Newman, Larry the Cable Guy",2006,117,7.1,244.05
+222,It Follows,"Horror,Mystery",David Robert Mitchell,"Maika Monroe, Keir Gilchrist, Olivia Luccardi,Lili Sepe",2014,100,6.9,14.67
+223,The Girl with the Dragon Tattoo,"Crime,Drama,Mystery",David Fincher,"Daniel Craig, Rooney Mara, Christopher Plummer,Stellan Skarsgård",2011,158,7.8,102.52M
+224,We're the Millers,"Comedy,Crime",Rawson Marshall Thurber,"Jason Sudeikis, Jennifer Aniston, Emma Roberts, Ed Helms",2013,110,7,150.37M
+225,American Honey,Drama,Andrea Arnold,"Sasha Lane, Shia LaBeouf, Riley Keough, McCaul Lombardi",2016,163,7,0.66
+226,The Lobster,"Comedy,Drama,Romance",Yorgos Lanthimos,"Colin Farrell, Rachel Weisz, Jessica Barden,Olivia Colman",2015,119,7.1,8.7
+227,Predators,"Action,Adventure,Sci-Fi",Nimród Antal,"Adrien Brody, Laurence Fishburne, Topher Grace,Alice Braga",2010,107,6.4,52
+228,Maleficent,"Action,Adventure,Family",Robert Stromberg,"Angelina Jolie, Elle Fanning, Sharlto Copley,Lesley Manville",2014,97,7,241.41
+229,Rupture,"Horror,Sci-Fi,Thriller",Steven Shainberg,"Noomi Rapace, Michael Chiklis, Kerry Bishé,Peter Stormare",2016,102,4.8,0
+230,Pan's Labyrinth,"Drama,Fantasy,War",Guillermo del Toro,"Ivana Baquero, Ariadna Gil, Sergi López,Maribel Verdú",2006,118,8.2,37.62
+231,A Kind of Murder,"Crime,Drama,Thriller",Andy Goddard,"Patrick Wilson, Jessica Biel, Haley Bennett, Vincent Kartheiser",2016,95,5.2,0
+232,Apocalypto,"Action,Adventure,Drama",Mel Gibson,"Gerardo Taracena, Raoul Max Trujillo, Dalia Hernández,Rudy Youngblood",2006,139,7.8,50.86
+233,Mission: Impossible - Rogue Nation,"Action,Adventure,Thriller",Christopher McQuarrie,"Tom Cruise, Rebecca Ferguson, Jeremy Renner, Simon Pegg",2015,131,7.4,195M
+234,The Huntsman: Winter's War,"Action,Adventure,Drama",Cedric Nicolas-Troyan,"Chris Hemsworth, Jessica Chastain, Charlize Theron, Emily Blunt",2016,114,6.1,47.95
+235,The Perks of Being a Wallflower,"Drama,Romance",Stephen Chbosky,"Logan Lerman, Emma Watson, Ezra Miller, Paul Rudd",2012,102,8,17.74
+236,Jackie,"Biography,Drama,History",Pablo Larraín,"Natalie Portman, Peter Sarsgaard, Greta Gerwig,Billy Crudup",2016,100,6.8,13.96
+237,The Disappointments Room,"Drama,Horror,Thriller",D.J. Caruso,"Kate Beckinsale, Mel Raido, Duncan Joiner, Lucas Till",2016,85,3.9,2.41
+238,The Grand Budapest Hotel,"Adventure,Comedy,Drama",Wes Anderson,"Ralph Fiennes, F. Murray Abraham, Mathieu Amalric,Adrien Brody",2014,99,8.1,59.07
+239,The Host ,"Action,Adventure,Romance",Andrew Niccol,"Saoirse Ronan, Max Irons, Jake Abel, Diane Kruger",2013,125,5.9,26.62
+240,Fury,"Action,Drama,War",David Ayer,"Brad Pitt, Shia LaBeouf, Logan Lerman, Michael Peña",2014,134,7.6,85.71
+241,Inside Out,"Animation,Adventure,Comedy",Pete Docter,"Amy Poehler, Bill Hader, Lewis Black, Mindy Kaling",2015,95,8.2,356.45
+242,Rock Dog,"Animation,Adventure,Comedy",Ash Brannon,"Luke Wilson, Eddie Izzard, J.K. Simmons, Lewis Black",2016,90,5.8,9.4
+243,Terminator Genisys,"Action,Adventure,Sci-Fi",Alan Taylor,"Arnold Schwarzenegger, Jason Clarke, Emilia Clarke,Jai Courtney",2015,126,6.5,89.73
+244,Percy Jackson & the Olympians: The Lightning Thief,"Adventure,Family,Fantasy",Chris Columbus,"Logan Lerman, Kevin McKidd, Steve Coogan,Brandon T. Jackson",2010,118,5.9,88.76
+245,Les Misérables,"Drama,Musical,Romance",Tom Hooper,"Hugh Jackman, Russell Crowe, Anne Hathaway,Amanda Seyfried",2012,158,7.6,148.78M
+246,Children of Men,"Drama,Sci-Fi,Thriller",Alfonso Cuarón,"Julianne Moore, Clive Owen, Chiwetel Ejiofor,Michael Caine",2006,109,7.9,35.29
+247,20th Century Women,"Comedy,Drama",Mike Mills,"Annette Bening, Elle Fanning, Greta Gerwig, Billy Crudup",2016,119,7.4,5.66
+248,Spy,"Action,Comedy,Crime",Paul Feig,"Melissa McCarthy, Rose Byrne, Jude Law, Jason Statham",2015,119,7.1,110.82M
+249,The Intouchables,"Biography,Comedy,Drama",Olivier Nakache,"François Cluzet, Omar Sy, Anne Le Ny, Audrey Fleurot",2011,112,8.6,13.18
+250,Bonjour Anne,"Comedy,Drama,Romance",Eleanor Coppola,"Diane Lane, Alec Baldwin, Arnaud Viard, Linda Gegusch",2016,92,4.9,0.32
+251,Kynodontas,"Drama,Thriller",Yorgos Lanthimos,"Christos Stergioglou, Michele Valley, Angeliki Papoulia, Hristos Passalis",2009,94,7.3,0.11
+252,Straight Outta Compton,"Biography,Drama,History",F. Gary Gray,"O'Shea Jackson Jr., Corey Hawkins, Jason Mitchell,Neil Brown Jr.",2015,147,7.9,161.03M
+253,The Amazing Spider-Man 2,"Action,Adventure,Sci-Fi",Marc Webb,"Andrew Garfield, Emma Stone, Jamie Foxx, Paul Giamatti",2014,142,6.7,202.85
+254,The Conjuring,"Horror,Mystery,Thriller",James Wan,"Patrick Wilson, Vera Farmiga, Ron Livingston, Lili Taylor",2013,112,7.5,137.39M
+255,The Hangover,Comedy,Todd Phillips,"Zach Galifianakis, Bradley Cooper, Justin Bartha, Ed Helms",2009,100,7.8,277.31
+256,Battleship,"Action,Adventure,Sci-Fi",Peter Berg,"Alexander Skarsgård, Brooklyn Decker, Liam Neeson,Rihanna",2012,131,5.8,65.17
+257,Rise of the Planet of the Apes,"Action,Drama,Sci-Fi",Rupert Wyatt,"James Franco, Andy Serkis, Freida Pinto, Karin Konoval",2011,105,7.6,176.74M
+258,Lights Out,Horror,David F. Sandberg,"Teresa Palmer, Gabriel Bateman, Maria Bello,Billy Burke",2016,81,6.4,67.24
+259,Norman: The Moderate Rise and Tragic Fall of a New York Fixer,"Drama,Thriller",Joseph Cedar,"Richard Gere, Lior Ashkenazi, Michael Sheen,Charlotte Gainsbourg",2016,118,7.1,2.27
+260,Birdman or (The Unexpected Virtue of Ignorance),"Comedy,Drama,Romance",Alejandro González Iñárritu,"Michael Keaton, Zach Galifianakis,Edward Norton, Andrea Riseborough",2014,119,7.8,42.34
+261,Black Swan,"Drama,Thriller",Darren Aronofsky,"Natalie Portman, Mila Kunis, Vincent Cassel,Winona Ryder",2010,108,8,106.95M
+262,Dear White People,"Comedy,Drama",Justin Simien,"Tyler James Williams, Tessa Thompson, Kyle Gallner,Teyonah Parris",2014,108,6.2,4.4
+263,Nymphomaniac: Vol. I,Drama,Lars von Trier,"Charlotte Gainsbourg, Stellan Skarsgård, Stacy Martin, Shia LaBeouf",2013,117,7,0.79
+264,Teenage Mutant Ninja Turtles: Out of the Shadows,"Action,Adventure,Comedy",Dave Green,"Megan Fox, Will Arnett, Tyler Perry, Laura Linney",2016,112,6,0.54
+265,Knock Knock,"Drama,Horror,Thriller",Eli Roth,"Keanu Reeves, Lorenza Izzo, Ana de Armas, Aaron Burns",2015,99,4.9,0.03
+266,Dirty Grandpa,Comedy,Dan Mazer,"Robert De Niro, Zac Efron, Zoey Deutch, Aubrey Plaza",2016,102,6,35.54
+267,Cloud Atlas,"Drama,Sci-Fi",Tom Tykwer,"Tom Hanks, Halle Berry, Hugh Grant, Hugo Weaving",2012,172,7.5,27.1
+268,X-Men Origins: Wolverine,"Action,Adventure,Sci-Fi",Gavin Hood,"Hugh Jackman, Liev Schreiber, Ryan Reynolds, Danny Huston",2009,107,6.7,179.88M
+269,Satanic,Horror,Jeffrey G. Hunt,"Sarah Hyland, Steven Krueger, Justin Chon, Clara Mamet",2016,85,3.7,0
+270,Skyfall,"Action,Adventure,Thriller",Sam Mendes,"Daniel Craig, Javier Bardem, Naomie Harris, Judi Dench",2012,143,7.8,304.36
+271,The Hobbit: An Unexpected Journey,"Adventure,Fantasy",Peter Jackson,"Martin Freeman, Ian McKellen, Richard Armitage,Andy Serkis",2012,169,7.9,303
+272,21 Jump Street,"Action,Comedy,Crime",Phil Lord,"Jonah Hill, Channing Tatum, Ice Cube,Brie Larson",2012,110,7.2,138.45M
+273,Sing Street,"Comedy,Drama,Music",John Carney,"Ferdia Walsh-Peelo, Aidan Gillen, Maria Doyle Kennedy, Jack Reynor",2016,106,8,3.23
+274,Ballerina,"Animation,Adventure,Comedy",Eric Summer,"Elle Fanning, Dane DeHaan, Carly Rae Jepsen, Maddie Ziegler",2016,89,6.8,0
+275,Oblivion,"Action,Adventure,Mystery",Joseph Kosinski,"Tom Cruise, Morgan Freeman, Andrea Riseborough, Olga Kurylenko",2013,124,7,89.02
+276,22 Jump Street,"Action,Comedy,Crime",Phil Lord,"Channing Tatum, Jonah Hill, Ice Cube,Nick Offerman",2014,112,7.1,191.62M
+277,Zodiac,"Crime,Drama,History",David Fincher,"Jake Gyllenhaal, Robert Downey Jr., Mark Ruffalo,Anthony Edwards",2007,157,7.7,33.05
+278,Everybody Wants Some!!,Comedy,Richard Linklater,"Blake Jenner, Tyler Hoechlin, Ryan Guzman,Zoey Deutch",2016,117,7,3.37
+279,Iron Man Three,"Action,Adventure,Sci-Fi",Shane Black,"Robert Downey Jr., Guy Pearce, Gwyneth Paltrow,Don Cheadle",2013,130,7.2,408.99
+280,Now You See Me,"Crime,Mystery,Thriller",Louis Leterrier,"Jesse Eisenberg, Common, Mark Ruffalo, Woody Harrelson",2013,115,7.3,117.7M
+281,Sherlock Holmes,"Action,Adventure,Crime",Guy Ritchie,"Robert Downey Jr., Jude Law, Rachel McAdams, Mark Strong",2009,128,7.6,209.02
+282,Death Proof,Thriller,Quentin Tarantino,"Kurt Russell, Zoë Bell, Rosario Dawson, Vanessa Ferlito",2007,113,7.1,0
+283,The Danish Girl,"Biography,Drama,Romance",Tom Hooper,"Eddie Redmayne, Alicia Vikander, Amber Heard, Ben Whishaw",2015,119,7,12.71
+284,Hercules,"Action,Adventure",Brett Ratner,"Dwayne Johnson, John Hurt, Ian McShane, Joseph Fiennes",2014,98,6,72.66
+285,Sucker Punch,"Action,Fantasy",Zack Snyder,"Emily Browning, Vanessa Hudgens, Abbie Cornish,Jena Malone",2011,110,6.1,36.38
+286,Keeping Up with the Joneses,"Action,Comedy",Greg Mottola,"Zach Galifianakis, Isla Fisher, Jon Hamm, Gal Gadot",2016,105,5.8,14.9
+287,Jupiter Ascending,"Action,Adventure,Sci-Fi",Lana Wachowski,"Channing Tatum, Mila Kunis,Eddie Redmayne, Sean Bean",2015,127,5.3,47.38
+288,Masterminds,"Action,Comedy,Crime",Jared Hess,"Zach Galifianakis, Kristen Wiig, Owen Wilson, Ross Kimball",2016,95,5.8,17.36
+289,Iris,Thriller,Jalil Lespert,"Romain Duris, Charlotte Le Bon, Jalil Lespert, Camille Cottin",2016,99,6.1,0
+290,Busanhaeng,"Action,Drama,Horror",Sang-ho Yeon,"Yoo Gong, Soo-an Kim, Yu-mi Jung, Dong-seok Ma",2016,118,7.5,2.13
+291,Pitch Perfect,"Comedy,Music,Romance",Jason Moore,"Anna Kendrick, Brittany Snow, Rebel Wilson, Anna Camp",2012,112,7.2,65
+292,Neighbors 2: Sorority Rising,Comedy,Nicholas Stoller,"Seth Rogen, Rose Byrne, Zac Efron, Chloë Grace Moretz",2016,92,5.7,55.29
+293,The Exception,Drama,David Leveaux,"Lily James, Jai Courtney, Christopher Plummer, Loïs van Wijk",2016,107,7.7,0
+294,Man of Steel,"Action,Adventure,Fantasy",Zack Snyder,"Henry Cavill, Amy Adams, Michael Shannon, Diane Lane",2013,143,7.1,291.02
+295,The Choice,"Drama,Romance",Ross Katz,"Benjamin Walker, Teresa Palmer, Alexandra Daddario,Maggie Grace",2016,111,6.6,18.71
+296,Ice Age: Collision Course,"Animation,Adventure,Comedy",Mike Thurmeier,"Ray Romano, Denis Leary, John Leguizamo, Chris Wedge",2016,94,5.7,64.06
+297,The Devil Wears Prada,"Comedy,Drama",David Frankel,"Anne Hathaway, Meryl Streep, Adrian Grenier, Emily Blunt",2006,109,6.8,124.73M
+298,The Infiltrator,"Biography,Crime,Drama",Brad Furman,"Bryan Cranston, John Leguizamo, Diane Kruger, Amy Ryan",2016,127,7.1,15.43
+299,There Will Be Blood,"Drama,History",Paul Thomas Anderson,"Daniel Day-Lewis, Paul Dano, Ciarán Hinds,Martin Stringer",2007,158,8.1,40.22
+300,The Equalizer,"Action,Crime,Thriller",Antoine Fuqua,"Denzel                      Washington, Marton Csokas, Chloë Grace Moretz, David Harbour",2014,132,7.2,101.53M
+301,Lone Survivor,"Action,Biography,Drama",Peter Berg,"Mark Wahlberg, Taylor Kitsch, Emile Hirsch, Ben Foster",2013,121,7.5,125.07M
+302,The Cabin in the Woods,Horror,Drew Goddard,"Kristen Connolly, Chris Hemsworth, Anna Hutchison,Fran Kranz",2012,95,7,42.04
+303,The House Bunny,"Comedy,Romance",Fred Wolf,"Anna Faris, Colin Hanks, Emma Stone, Kat Dennings",2008,97,5.5,48.24
+304,She's Out of My League,"Comedy,Romance",Jim Field Smith,"Jay Baruchel, Alice Eve, T.J. Miller, Mike Vogel",2010,104,6.4,31.58
+305,Inherent Vice,"Comedy,Crime,Drama",Paul Thomas Anderson,"Joaquin Phoenix, Josh Brolin, Owen Wilson,Katherine Waterston",2014,148,6.7,8.09
+306,Alice Through the Looking Glass,"Adventure,Family,Fantasy",James Bobin,"Mia Wasikowska, Johnny Depp, Helena Bonham Carter, Anne Hathaway",2016,113,6.2,77.04
+307,Vincent N Roxxy,"Crime,Drama,Thriller",Gary Michael Schultz,"Emile Hirsch, Zoë Kravitz, Zoey Deutch,Emory Cohen",2016,110,5.5,0
+308,The Fast and the Furious: Tokyo Drift,"Action,Crime,Thriller",Justin Lin,"Lucas Black, Zachery Ty Bryan, Shad Moss, Damien Marzette",2006,104,6,62.49
+309,How to Be Single,"Comedy,Romance",Christian Ditter,"Dakota Johnson, Rebel Wilson, Leslie Mann, Alison Brie",2016,110,6.1,46.81
+310,The Blind Side,"Biography,Drama,Sport",John Lee Hancock,"Quinton Aaron, Sandra Bullock, Tim McGraw,Jae Head",2009,129,7.7,255.95
+311,La vie d'Adèle,"Drama,Romance",Abdellatif Kechiche,"Léa Seydoux, Adèle Exarchopoulos, Salim Kechiouche, Aurélien Recoing",2013,180,7.8,2.2
+312,The Babadook,"Drama,Horror",Jennifer Kent,"Essie Davis, Noah Wiseman, Daniel Henshall, Hayley McElhinney",2014,93,6.8,0.92
+313,The Hobbit: The Battle of the Five Armies,"Adventure,Fantasy",Peter Jackson,"Ian McKellen, Martin Freeman, Richard Armitage,Cate Blanchett",2014,144,7.4,255.11
+314,Harry Potter and the Order of the Phoenix,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Brendan Gleeson",2007,138,7.5,292
+315,Snowpiercer,"Action,Drama,Sci-Fi",Bong Joon Ho,"Chris Evans, Jamie Bell, Tilda Swinton, Ed Harris",2013,126,7,4.56
+316,The 5th Wave,"Action,Adventure,Sci-Fi",J Blakeson,"Chloë Grace Moretz, Matthew Zuk, Gabriela Lopez,Bailey Anne Borders",2016,112,5.2,34.91
+317,The Stakelander,"Action,Horror",Dan Berk,"Connor Paolo, Nick Damici, Laura Abramsen, A.C. Peterson",2016,81,5.3,0
+318,The Visit,"Comedy,Horror,Thriller",M. Night Shyamalan,"Olivia DeJonge, Ed Oxenbould, Deanna Dunagan, Peter McRobbie",2015,94,6.2,65.07
+319,Fast Five,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Dwayne Johnson, Jordana Brewster",2011,131,7.3,209.81
+320,Step Up,"Crime,Drama,Music",Anne Fletcher,"Channing Tatum, Jenna Dewan Tatum, Damaine Radcliff, De'Shawn Washington",2006,104,6.5,65.27
+321,Lovesong,Drama,So Yong Kim,"Riley Keough, Jena Malone, Jessie Ok Gray, Cary Joji Fukunaga",2016,84,6.4,0.01
+322,RocknRolla,"Action,Crime,Thriller",Guy Ritchie,"Gerard Butler, Tom Wilkinson, Idris Elba, Thandie Newton",2008,114,7.3,5.69
+323,In Time,"Action,Sci-Fi,Thriller",Andrew Niccol,"Justin Timberlake, Amanda Seyfried, Cillian Murphy,Olivia Wilde",2011,109,6.7,37.55
+324,The Social Network,"Biography,Drama",David Fincher,"Jesse Eisenberg, Andrew Garfield, Justin Timberlake,Rooney Mara",2010,120,7.7,96.92
+325,The Last Witch Hunter,"Action,Adventure,Fantasy",Breck Eisner,"Vin Diesel, Rose Leslie, Elijah Wood, Ólafur Darri Ólafsson",2015,106,6,27.36
+326,Victor Frankenstein,"Drama,Horror,Sci-Fi",Paul McGuigan,"Daniel Radcliffe, James McAvoy, Jessica Brown Findlay, Andrew Scott",2015,110,6,5.77
+327,A Street Cat Named Bob,"Biography,Comedy,Drama",Roger Spottiswoode,"Luke Treadaway, Bob the Cat, Ruta Gedmintas, Joanne Froggatt",2016,103,7.4,0.04
+328,Green Room,"Crime,Horror,Thriller",Jeremy Saulnier,"Anton Yelchin, Imogen Poots, Alia Shawkat,Patrick Stewart",2015,95,7,3.22
+329,Blackhat,"Crime,Drama,Mystery",Michael Mann,"Chris Hemsworth, Viola Davis, Wei Tang, Leehom Wang",2015,133,5.4,7.1
+330,Storks,"Animation,Adventure,Comedy",Nicholas Stoller,"Andy Samberg, Katie Crown,Kelsey Grammer, Jennifer Aniston",2016,87,6.9,72.66
+331,American Sniper,"Action,Biography,Drama",Clint Eastwood,"Bradley Cooper, Sienna Miller, Kyle Gallner, Cole Konis",2014,133,7.3,350.12
+332,Dallas Buyers Club,"Biography,Drama",Jean-Marc Vallée,"Matthew McConaughey, Jennifer Garner, Jared Leto, Steve Zahn",2013,117,8,27.3
+333,Lincoln,"Biography,Drama,History",Steven Spielberg,"Daniel Day-Lewis, Sally Field, David Strathairn,Joseph Gordon-Levitt",2012,150,7.4,182.2M
+334,Rush,"Action,Biography,Drama",Ron Howard,"Daniel Brühl, Chris Hemsworth, Olivia Wilde,Alexandra Maria Lara",2013,123,8.1,26.9
+335,Before I Wake,"Drama,Fantasy,Horror",Mike Flanagan,"Kate Bosworth, Thomas Jane, Jacob Tremblay,Annabeth Gish",2016,97,6.1,0
+336,Silver Linings Playbook,"Comedy,Drama,Romance",David O. Russell,"Bradley Cooper, Jennifer Lawrence, Robert De Niro, Jacki Weaver",2012,122,7.8,132.09M
+337,Tracktown,"Drama,Sport",Alexi Pappas,"Alexi Pappas, Chase Offerle, Rachel Dratch, Andy Buckley",2016,88,5.9,0
+338,The Fault in Our Stars,"Drama,Romance",Josh Boone,"Shailene Woodley, Ansel Elgort, Nat Wolff, Laura Dern",2014,126,7.8,124.87M
+339,Blended,"Comedy,Romance",Frank Coraci,"Adam Sandler, Drew Barrymore, Wendi McLendon-Covey, Kevin Nealon",2014,117,6.5,46.28
+340,Fast & Furious,"Action,Crime,Thriller",Justin Lin,"Vin Diesel, Paul Walker, Michelle Rodriguez, Jordana Brewster",2009,107,6.6,155.02M
+341,Looper,"Action,Crime,Drama",Rian Johnson,"Joseph Gordon-Levitt, Bruce Willis, Emily Blunt, Paul Dano",2012,119,7.4,66.47
+342,White House Down,"Action,Drama,Thriller",Roland Emmerich,"Channing Tatum, Jamie Foxx, Maggie Gyllenhaal,Jason Clarke",2013,131,6.4,73.1
+343,Pete's Dragon,"Adventure,Family,Fantasy",David Lowery,"Bryce Dallas Howard, Robert Redford, Oakes Fegley,Oona Laurence",2016,102,6.8,76.2
+344,Spider-Man 3,"Action,Adventure",Sam Raimi,"Tobey Maguire, Kirsten Dunst, Topher Grace, Thomas Haden Church",2007,139,6.2,336.53
+345,The Three Musketeers,"Action,Adventure,Romance",Paul W.S. Anderson,"Logan Lerman, Matthew Macfadyen, Ray Stevenson, Milla Jovovich",2011,110,5.8,20.32
+346,Stardust,"Adventure,Family,Fantasy",Matthew Vaughn,"Charlie Cox, Claire Danes, Sienna Miller, Ian McKellen",2007,127,7.7,38.35
+347,American Hustle,"Crime,Drama",David O. Russell,"Christian Bale, Amy Adams, Bradley Cooper,Jennifer Lawrence",2013,138,7.3,150.12M
+348,Jennifer's Body,"Comedy,Horror",Karyn Kusama,"Megan Fox, Amanda Seyfried, Adam Brody, Johnny Simmons",2009,102,5.1,16.2
+349,Midnight in Paris,"Comedy,Fantasy,Romance",Woody Allen,"Owen Wilson, Rachel McAdams, Kathy Bates, Kurt Fuller",2011,94,7.7,56.82
+350,Lady Macbeth,Drama,William Oldroyd,"Florence Pugh, Christopher Fairbank, Cosmo Jarvis, Naomi Ackie",2016,89,7.3,0
+351,Joy,Drama,David O. Russell,"Jennifer Lawrence, Robert De Niro, Bradley Cooper, Edgar Ramírez",2015,124,6.6,56.44
+352,The Dressmaker,"Comedy,Drama",Jocelyn Moorhouse,"Kate Winslet, Judy Davis, Liam Hemsworth,Hugo Weaving",2015,119,7.1,2.02
+353,Café Society,"Comedy,Drama,Romance",Woody Allen,"Jesse Eisenberg, Kristen Stewart, Steve Carell, Blake Lively",2016,96,6.7,11.08
+354,Insurgent,"Adventure,Sci-Fi,Thriller",Robert Schwentke,"Shailene Woodley, Ansel Elgort, Theo James,Kate Winslet",2015,119,6.3,130M
+355,Seventh Son,"Action,Adventure,Fantasy",Sergei Bodrov,"Ben Barnes, Julianne Moore, Jeff Bridges, Alicia Vikander",2014,102,5.5,17.18
+356,The Theory of Everything,"Biography,Drama,Romance",James Marsh,"Eddie Redmayne, Felicity Jones, Tom Prior, Sophie Perry",2014,123,7.7,35.89
+357,This Is the End,"Comedy,Fantasy",Evan Goldberg,"James Franco, Jonah Hill, Seth Rogen,Jay Baruchel",2013,107,6.6,101.47M
+358,About Time,"Comedy,Drama,Fantasy",Richard Curtis,"Domhnall Gleeson, Rachel McAdams, Bill Nighy,Lydia Wilson",2013,123,7.8,15.29
+359,Step Brothers,Comedy,Adam McKay,"Will Ferrell, John C. Reilly, Mary Steenburgen,Richard Jenkins",2008,98,6.9,100.47M
+360,Clown,"Horror,Thriller",Jon Watts,"Andy Powers, Laura Allen, Peter Stormare, Christian Distefano",2014,100,5.7,0.05
+361,Star Trek Into Darkness,"Action,Adventure,Sci-Fi",J.J. Abrams,"Chris Pine, Zachary Quinto, Zoe Saldana, Benedict Cumberbatch",2013,132,7.8,228.76
+362,Zombieland,"Adventure,Comedy,Horror",Ruben Fleischer,"Jesse Eisenberg, Emma Stone, Woody Harrelson,Abigail Breslin",2009,88,7.7,75.59
+363,"Hail, Caesar!","Comedy,Mystery",Ethan Coen,"Josh Brolin, George Clooney, Alden Ehrenreich, Ralph Fiennes",2016,106,6.3,30
+364,Slumdog Millionaire,Drama,Danny Boyle,"Dev Patel, Freida Pinto, Saurabh Shukla, Anil Kapoor",2008,120,8,141.32M
+365,The Twilight Saga: Breaking Dawn - Part 2,"Adventure,Drama,Fantasy",Bill Condon,"Kristen Stewart, Robert Pattinson, Taylor Lautner, Peter Facinelli",2012,115,5.5,292.3
+366,American Wrestler: The Wizard,"Drama,Sport",Alex Ranarivelo,"William Fichtner, Jon Voight, Lia Marie Johnson,Gabriel Basso",2016,117,6.9,0
+367,The Amazing Spider-Man,"Action,Adventure",Marc Webb,"Andrew Garfield, Emma Stone, Rhys Ifans, Irrfan Khan",2012,136,7,262.03
+368,Ben-Hur,"Action,Adventure,Drama",Timur Bekmambetov,"Jack Huston, Toby Kebbell, Rodrigo Santoro,Nazanin Boniadi",2016,123,5.7,26.38
+369,Sleight,"Action,Drama,Sci-Fi",J.D. Dillard,"Jacob Latimore, Seychelle Gabriel, Dulé Hill, Storm Reid",2016,89,6,3.85
+370,The Maze Runner,"Action,Mystery,Sci-Fi",Wes Ball,"Dylan O'Brien, Kaya Scodelario, Will Poulter, Thomas Brodie-Sangster",2014,113,6.8,102.41M
+371,Criminal,"Action,Crime,Drama",Ariel Vromen,"Kevin Costner, Ryan Reynolds, Gal Gadot, Gary Oldman",2016,113,6.3,14.27
+372,Wanted,"Action,Crime,Fantasy",Timur Bekmambetov,"Angelina Jolie, James McAvoy, Morgan Freeman, Terence Stamp",2008,110,6.7,134.57M
+373,Florence Foster Jenkins,"Biography,Comedy,Drama",Stephen Frears,"Meryl Streep, Hugh Grant, Simon Helberg, Rebecca Ferguson",2016,111,6.9,27.37
+374,Collide,"Action,Crime,Thriller",Eran Creevy,"Nicholas Hoult, Felicity Jones, Anthony Hopkins, Ben Kingsley",2016,99,5.7,2.2
+375,Black Mass,"Biography,Crime,Drama",Scott Cooper,"Johnny Depp, Benedict Cumberbatch, Dakota Johnson, Joel Edgerton",2015,123,6.9,62.56
+376,Creed,"Drama,Sport",Ryan Coogler,"Michael B. Jordan, Sylvester Stallone, Tessa Thompson, Phylicia Rashad",2015,133,7.6,109.71M
+377,Swiss Army Man,"Adventure,Comedy,Drama",Dan Kwan,"Paul Dano, Daniel Radcliffe, Mary Elizabeth Winstead, Antonia Ribero",2016,97,7.1,4.21
+378,The Expendables 3,"Action,Adventure,Thriller",Patrick Hughes,"Sylvester Stallone, Jason Statham, Jet Li, Antonio Banderas",2014,126,6.1,39.29
+379,What We Do in the Shadows,"Comedy,Fantasy,Horror",Jemaine Clement,"Jemaine Clement, Taika Waititi,Cori Gonzalez-Macuer, Jonny Brugh",2014,86,7.6,3.33
+380,Southpaw,"Drama,Sport",Antoine Fuqua,"Jake Gyllenhaal, Rachel McAdams, Oona Laurence,Forest Whitaker",2015,124,7.4,52.42
+381,Hush,"Horror,Thriller",Mike Flanagan,"John Gallagher Jr., Kate Siegel, Michael Trucco,Samantha Sloyan",2016,81,6.6,0
+382,Bridge of Spies,"Drama,History,Thriller",Steven Spielberg,"Tom Hanks, Mark Rylance, Alan Alda, Amy Ryan",2015,142,7.6,72.31
+383,The Lego Movie,"Animation,Action,Adventure",Phil Lord,"Chris Pratt, Will Ferrell, Elizabeth Banks, Will Arnett",2014,100,7.8,257.76
+384,Everest,"Action,Adventure,Drama",Baltasar Kormákur,"Jason Clarke, Ang Phula Sherpa, Thomas M. Wright, Martin Henderson",2015,121,7.1,43.25
+385,Pixels,"Action,Comedy,Family",Chris Columbus,"Adam Sandler, Kevin James, Michelle Monaghan,Peter Dinklage",2015,105,5.6,78.75
+386,Robin Hood,"Action,Adventure,Drama",Ridley Scott,"Russell Crowe, Cate Blanchett, Matthew Macfadyen,Max von Sydow",2010,140,6.7,105.22M
+387,The Wolverine,"Action,Adventure,Sci-Fi",James Mangold,"Hugh Jackman, Will Yun Lee, Tao Okamoto, Rila Fukushima",2013,126,6.7,132.55M
+388,John Carter,"Action,Adventure,Sci-Fi",Andrew Stanton,"Taylor Kitsch, Lynn Collins, Willem Dafoe,Samantha Morton",2012,132,6.6,73.06
+389,Keanu,"Action,Comedy",Peter Atencio,"Keegan-Michael Key, Jordan Peele, Tiffany Haddish,Method Man",2016,100,6.3,20.57
+390,The Gunman,"Action,Crime,Drama",Pierre Morel,"Sean Penn, Idris Elba, Jasmine Trinca, Javier Bardem",2015,115,5.8,10.64
+391,Steve Jobs,"Biography,Drama",Danny Boyle,"Michael   Fassbender, Kate Winslet, Seth Rogen, Jeff Daniels",2015,122,7.2,17.75
+392,Whisky Galore,"Comedy,Romance",Gillies MacKinnon,"Tim Pigott-Smith, Naomi Battrick, Ellie Kendrick,James Cosmo",2016,98,5,0
+393,Grown Ups 2,Comedy,Dennis Dugan,"Adam Sandler, Kevin James, Chris Rock, David Spade",2013,101,5.4,133.67M
+394,The Age of Adaline,"Drama,Fantasy,Romance",Lee Toland Krieger,"Blake Lively, Michiel Huisman, Harrison Ford,Kathy Baker",2015,112,7.2,42.48
+395,The Incredible Hulk,"Action,Adventure,Sci-Fi",Louis Leterrier,"Edward Norton, Liv Tyler, Tim Roth, William Hurt",2008,112,6.8,134.52M
+396,Couples Retreat,Comedy,Peter Billingsley,"Vince Vaughn, Malin Akerman, Jon Favreau, Jason Bateman",2009,113,5.5,109.18M
+397,Absolutely Anything,"Comedy,Sci-Fi",Terry Jones,"Simon Pegg, Kate Beckinsale, Sanjeev Bhaskar, Rob Riggle",2015,85,6,0
+398,Magic Mike,"Comedy,Drama",Steven Soderbergh,"Channing Tatum, Alex Pettyfer, Olivia Munn,Matthew McConaughey",2012,110,6.1,113.71M
+399,Minions,"Animation,Action,Adventure",Kyle Balda,"Sandra Bullock, Jon Hamm, Michael Keaton, Pierre Coffin",2015,91,6.4,336.03
+400,The Black Room,Horror,Rolfe Kanefsky,"Natasha Henstridge, Lukas Hassel, Lin Shaye,Dominique Swain",2016,91,3.9,0
+401,Bronson,"Action,Biography,Crime",Nicolas Winding Refn,"Tom Hardy, Kelly Adams, Luing Andrews,Katy Barker",2008,92,7.1,0.1
+402,Despicable Me,"Animation,Adventure,Comedy",Pierre Coffin,"Steve Carell, Jason Segel, Russell Brand, Julie Andrews",2010,95,7.7,251.5
+403,The Best of Me,"Drama,Romance",Michael Hoffman,"James Marsden, Michelle Monaghan, Luke Bracey,Liana Liberato",2014,118,6.7,26.76
+404,The Invitation,"Drama,Mystery,Thriller",Karyn Kusama,"Logan Marshall-Green, Emayatzy Corinealdi, Michiel Huisman, Tammy Blanchard",2015,100,6.7,0.23
+405,Zero Dark Thirty,"Drama,History,Thriller",Kathryn Bigelow,"Jessica Chastain, Joel Edgerton, Chris Pratt, Mark Strong",2012,157,7.4,95.72
+406,Tangled,"Animation,Adventure,Comedy",Nathan Greno,"Mandy Moore, Zachary Levi, Donna Murphy, Ron Perlman",2010,100,7.8,200.81
+407,The Hunger Games: Mockingjay - Part 2,"Action,Adventure,Sci-Fi",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson",2015,137,6.6,281.67
+408,Vacation,"Adventure,Comedy",John Francis Daley,"Ed Helms, Christina Applegate, Skyler Gisondo, Steele Stebbins",2015,99,6.1,58.88
+409,Taken,"Action,Thriller",Pierre Morel,"Liam Neeson, Maggie Grace, Famke Janssen, Leland Orser",2008,93,7.8,145M
+410,Pitch Perfect 2,"Comedy,Music",Elizabeth Banks,"Anna Kendrick, Rebel Wilson, Hailee Steinfeld,Brittany Snow",2015,115,6.5,183.44M
+411,Monsters University,"Animation,Adventure,Comedy",Dan Scanlon,"Billy Crystal, John Goodman, Steve Buscemi, Helen Mirren",2013,104,7.3,268.49
+412,Elle,"Crime,Drama,Thriller",Paul Verhoeven,"Isabelle Huppert, Laurent Lafitte, Anne Consigny,Charles Berling",2016,130,7.2,0
+413,Mechanic: Resurrection,"Action,Adventure,Crime",Dennis Gansel,"Jason Statham, Jessica Alba, Tommy Lee Jones,Michelle Yeoh",2016,98,5.6,21.2
+414,Tusk,"Comedy,Drama,Horror",Kevin Smith,"Justin Long, Michael Parks, Haley Joel Osment,Genesis Rodriguez",2014,102,5.4,1.82
+415,The Headhunter's Calling,Drama,Mark Williams,"Alison Brie, Gerard Butler, Willem Dafoe, Gretchen Mol",2016,108,6.9,0
+416,Atonement,"Drama,Mystery,Romance",Joe Wright,"Keira Knightley, James McAvoy, Brenda Blethyn,Saoirse Ronan",2007,123,7.8,50.92
+417,Harry Potter and the Deathly Hallows: Part 1,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Bill Nighy",2010,146,7.7,294.98
+418,Shame,Drama,Steve McQueen,"Michael        Fassbender, Carey Mulligan, James Badge Dale, Lucy Walters",2011,101,7.2,4
+419,Hanna,"Action,Drama,Thriller",Joe Wright,"Saoirse Ronan, Cate Blanchett, Eric Bana, Vicky Krieps",2011,111,6.8,40.25
+420,The Babysitters,Drama,David Ross,"Lauren Birkell, Paul Borghese, Chira Cassel, Anthony Cirillo",2007,88,5.7,0.04
+421,Pride and Prejudice and Zombies,"Action,Horror,Romance",Burr Steers,"Lily James, Sam Riley, Jack Huston, Bella Heathcote",2016,108,5.8,10.91
+422,300: Rise of an Empire,"Action,Drama,Fantasy",Noam Murro,"Sullivan Stapleton, Eva Green, Lena Headey, Hans Matheson",2014,102,6.2,106.37M
+423,London Has Fallen,"Action,Crime,Drama",Babak Najafi,"Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett",2016,99,5.9,62.4
+424,The Curious Case of Benjamin Button,"Drama,Fantasy,Romance",David Fincher,"Brad Pitt, Cate Blanchett, Tilda Swinton, Julia Ormond",2008,166,7.8,127.49M
+425,Sin City: A Dame to Kill For,"Action,Crime,Thriller",Frank Miller,"Mickey Rourke, Jessica Alba, Josh Brolin, Joseph Gordon-Levitt",2014,102,6.5,13.75
+426,The Bourne Ultimatum,"Action,Mystery,Thriller",Paul Greengrass,"Matt Damon, Edgar Ramírez, Joan Allen, Julia Stiles",2007,115,8.1,227.14
+427,Srpski film,"Horror,Mystery,Thriller",Srdjan Spasojevic,"Srdjan 'Zika' Todorovic, Sergej Trifunovic,Jelena Gavrilovic, Slobodan Bestic",2010,104,5.2,0
+428,The Purge: Election Year,"Action,Horror,Sci-Fi",James DeMonaco,"Frank Grillo, Elizabeth Mitchell, Mykelti Williamson, Joseph Julian Soria",2016,109,6,79
+429,3 Idiots,"Comedy,Drama",Rajkumar Hirani,"Aamir Khan, Madhavan, Mona Singh, Sharman Joshi",2009,170,8.4,6.52
+430,Zoolander 2,Comedy,Ben Stiller,"Ben Stiller, Owen Wilson, Penélope Cruz, Will Ferrell",2016,102,4.7,28.84
+431,World War Z,"Action,Adventure,Horror",Marc Forster,"Brad Pitt, Mireille Enos, Daniella Kertesz, James Badge Dale",2013,116,7,202.35
+432,Mission: Impossible - Ghost Protocol,"Action,Adventure,Thriller",Brad Bird,"Tom Cruise, Jeremy Renner, Simon Pegg, Paula Patton",2011,132,7.4,209.36
+433,Let Me Make You a Martyr,"Action,Crime,Drama",Corey Asraf,"Marilyn Manson, Mark Boone Junior, Sam Quartin, Niko Nicotera",2016,102,6.4,0
+434,Filth,"Comedy,Crime,Drama",Jon S. Baird,"James McAvoy, Jamie Bell, Eddie Marsan, Imogen Poots",2013,97,7.1,0.03
+435,The Longest Ride,"Drama,Romance",George Tillman Jr.,"Scott Eastwood, Britt Robertson, Alan Alda, Jack Huston",2015,123,7.1,37.43
+436,The imposible,"Drama,Thriller",J.A. Bayona,"Naomi Watts, Ewan McGregor, Tom Holland, Oaklee Pendergast",2012,114,7.6,19
+437,Kick-Ass 2,"Action,Comedy,Crime",Jeff Wadlow,"Aaron Taylor-Johnson, Chloë Grace Moretz,Christopher Mintz-Plasse, Jim Carrey",2013,103,6.6,28.75
+438,Folk Hero & Funny Guy,Comedy,Jeff Grace,"Alex Karpovsky, Wyatt Russell, Meredith Hagner,Melanie Lynskey",2016,88,5.6,0
+439,Oz the Great and Powerful,"Adventure,Family,Fantasy",Sam Raimi,"James Franco, Michelle Williams, Rachel Weisz, Mila Kunis",2013,130,6.3,234.9
+440,Brooklyn,"Drama,Romance",John Crowley,"Saoirse Ronan, Emory Cohen, Domhnall Gleeson,Jim Broadbent",2015,117,7.5,38.32
+441,Coraline,"Animation,Family,Fantasy",Henry Selick,"Dakota Fanning, Teri Hatcher, John Hodgman, Jennifer Saunders",2009,100,7.7,75.28
+442,Blue Valentine,"Drama,Romance",Derek Cianfrance,"Ryan Gosling, Michelle Williams, John Doman,Faith Wladyka",2010,112,7.4,9.7
+443,The Thinning,Thriller,Michael J. Gallagher,"Logan Paul, Peyton List, Lia Marie Johnson,Calum Worthy",2016,81,6,0
+444,Silent Hill,"Adventure,Horror,Mystery",Christophe Gans,"Radha Mitchell, Laurie Holden, Sean Bean,Deborah Kara Unger",2006,125,6.6,46.98
+445,Dredd,"Action,Sci-Fi",Pete Travis,"Karl Urban, Olivia Thirlby, Lena Headey, Rachel Wood",2012,95,7.1,13.4
+446,Hunt for the Wilderpeople,"Adventure,Comedy,Drama",Taika Waititi,"Sam Neill, Julian Dennison, Rima Te Wiata, Rachel House",2016,101,7.9,5.2
+447,Big Hero 6,"Animation,Action,Adventure",Don Hall,"Ryan Potter, Scott Adsit, Jamie Chung,T.J. Miller",2014,102,7.8,222.49
+448,Carrie,"Drama,Horror",Kimberly Peirce,"Chloë Grace Moretz, Julianne Moore, Gabriella Wilde, Portia Doubleday",2013,100,5.9,35.27
+449,Iron Man 2,"Action,Adventure,Sci-Fi",Jon Favreau,"Robert Downey Jr., Mickey Rourke, Gwyneth Paltrow,Don Cheadle",2010,124,7,312.06
+450,Demolition,"Comedy,Drama",Jean-Marc Vallée,"Jake Gyllenhaal, Naomi Watts, Chris Cooper,Judah Lewis",2015,101,7,1.82
+451,Pandorum,"Action,Horror,Mystery",Christian Alvart,"Dennis Quaid, Ben Foster, Cam Gigandet, Antje Traue",2009,108,6.8,10.33
+452,Olympus Has Fallen,"Action,Thriller",Antoine Fuqua,"Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett",2013,119,6.5,98.9
+453,I Am Number Four,"Action,Adventure,Sci-Fi",D.J. Caruso,"Alex Pettyfer, Timothy Olyphant, Dianna Agron, Teresa Palmer",2011,109,6.1,55.09
+454,Jagten,Drama,Thomas Vinterberg,"Mads Mikkelsen, Thomas Bo Larsen, Annika Wedderkopp, Lasse Fogelstrøm",2012,115,8.3,0.61
+455,The Proposal,"Comedy,Drama,Romance",Anne Fletcher,"Sandra Bullock, Ryan Reynolds, Mary Steenburgen,Craig T. Nelson",2009,108,6.7,163.95M
+456,Get Hard,"Comedy,Crime",Etan Cohen,"Will Ferrell, Kevin Hart, Alison Brie, T.I.",2015,100,6,90.35
+457,Just Go with It,"Comedy,Romance",Dennis Dugan,"Adam Sandler, Jennifer Aniston, Brooklyn Decker,Nicole Kidman",2011,117,6.4,103.03M
+458,Revolutionary Road,"Drama,Romance",Sam Mendes,"Leonardo DiCaprio, Kate Winslet, Christopher Fitzgerald, Jonathan Roumie",2008,119,7.3,22.88
+459,The Town,"Crime,Drama,Thriller",Ben Affleck,"Ben Affleck, Rebecca Hall, Jon Hamm, Jeremy Renner",2010,125,7.6,92.17
+460,The Boy,"Horror,Mystery,Thriller",William Brent Bell,"Lauren Cohan, Rupert Evans, James Russell, Jim Norton",2016,97,6,35.79
+461,Denial,"Biography,Drama",Mick Jackson,"Rachel Weisz, Tom Wilkinson, Timothy Spall, Andrew Scott",2016,109,6.6,4.07
+462,Predestination,"Drama,Mystery,Sci-Fi",Michael Spierig,"Ethan Hawke, Sarah Snook, Noah Taylor, Madeleine West",2014,97,7.5,0
+463,Goosebumps,"Adventure,Comedy,Family",Rob Letterman,"Jack Black, Dylan Minnette, Odeya Rush, Ryan Lee",2015,103,6.3,80.02
+464,Sherlock Holmes: A Game of Shadows,"Action,Adventure,Crime",Guy Ritchie,"Robert Downey Jr., Jude Law, Jared Harris, Rachel McAdams",2011,129,7.5,186.83M
+465,Salt,"Action,Crime,Mystery",Phillip Noyce,"Angelina Jolie, Liev Schreiber, Chiwetel Ejiofor, Daniel Olbrychski",2010,100,6.4,118.31M
+466,Enemy,"Mystery,Thriller",Denis Villeneuve,"Jake Gyllenhaal, Mélanie Laurent, Sarah Gadon,Isabella Rossellini",2013,91,6.9,1.01
+467,District 9,"Action,Sci-Fi,Thriller",Neill Blomkamp,"Sharlto Copley, David James, Jason Cope, Nathalie Boltt",2009,112,8,115.65M
+468,The Other Guys,"Action,Comedy,Crime",Adam McKay,"Will Ferrell, Mark Wahlberg, Derek Jeter, Eva Mendes",2010,107,6.7,119.22M
+469,American Gangster,"Biography,Crime,Drama",Ridley Scott,"denzel Washington, Russell Crowe, Chiwetel Ejiofor,Josh Brolin",2007,157,7.8,130.13M
+470,Marie Antoinette,"Biography,Drama,History",Sofia Coppola,"Kirsten Dunst, Jason Schwartzman, Rip Torn, Judy Davis",2006,123,6.4,15.96
+471,2012,"Action,Adventure,Sci-Fi",Roland Emmerich,"John Cusack, Thandie Newton, Chiwetel Ejiofor,Amanda Peet",2009,158,5.8,166.11M
+472,Harry Potter and the Half-Blood Prince,"Adventure,Family,Fantasy",David Yates,"Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon",2009,153,7.5,301.96
+473,Argo,"Biography,Drama,History",Ben Affleck,"Ben Affleck, Bryan Cranston, John Goodman, Alan Arkin",2012,120,7.7,136.02M
+474,Eddie the Eagle,"Biography,Comedy,Drama",Dexter Fletcher,"Taron Egerton, Hugh Jackman, Tom Costello, Jo Hartley",2016,106,7.4,15.79
+475,The Lives of Others,"Drama,Thriller",Florian Henckel von Donnersmarck,"Ulrich Mühe, Martina Gedeck,Sebastian Koch, Ulrich Tukur",2006,137,8.5,11.28
+476,Pet,"Horror,Thriller",Carles Torrens,"Dominic Monaghan, Ksenia Solo, Jennette McCurdy,Da'Vone McDonald",2016,94,5.7,0
+477,Paint It Black,Drama,Amber Tamblyn,"Alia Shawkat, Nancy Kwan, Annabelle Attanasio,Alfred Molina",2016,96,8.3,0
+478,Macbeth,"Drama,War",Justin Kurzel,"Michael        Fassbender, Marion Cotillard, Jack Madigan,Frank Madigan",2015,113,6.7,0
+479,Forgetting Sarah Marshall,"Comedy,Drama,Romance",Nicholas Stoller,"Kristen Bell, Jason Segel, Paul Rudd, Mila Kunis",2008,111,7.2,62.88
+480,The Giver,"Drama,Romance,Sci-Fi",Phillip Noyce,"Brenton Thwaites, Jeff Bridges, Meryl Streep, Taylor Swift",2014,97,6.5,45.09
+481,Triple 9,"Action,Crime,Drama",John Hillcoat,"Casey Affleck, Chiwetel Ejiofor, Anthony Mackie,Aaron Paul",2016,115,6.3,12.63
+482,Perfetti sconosciuti,"Comedy,Drama",Paolo Genovese,"Giuseppe Battiston, Anna Foglietta, Marco Giallini,Edoardo Leo",2016,97,7.7,0
+483,Angry Birds,"Animation,Action,Adventure",Clay Kaytis,"Jason Sudeikis, Josh Gad, Danny McBride, Maya Rudolph",2016,97,6.3,107.51M
+484,Moonrise Kingdom,"Adventure,Comedy,Drama",Wes Anderson,"Jared Gilman, Kara Hayward, Bruce Willis, Bill Murray",2012,94,7.8,45.51
+485,Hairspray,"Comedy,Drama,Family",Adam Shankman,"John Travolta, Queen Latifah, Nikki Blonsky,Michelle Pfeiffer",2007,117,6.7,118.82M
+486,Safe Haven,"Drama,Romance,Thriller",Lasse Hallström,"Julianne Hough, Josh Duhamel, Cobie Smulders,David Lyons",2013,115,6.7,71.35
+487,Focus,"Comedy,Crime,Drama",Glenn Ficarra,"Will Smith, Margot Robbie, Rodrigo Santoro, Adrian Martinez",2015,105,6.6,53.85
+488,Ratatouille,"Animation,Comedy,Family",Brad Bird,"Brad Garrett, Lou Romano, Patton Oswalt,Ian Holm",2007,111,8,206.44
+489,Stake Land,"Drama,Horror,Sci-Fi",Jim Mickle,"Connor Paolo, Nick Damici, Kelly McGillis, Gregory Jones",2010,98,6.5,0.02
+490,The Book of Eli,"Action,Adventure,Drama",Albert Hughes,"Denzel       Washington, Mila Kunis, Ray Stevenson, Gary Oldman",2010,118,6.9,94.82
+491,Cloverfield,"Action,Horror,Sci-Fi",Matt Reeves,"Mike Vogel, Jessica Lucas, Lizzy Caplan, T.J. Miller",2008,85,7,80.03
+492,Point Break,"Action,Crime,Sport",Ericson Core,"Edgar Ramírez, Luke Bracey, Ray Winstone, Teresa Palmer",2015,114,5.3,28.77
+493,Under the Skin,"Drama,Horror,Sci-Fi",Jonathan Glazer,"Scarlett Johansson, Jeremy McWilliams, Lynsey Taylor Mackay, Dougie McConnell",2013,108,6.3,2.61
+494,I Am Legend,"Drama,Horror,Sci-Fi",Francis Lawrence,"Will Smith, Alice Braga, Charlie Tahan, Salli Richardson-Whitfield",2007,101,7.2,256.39
+495,Men in Black 3,"Action,Adventure,Comedy",Barry Sonnenfeld,"Will Smith, Tommy Lee Jones, Josh Brolin,Jemaine Clement",2012,106,6.8,179.02M
+496,Super 8,"Mystery,Sci-Fi,Thriller",J.J. Abrams,"Elle Fanning, AJ Michalka, Kyle Chandler, Joel Courtney",2011,112,7.1,126.98M
+497,Law Abiding Citizen,"Crime,Drama,Thriller",F. Gary Gray,"Gerard Butler, Jamie Foxx, Leslie Bibb, Colm Meaney",2009,109,7.4,73.34
+498,Up,"Animation,Adventure,Comedy",Pete Docter,"Edward Asner, Jordan Nagai, John Ratzenberger, Christopher Plummer",2009,96,8.3,292.98
+499,Maze Runner: The Scorch Trials,"Action,Sci-Fi,Thriller",Wes Ball,"Dylan O'Brien, Kaya Scodelario, Thomas Brodie-Sangster,Giancarlo Esposito",2015,131,6.3,81.69
+500,Carol,"Drama,Romance",Todd Haynes,"Cate Blanchett, Rooney Mara, Sarah Paulson, Kyle Chandler",2015,118,7.2,0.25
+501,Imperium,"Crime,Drama,Thriller",Daniel Ragussis,"Daniel Radcliffe, Toni Collette, Tracy Letts, Sam Trammell",2016,109,6.5,0
+502,Youth,"Comedy,Drama,Music",Paolo Sorrentino,"Michael Caine, Harvey Keitel, Rachel Weisz, Jane Fonda",2015,124,7.3,2.7
+503,Mr. Nobody,"Drama,Fantasy,Romance",Jaco Van Dormael,"Jared Leto, Sarah Polley, Diane Kruger, Linh Dan Pham",2009,141,7.9,0
+504,City of Tiny Lights,"Crime,Drama,Thriller",Pete Travis,"Riz Ahmed, Billie Piper, James Floyd, Cush Jumbo",2016,110,5.7,0
+505,Savages,"Crime,Drama,Thriller",Oliver Stone,"Aaron Taylor-Johnson, Taylor Kitsch, Blake Lively,Benicio Del Toro",2012,131,6.5,47.31
+506,(500) Days of Summer,"Comedy,Drama,Romance",Marc Webb,"Zooey Deschanel, Joseph Gordon-Levitt, Geoffrey Arend, Chloë Grace Moretz",2009,95,7.7,32.39
+507,Movie 43,"Comedy,Romance",Elizabeth Banks,"Emma Stone, Stephen Merchant, Richard Gere, Liev Schreiber",2013,94,4.3,8.83
+508,Gravity,"Drama,Sci-Fi,Thriller",Alfonso Cuarón,"Sandra Bullock, George Clooney, Ed Harris, Orto Ignatiussen",2013,91,7.8,274.08
+509,The Boy in the Striped Pyjamas,"Drama,War",Mark Herman,"Asa Butterfield, David Thewlis, Rupert Friend, Zac Mattoon O'Brien",2008,94,7.8,9.03
+510,Shooter,"Action,Crime,Drama",Antoine Fuqua,"Mark Wahlberg, Michael Peña, Rhona Mitra, Danny Glover",2007,124,7.2,46.98
+511,The Happening,"Sci-Fi,Thriller",M. Night Shyamalan,"Mark Wahlberg, Zooey Deschanel, John Leguizamo, Ashlyn Sanchez",2008,91,5,64.51
+512,Bone Tomahawk,"Adventure,Drama,Horror",S. Craig Zahler,"Kurt Russell, Patrick Wilson, Matthew Fox, Richard Jenkins",2015,132,7.1,66.01
+513,Magic Mike XXL,"Comedy,Drama,Music",Gregory Jacobs,"Channing Tatum, Joe Manganiello, Matt Bomer,Adam Rodriguez",2015,115,5.7,0
+514,Easy A,"Comedy,Drama,Romance",Will Gluck,"Emma Stone, Amanda Bynes, Penn Badgley, Dan Byrd",2010,92,7.1,58.4
+515,Exodus: Gods and Kings,"Action,Adventure,Drama",Ridley Scott,"Christian Bale, Joel Edgerton, Ben Kingsley, Sigourney Weaver",2014,150,6,65.01
+516,Chappie,"Action,Crime,Drama",Neill Blomkamp,"Sharlto Copley, Dev Patel, Hugh Jackman,Sigourney Weaver",2015,120,6.9,31.57
+517,The Hobbit: The Desolation of Smaug,"Adventure,Fantasy",Peter Jackson,"Ian McKellen, Martin Freeman, Richard Armitage,Ken Stott",2013,161,7.9,258.36
+518,Half of a Yellow Sun,"Drama,Romance",Biyi Bandele,"Chiwetel Ejiofor, Thandie Newton, Anika Noni Rose,Joseph Mawle",2013,111,6.2,0.05
+519,Anthropoid,"Biography,History,Thriller",Sean Ellis,"Jamie Dornan, Cillian Murphy, Brian Caspe, Karel Hermánek Jr.",2016,120,7.2,2.96
+520,The Counselor,"Crime,Drama,Thriller",Ridley Scott,"Michael   fassbender, Penélope Cruz, Cameron Diaz,Javier Bardem",2013,117,5.3,16.97
+521,Viking,"Action,Drama,History",Andrey Kravchuk,"Anton Adasinsky, Aleksandr Armer, Vilen Babichev, Rostislav Bershauer",2016,133,4.7,23.05
+522,Whiskey Tango Foxtrot,"Biography,Comedy,Drama",Glenn Ficarra,"Tina Fey, Margot Robbie, Martin Freeman, Alfred Molina",2016,112,6.6,0
+523,Trust,"Crime,Drama,Thriller",David Schwimmer,"Clive Owen, Catherine Keener, Liana Liberato,Jason Clarke",2010,106,7,0.06
+524,Birth of the Dragon,"Action,Biography,Drama",George Nolfi,"Billy Magnussen, Terry Chen, Teresa Navarro,Vanessa Ross",2016,103,3.9,93.05
+525,Elysium,"Action,Drama,Sci-Fi",Neill Blomkamp,"Matt Damon, Jodie Foster, Sharlto Copley, Alice Braga",2013,109,6.6,0
+526,The Green Inferno,"Adventure,Horror",Eli Roth,"Lorenza Izzo, Ariel Levy, Aaron Burns, Kirby Bliss Blanton",2013,100,5.4,7.19
+527,Godzilla,"Action,Adventure,Sci-Fi",Gareth Edwards,"Aaron Taylor-Johnson, Elizabeth Olsen, Bryan Cranston, Ken Watanabe",2014,123,6.4,200.66
+528,The Bourne Legacy,"Action,Adventure,Mystery",Tony Gilroy,"Jeremy Renner, Rachel Weisz, Edward Norton, Scott Glenn",2012,135,6.7,113.17M
+529,A Good Year,"Comedy,Drama,Romance",Ridley Scott,"Russell Crowe, Abbie Cornish, Albert Finney, Marion Cotillard",2006,117,6.9,7.46
+530,Friend Request,"Horror,Thriller",Simon Verhoeven,"Alycia Debnam-Carey, William Moseley, Connor Paolo, Brit Morgan",2016,92,5.4,64.03
+531,Deja Vu,"Action,Sci-Fi,Thriller",Tony Scott,"Denzel Washington, Paula Patton, Jim Caviezel, Val Kilmer",2006,126,7,0
+532,Lucy,"Action,Sci-Fi,Thriller",Luc Besson,"Scarlett Johansson, Morgan Freeman, Min-sik Choi,Amr Waked",2014,89,6.4,126.55M
+533,A Quiet Passion,"Biography,Drama",Terence Davies,"Cynthia Nixon, Jennifer Ehle, Duncan Duff, Keith Carradine",2016,125,7.2,1.08
+534,Need for Speed,"Action,Crime,Drama",Scott Waugh,"Aaron Paul, Dominic Cooper, Imogen Poots, Scott Mescudi",2014,132,6.5,43.57
+535,Jack Reacher,"Action,Crime,Mystery",Christopher McQuarrie,"Tom Cruise, Rosamund Pike, Richard Jenkins, Werner Herzog",2012,130,7,58.68
+536,The Do-Over,"Action,Adventure,Comedy",Steven Brill,"Adam Sandler, David Spade, Paula Patton, Kathryn Hahn",2016,108,5.7,0.54
+537,True Crimes,"Crime,Drama,Thriller",Alexandros Avranas,"Jim Carrey, Charlotte Gainsbourg, Marton Csokas, Kati Outinen",2016,92,7.3,0
+538,American Pastoral,"Crime,Drama",Ewan McGregor,"Ewan McGregor, Jennifer Connelly, Dakota Fanning, Peter Riegert",2016,108,6.1,0
+539,The Ghost Writer,"Mystery,Thriller",Roman Polanski,"Ewan McGregor, Pierce Brosnan, Olivia Williams,Jon Bernthal",2010,128,7.2,15.52
+540,Limitless,"Mystery,Sci-Fi,Thriller",Neil Burger,"Bradley Cooper, Anna Friel, Abbie Cornish, Robert De Niro",2011,105,7.4,79.24
+541,Spectral,"Action,Mystery,Sci-Fi",Nic Mathieu,"James Badge Dale, Emily Mortimer, Bruce Greenwood,Max Martini",2016,107,6.3,0
+542,P.S. I Love You,"Drama,Romance",Richard LaGravenese,"Hilary Swank, Gerard Butler, Harry Connick Jr., Lisa Kudrow",2007,126,7.1,53.68
+543,Zipper,"Drama,Thriller",Mora Stephens,"Patrick Wilson, Lena Headey, Ray Winstone,Richard Dreyfuss",2015,103,5.7,0
+544,Midnight Special,"Drama,Mystery,Sci-Fi",Jeff Nichols,"Michael Shannon, Joel Edgerton, Kirsten Dunst, Adam Driver",2016,112,6.7,3.71
+545,Don't Think Twice,"Comedy,Drama",Mike Birbiglia,"Keegan-Michael Key, Gillian Jacobs, Mike Birbiglia,Chris Gethard",2016,92,6.8,4.42
+546,Alice in Wonderland,"Adventure,Family,Fantasy",Tim Burton,"Mia Wasikowska, Johnny Depp, Helena Bonham Carter,Anne Hathaway",2010,108,6.5,334.19
+547,Chuck,"Biography,Drama,Sport",Philippe Falardeau,"Elisabeth Moss, Naomi Watts, Ron Perlman, Liev Schreiber",2016,98,6.8,0.11
+548,"I, Daniel Blake",Drama,Ken Loach,"Dave Johns, Hayley Squires, Sharon Percy, Briana Shann",2016,100,7.9,0
+549,The Break-Up,"Comedy,Drama,Romance",Peyton Reed,"Jennifer Aniston, Vince Vaughn, Jon Favreau, Joey Lauren Adams",2006,106,5.8,118.68M
+550,Loving,"Biography,Drama,Romance",Jeff Nichols,"Ruth Negga, Joel Edgerton, Will Dalton, Dean Mumford",2016,123,7.1,7.7
+551,Fantastic Four,"Action,Adventure,Sci-Fi",Josh Trank,"Miles Teller, Kate Mara, Michael B. Jordan, Jamie Bell",2015,100,4.3,56.11
+552,The Survivalist,"Drama,Sci-Fi,Thriller",Stephen Fingleton,"Mia Goth, Martin McCann, Barry Ward, Andrew Simpson",2015,104,6.3,0
+553,Colonia,"Drama,Romance,Thriller",Florian Gallenberger,"Emma Watson, Daniel Brühl, Michael Nyqvist,Richenda Carey",2015,106,7.1,0
+554,The Boy Next Door,"Mystery,Thriller",Rob Cohen,"Jennifer Lopez, Ryan Guzman, Kristin Chenoweth, John Corbett",2015,91,4.6,35.39
+555,The Gift,"Mystery,Thriller",Joel Edgerton,"Jason Bateman, Rebecca Hall, Joel Edgerton, Allison Tolman",2015,108,7.1,43.77
+556,Dracula Untold,"Action,Drama,Fantasy",Gary Shore,"Luke Evans, Dominic Cooper, Sarah Gadon, Art Parkinson",2014,92,6.3,55.94
+557,In the Heart of the Sea,"Action,Adventure,Biography",Ron Howard,"Chris Hemsworth, Cillian Murphy, Brendan Gleeson,Ben Whishaw",2015,122,6.9,24.99
+558,Idiocracy,"Adventure,Comedy,Sci-Fi",Mike Judge,"Luke Wilson, Maya Rudolph, Dax Shepard, Terry Crews",2006,84,6.6,0.44
+559,The Expendables,"Action,Adventure,Thriller",Sylvester Stallone,"Sylvester Stallone, Jason Statham, Jet Li, Dolph Lundgren",2010,103,6.5,102.98M
+560,Evil Dead,"Fantasy,Horror",Fede Alvarez,"Jane Levy, Shiloh Fernandez, Jessica Lucas, Lou Taylor Pucci",2013,91,6.5,54.24
+561,Sinister,"Horror,Mystery",Scott Derrickson,"Ethan Hawke, Juliet Rylance, James Ransone,Fred Dalton Thompson",2012,110,6.8,48.06
+562,Wreck-It Ralph,"Animation,Adventure,Comedy",Rich Moore,"John C. Reilly, Jack McBrayer, Jane Lynch, Sarah Silverman",2012,101,7.8,189.41M
+563,Snow White and the Huntsman,"Action,Adventure,Drama",Rupert Sanders,"Kristen Stewart, Chris Hemsworth, Charlize Theron, Sam Claflin",2012,127,6.1,155.11M
+564,Pan,"Adventure,Family,Fantasy",Joe Wright,"Levi Miller, Hugh Jackman, Garrett Hedlund, Rooney Mara",2015,111,5.8,34.96
+565,Transformers: Dark of the Moon,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Rosie Huntington-Whiteley, Tyrese Gibson, Josh Duhamel",2011,154,6.3,352.36
+566,Juno,"Comedy,Drama",Jason Reitman,"Ellen Page, Michael Cera, Jennifer Garner, Jason Bateman",2007,96,7.5,143.49M
+567,A Hologram for the King,"Comedy,Drama",Tom Tykwer,"Tom Hanks, Sarita Choudhury, Ben Whishaw,Alexander Black",2016,98,6.1,4.2
+568,Money Monster,"Crime,Drama,Thriller",Jodie Foster,"George Clooney, Julia Roberts, Jack O'Connell,Dominic West",2016,98,6.5,41.01
+569,The Other Woman,"Comedy,Romance",Nick Cassavetes,"Cameron Diaz, Leslie Mann, Kate Upton, Nikolaj Coster-Waldau",2014,109,6,83.91
+570,Enchanted,"Animation,Comedy,Family",Kevin Lima,"Amy Adams, Susan Sarandon, James Marsden, Patrick Dempsey",2007,107,7.1,127.71M
+571,The Intern,"Comedy,Drama",Nancy Meyers,"Robert De Niro, Anne Hathaway, Rene Russo,Anders Holm",2015,121,7.1,75.27
+572,Little Miss Sunshine,"Comedy,Drama",Jonathan Dayton,"Steve Carell, Toni Collette, Greg Kinnear, Abigail Breslin",2006,101,7.8,59.89
+573,Bleed for This,"Biography,Drama,Sport",Ben Younger,"Miles Teller, Aaron Eckhart, Katey Sagal, Ciarán Hinds",2016,117,6.8,4.85
+574,Clash of the Titans,"Action,Adventure,Fantasy",Louis Leterrier,"Sam Worthington, Liam Neeson, Ralph Fiennes,Jason Flemyng",2010,106,5.8,163.19M
+575,The Finest Hours,"Action,Drama,History",Craig Gillespie,"Chris Pine, Casey Affleck, Ben Foster, Eric Bana",2016,117,6.8,27.55
+576,Tron,"Action,Adventure,Sci-Fi",Joseph Kosinski,"Jeff Bridges, Garrett Hedlund, Olivia Wilde, Bruce Boxleitner",2010,125,6.8,172.05M
+577,The Hunger Games: Catching Fire,"Action,Adventure,Mystery",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Philip Seymour Hoffman",2013,146,7.6,424.65
+578,All Good Things,"Crime,Drama,Mystery",Andrew Jarecki,"Ryan Gosling, Kirsten Dunst, Frank Langella, Lily Rabe",2010,101,6.3,0.58
+579,Kickboxer: Vengeance,Action,John Stockwell,"Dave Bautista, Alain Moussi, Gina Carano, Jean-Claude Van Damme",2016,90,4.9,131.56M
+580,The Last Airbender,"Action,Adventure,Family",M. Night Shyamalan,"Noah Ringer, Nicola Peltz, Jackson Rathbone,Dev Patel",2010,103,4.2,0
+581,Sex Tape,"Comedy,Romance",Jake Kasdan,"Jason Segel, Cameron Diaz, Rob Corddry, Ellie Kemper",2014,94,5.1,38.54
+582,What to Expect When You're Expecting,"Comedy,Drama,Romance",Kirk Jones,"Cameron Diaz, Matthew Morrison, J. Todd Smith, Dennis Quaid",2012,110,5.7,41.1
+583,Moneyball,"Biography,Drama,Sport",Bennett Miller,"Brad Pitt, Robin Wright, Jonah Hill, Philip Seymour Hoffman",2011,133,7.6,75.61
+584,Ghost Rider,"Action,Fantasy,Thriller",Mark Steven Johnson,"Nicolas Cage, Eva Mendes, Sam Elliott, Matt Long",2007,114,5.2,115.8M
+585,Unbroken,"Biography,Drama,Sport",Angelina Jolie,"Jack O'Connell, Miyavi, Domhnall Gleeson, Garrett Hedlund",2014,137,7.2,115.6M
+586,Immortals,"Action,Drama,Fantasy",Tarsem Singh,"Henry Cavill, Mickey Rourke, John Hurt, Stephen Dorff",2011,110,6,83.5
+587,Sunshine,"Adventure,Sci-Fi,Thriller",Danny Boyle,"Cillian Murphy, Rose Byrne, Chris Evans, Michelle Yeoh",2007,107,7.3,3.68
+588,Brave,"Animation,Adventure,Comedy",Mark Andrews,"Kelly Macdonald,Billy Connolly, Emma Thompson, Julie Walters",2012,93,7.2,237.28
+589,Män som hatar kvinnor,"Drama,Mystery,Thriller",Niels Arden Oplev,"Michael Nyqvist, Noomi Rapace, Ewa Fröling,Lena Endre",2009,152,7.8,10.1
+590,Adoration,"Drama,Romance",Anne Fontaine,"Naomi Watts, Robin Wright, Xavier Samuel, James Frecheville",2013,112,6.2,0.32
+591,The Drop,"Crime,Drama,Mystery",Michaël R. Roskam,"Tom Hardy, Noomi Rapace, James Gandolfini,Matthias Schoenaerts",2014,106,7.1,10.72
+592,She's the Man,"Comedy,Romance,Sport",Andy Fickman,"Amanda Bynes, Laura Ramsey, Channing Tatum,Vinnie Jones",2006,105,6.4,2.34
+593,Daddy's Home,"Comedy,Family",Sean Anders,"Will Ferrell, Mark Wahlberg, Linda Cardellini, Thomas Haden Church",2015,96,6.1,150.32M
+594,Let Me In,"Drama,Horror,Mystery",Matt Reeves,"Kodi Smit-McPhee, Chloë Grace Moretz, Richard Jenkins, Cara Buono",2010,116,7.2,12.13
+595,Never Back Down,"Action,Drama,Sport",Jeff Wadlow,"Sean Faris, Djimon Hounsou, Amber Heard, Cam Gigandet",2008,110,6.6,24.85
+596,Grimsby,"Action,Adventure,Comedy",Louis Leterrier,"Sacha Baron Cohen, Mark Strong, Rebel Wilson,Freddie Crowder",2016,83,6.2,6.86
+597,Moon,"Drama,Mystery,Sci-Fi",Duncan Jones,"Sam Rockwell, Kevin Spacey, Dominique McElligott,Rosie Shaw",2009,97,7.9,5.01
+598,Megamind,"Animation,Action,Comedy",Tom McGrath,"Will Ferrell, Jonah Hill, Brad Pitt, Tina Fey",2010,95,7.3,148.34M
+599,Gangster Squad,"Action,Crime,Drama",Ruben Fleischer,"Sean Penn, Ryan Gosling, Emma Stone, Giovanni Ribisi",2013,113,6.7,46
+600,Blood Father,"Action,Crime,Drama",Jean-François Richet,"Mel Gibson, Erin Moriarty, Diego Luna, Michael Parks",2016,88,6.4,93.95
+601,He's Just Not That Into You,"Comedy,Drama,Romance",Ken Kwapis,"Jennifer Aniston, Jennifer Connelly, Morgan Lily,Trenton Rogers",2009,129,6.4,0
+602,Kung Fu Panda 3,"Animation,Action,Adventure",Alessandro Carloni,"Jack Black, Bryan Cranston, Dustin Hoffman, Angelina Jolie",2016,95,7.2,143.52M
+603,The Rise of the Krays,"Crime,Drama",Zackary Adler,"Matt Vael, Simon Cotton, Kevin Leslie, Olivia Moyles",2015,110,5.1,6.53
+604,Handsome Devil,Drama,John Butler,"Fionn O'Shea, Nicholas Galitzine, Andrew Scott, Moe Dunford",2016,95,7.4,0
+605,Winter's Bone,Drama,Debra Granik,"Jennifer Lawrence, John Hawkes, Garret Dillahunt,Isaiah Stone",2010,100,7.2,0
+606,Horrible Bosses,"Comedy,Crime",Seth Gordon,"Jason Bateman, Charlie Day, Jason Sudeikis, Steve Wiebe",2011,98,6.9,117.53M
+607,Mommy,Drama,Xavier Dolan,"Anne Dorval, Antoine-Olivier Pilon, Suzanne Clément,Patrick Huard",2014,139,8.1,3.49
+608,Hellboy II: The Golden Army,"Action,Adventure,Fantasy",Guillermo del Toro,"Ron Perlman, Selma Blair, Doug Jones, John Alexander",2008,120,7,75.75
+609,Beautiful Creatures,"Drama,Fantasy,Romance",Richard LaGravenese,"Alice Englert, Viola Davis, Emma Thompson,Alden Ehrenreich",2013,124,6.2,19.45
+610,Toni Erdmann,"Comedy,Drama",Maren Ade,"Sandra Hüller, Peter Simonischek, Michael Wittenborn,Thomas Loibl",2016,162,7.6,1.48
+611,The Lovely Bones,"Drama,Fantasy,Thriller",Peter Jackson,"Rachel Weisz, Mark Wahlberg, Saoirse Ronan, Susan Sarandon",2009,135,6.7,43.98
+612,The Assassination of Jesse James by the Coward Robert Ford,"Biography,Crime,Drama",Andrew Dominik,"Brad Pitt, Casey Affleck, Sam Shepard, Mary-Louise Parker",2007,160,7.5,3.9
+613,Don Jon,"Comedy,Drama,Romance",Joseph Gordon-Levitt,"Joseph Gordon-Levitt, Scarlett Johansson,Julianne Moore, Tony Danza",2013,90,6.6,24.48
+614,Bastille Day,"Action,Crime,Drama",James Watkins,"Idris Elba, Richard Madden, Charlotte Le Bon, Kelly Reilly",2016,92,6.3,0.04
+615,2307: Winter's Dream,Sci-Fi,Joey Curtis,"Paul Sidhu, Branden Coles, Arielle Holmes, Kelcey Watson",2016,101,4,20.76
+616,Free State of Jones,"Action,Biography,Drama",Gary Ross,"Matthew McConaughey, Gugu Mbatha-Raw, Mahershala Ali, Keri Russell",2016,139,6.9,0
+617,Mr. Right,"Action,Comedy,Romance",Paco Cabezas,"Anna Kendrick, Sam Rockwell, Tim Roth, James Ransone",2015,95,6.3,0.03
+618,The Secret Life of Walter Mitty,"Adventure,Comedy,Drama",Ben Stiller,"Ben Stiller, Kristen Wiig, Jon Daly, Kathryn Hahn",2013,114,7.3,58.23
+619,Dope,"Comedy,Crime,Drama",Rick Famuyiwa,"Shameik Moore, Tony Revolori, Kiersey Clemons,Kimberly Elise",2015,103,7.3,17.47
+620,Underworld Awakening,"Action,Fantasy,Horror",Måns Mårlind,"Kate Beckinsale, Michael Ealy, India Eisley, Stephen Rea",2012,88,6.4,62.32
+621,Antichrist,"Drama,Horror",Lars von Trier,"Willem Dafoe, Charlotte Gainsbourg, Storm Acheche Sahlstrøm",2009,108,6.6,0.4
+622,Friday the 13th,Horror,Marcus Nispel,"Jared Padalecki, Amanda Righetti, Derek Mears,Danielle Panabaker",2009,97,5.6,65
+623,Taken 3,"Action,Thriller",Olivier Megaton,"Liam Neeson, Forest Whitaker, Maggie Grace,Famke Janssen",2014,109,6,89.25
+624,Total Recall,"Action,Adventure,Mystery",Len Wiseman,"Colin Farrell, Bokeem Woodbine, Bryan Cranston,Kate Beckinsale",2012,118,6.3,58.88
+625,X-Men: The Last Stand,"Action,Adventure,Fantasy",Brett Ratner,"Patrick Stewart, Hugh Jackman, Halle Berry, Famke Janssen",2006,104,6.7,234.36
+626,The Escort,"Comedy,Drama,Romance",Will Slocombe,"Lyndsy Fonseca, Michael Doneger, Tommy Dewey,Bruce Campbell",2016,88,6,0
+627,The Whole Truth,"Crime,Drama,Mystery",Courtney Hunt,"Keanu Reeves, Renée Zellweger, Gugu Mbatha-Raw, Gabriel Basso",2016,93,6.1,0
+628,Night at the Museum: Secret of the Tomb,"Adventure,Comedy,Family",Shawn Levy,"Ben Stiller, Robin Williams, Owen Wilson, Dick Van Dyke",2014,98,6.2,113.73M
+629,Love & Other Drugs,"Comedy,Drama,Romance",Edward Zwick,"Jake Gyllenhaal, Anne Hathaway, Judy Greer, Oliver Platt",2010,112,6.7,32.36
+630,The Interview,Comedy,Evan Goldberg,"James Franco, Seth Rogen, Randall Park, Lizzy Caplan",2014,112,6.6,6.11
+631,The Host,"Comedy,Drama,Horror",Bong Joon Ho,"Kang-ho Song, Hee-Bong Byun, Hae-il Park, Doona Bae",2006,120,7,2.2
+632,Megan Is Missing,"Drama,Horror,Thriller",Michael Goi,"Amber Perkins, Rachel Quinn, Dean Waite, Jael Elizabeth Steinmeyer",2011,85,4.9,0
+633,WALL·E,"Animation,Adventure,Family",Andrew Stanton,"Ben Burtt, Elissa Knight, Jeff Garlin, Fred Willard",2008,98,8.4,223.81
+634,Knocked Up,"Comedy,Romance",Judd Apatow,"Seth Rogen, Katherine Heigl, Paul Rudd, Leslie Mann",2007,129,7,148.73M
+635,Source Code,"Mystery,Romance,Sci-Fi",Duncan Jones,"Jake Gyllenhaal, Michelle Monaghan, Vera Farmiga,Jeffrey Wright",2011,93,7.5,54.7
+636,Lawless,"Crime,Drama",John Hillcoat,"Tom Hardy, Shia LaBeouf, Guy Pearce, Jason Clarke",2012,116,7.3,37.4
+637,Unfriended,"Drama,Horror,Mystery",Levan Gabriadze,"Heather Sossaman, Matthew Bohrer, Courtney Halverson, Shelley Hennig",2014,83,5.6,31.54
+638,American Reunion,Comedy,Jon Hurwitz,"Jason Biggs, Alyson Hannigan,Seann William Scott, Chris Klein",2012,113,6.7,56.72
+639,The Pursuit of Happyness,"Biography,Drama",Gabriele Muccino,"Will Smith, Thandie Newton, Jaden Smith, Brian Howe",2006,117,8,162.59M
+640,Relatos salvajes,"Comedy,Drama,Thriller",Damián Szifron,"Darío Grandinetti, María Marull, Mónica Villa, Rita Cortese",2014,122,8.1,3.08
+641,The Ridiculous 6,"Comedy,Western",Frank Coraci,"Adam Sandler, Terry Crews, Jorge Garcia, Taylor Lautner",2015,119,4.8,0
+642,Frantz,"Drama,History,War",François Ozon,"Pierre Niney, Paula Beer, Ernst Stötzner, Marie Gruber",2016,113,7.5,0.86
+643,Viral,"Drama,Horror,Sci-Fi",Henry Joost,"Sofia Black-D'Elia, Analeigh Tipton,Travis Tope, Michael Kelly",2016,85,5.5,0
+644,Gran Torino,Drama,Clint Eastwood,"Clint Eastwood, Bee Vang, Christopher Carley,Ahney Her",2008,116,8.2,148.09M
+645,Burnt,"Comedy,Drama",John Wells,"Bradley Cooper, Sienna Miller, Daniel Brühl, Riccardo Scamarcio",2015,101,6.6,13.65
+646,Tall Men,"Fantasy,Horror,Thriller",Jonathan Holbrook,"Dan Crisafulli, Kay Whitney, Richard Garcia, Pat Cashman",2016,133,3.2,0
+647,Sleeping Beauty,"Drama,Romance",Julia Leigh,"Emily Browning, Rachael Blake, Ewen Leslie, Bridgette Barrett",2011,101,5.3,0.03
+648,Vampire Academy,"Action,Comedy,Fantasy",Mark Waters,"Zoey Deutch, Lucy Fry, Danila Kozlovsky, Gabriel Byrne",2014,104,5.6,7.79
+649,Sweeney Todd: The Demon Barber of Fleet Street,"Drama,Horror,Musical",Tim Burton,"Johnny Depp, Helena Bonham Carter, Alan Rickman,Timothy Spall",2007,116,7.4,52.88
+650,Solace,"Crime,Drama,Mystery",Afonso Poyart,"Anthony Hopkins, Jeffrey Dean Morgan, Abbie Cornish, Colin Farrell",2015,101,6.4,0
+651,Insidious,"Horror,Mystery,Thriller",James Wan,"Patrick Wilson, Rose Byrne, Ty Simpkins, Lin Shaye",2010,103,6.8,53.99
+652,Popstar: Never Stop Never Stopping,"Comedy,Music",Akiva Schaffer,"Andy Samberg, Jorma Taccone,Akiva Schaffer, Sarah Silverman",2016,87,6.7,9.39
+653,The Levelling,Drama,Hope Dickson Leach,"Ellie Kendrick, David Troughton, Jack Holden,Joe Blakemore",2016,83,6.4,0
+654,Public Enemies,"Biography,Crime,Drama",Michael Mann,"Christian Bale, Johnny Depp, Christian Stolte, Jason Clarke",2009,140,7,97.03
+655,Boyhood,Drama,Richard Linklater,"Ellar Coltrane, Patricia Arquette, Ethan Hawke,Elijah Smith",2014,165,7.9,25.36
+656,Teenage Mutant Ninja Turtles,"Action,Adventure,Comedy",Jonathan Liebesman,"Megan Fox, Will Arnett, William Fichtner, Noel Fisher",2014,101,5.9,190.87M
+657,Eastern Promises,"Crime,Drama,Mystery",David Cronenberg,"Naomi Watts, Viggo Mortensen, Armin Mueller-Stahl, Josef Altin",2007,100,7.7,17.11
+658,The Daughter,Drama,Simon Stone,"Geoffrey Rush, Nicholas Hope, Sam Neill, Ewen Leslie",2015,96,6.7,0.03
+659,Pineapple Express,"Action,Comedy,Crime",David Gordon Green,"Seth Rogen, James Franco, Gary Cole, Danny McBride",2008,111,7,87.34
+660,The First Time,"Comedy,Drama,Romance",Jon Kasdan,"Dylan O'Brien, Britt Robertson, Victoria Justice, James Frecheville",2012,95,6.9,0.02
+661,Gone Baby Gone,"Crime,Drama,Mystery",Ben Affleck,"Morgan Freeman, Ed Harris, Casey Affleck, Michelle Monaghan",2007,114,7.7,20.3
+662,The Heat,"Action,Comedy,Crime",Paul Feig,"Sandra Bullock, Michael McDonald, Melissa McCarthy,Demián Bichir",2013,117,6.6,159.58M
+663,L'avenir,Drama,Mia Hansen-Løve,"Isabelle Huppert, André Marcon, Roman Kolinka,Edith Scob",2016,102,7.1,0.28
+664,Anna Karenina,"Drama,Romance",Joe Wright,"Keira Knightley, Jude Law, Aaron Taylor-Johnson,Matthew Macfadyen",2012,129,6.6,12.8
+665,Regression,"Crime,Drama,Mystery",Alejandro Amenábar,"Ethan Hawke, David Thewlis, Emma Watson,Dale Dickey",2015,106,5.7,0.05
+666,Ted 2,"Adventure,Comedy,Romance",Seth MacFarlane,"Mark Wahlberg, Seth MacFarlane, Amanda Seyfried, Jessica Barth",2015,115,6.3,81.26
+667,Pain & Gain,"Comedy,Crime,Drama",Michael Bay,"Mark Wahlberg, Dwayne Johnson, Anthony Mackie,Tony Shalhoub",2013,129,6.5,49.87
+668,Blood Diamond,"Adventure,Drama,Thriller",Edward Zwick,"Leonardo DiCaprio, Djimon Hounsou, Jennifer Connelly, Kagiso Kuypers",2006,143,8,57.37
+669,Devil's Knot,"Biography,Crime,Drama",Atom Egoyan,"Colin Firth, Reese Witherspoon, Alessandro Nivola,James Hamrick",2013,114,6.1,0
+670,Child 44,"Crime,Drama,Thriller",Daniel Espinosa,"Tom Hardy, Gary Oldman, Noomi Rapace, Joel Kinnaman",2015,137,6.5,1.21
+671,The Hurt Locker,"Drama,History,Thriller",Kathryn Bigelow,"Jeremy Renner, Anthony Mackie, Brian Geraghty,Guy Pearce",2008,131,7.6,15.7
+672,Green Lantern,"Action,Adventure,Sci-Fi",Martin Campbell,"Ryan Reynolds, Blake Lively, Peter Sarsgaard,Mark Strong",2011,114,5.6,116.59M
+673,War on Everyone,"Action,Comedy",John Michael McDonagh,"Alexander Skarsgård, Michael Peña, Theo James, Tessa Thompson",2016,98,5.9,0
+674,The Mist,Horror,Frank Darabont,"Thomas Jane, Marcia Gay Harden, Laurie Holden,Andre Braugher",2007,126,7.2,25.59
+675,Escape Plan,"Action,Crime,Mystery",Mikael Håfström,"Sylvester Stallone, Arnold Schwarzenegger, 50 Cent, Vincent D'Onofrio",2013,115,6.7,25.12
+676,"Love, Rosie","Comedy,Romance",Christian Ditter,"Lily Collins, Sam Claflin, Christian Cooke, Jaime Winstone",2014,102,7.2,0.01
+677,The DUFF,Comedy,Ari Sandel,"Mae Whitman, Bella Thorne, Robbie Amell, Allison Janney",2015,101,6.5,34.02
+678,The Age of Shadows,"Action,Drama,Thriller",Jee-woon Kim,"Byung-hun Lee, Yoo Gong, Kang-ho Song, Ji-min Han",2016,140,7.2,0.54
+679,The Hunger Games: Mockingjay - Part 1,"Action,Adventure,Sci-Fi",Francis Lawrence,"Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson",2014,123,6.7,337.1
+680,We Need to Talk About Kevin,"Drama,Mystery,Thriller",Lynne Ramsay,"Tilda Swinton, John C. Reilly, Ezra Miller, Jasper Newell",2011,112,7.5,1.74
+681,Love & Friendship,"Comedy,Drama,Romance",Whit Stillman,"Kate Beckinsale, Chloë Sevigny, Xavier Samuel,Emma Greenwell",2016,90,6.5,14.01
+682,The Mortal Instruments: City of Bones,"Action,Fantasy,Horror",Harald Zwart,"Lily Collins, Jamie Campbell Bower, Robert Sheehan,Jemima West",2013,130,5.9,31.17
+683,Seven Pounds,"Drama,Romance",Gabriele Muccino,"Will Smith, Rosario Dawson, Woody Harrelson,Michael Ealy",2008,123,7.7,69.95
+684,The King's Speech,"Biography,Drama",Tom Hooper,"Colin Firth, Geoffrey Rush, Helena Bonham Carter,Derek Jacobi",2010,118,8,138.8M
+685,Hunger,"Biography,Drama",Steve McQueen,"Stuart Graham, Laine Megaw, Brian Milligan, Liam McMahon",2008,96,7.6,0.15
+686,Jumper,"Action,Adventure,Sci-Fi",Doug Liman,"Hayden Christensen, Samuel L. Jackson, Jamie Bell,Rachel Bilson",2008,88,6.1,80.17
+687,Toy Story 3,"Animation,Adventure,Comedy",Lee Unkrich,"Tom Hanks, Tim Allen, Joan Cusack, Ned Beatty",2010,103,8.3,414.98
+688,Tinker Tailor Soldier Spy,"Drama,Mystery,Thriller",Tomas Alfredson,"Gary Oldman, Colin Firth, Tom Hardy, Mark Strong",2011,122,7.1,24.1
+689,Resident Evil: Retribution,"Action,Horror,Sci-Fi",Paul W.S. Anderson,"Milla Jovovich, Sienna Guillory, Michelle Rodriguez, Aryana Engineer",2012,96,5.4,42.35
+690,Dear Zindagi,"Drama,Romance",Gauri Shinde,"Alia Bhatt, Shah Rukh Khan, Kunal Kapoor, Priyanka Moodley",2016,151,7.8,1.4
+691,Genius,"Biography,Drama",Michael Grandage,"Colin Firth, Jude Law, Nicole Kidman, Laura Linney",2016,104,6.5,1.36
+692,Pompeii,"Action,Adventure,Drama",Paul W.S. Anderson,"Kit Harington, Emily Browning, Kiefer Sutherland, Adewale Akinnuoye-Agbaje",2014,105,5.5,23.22
+693,Life of Pi,"Adventure,Drama,Fantasy",Ang Lee,"Suraj Sharma, Irrfan Khan, Adil Hussain, Tabu",2012,127,7.9,124.98M
+694,Hachi: A Dog's Tale,"Drama,Family",Lasse Hallström,"Richard Gere, Joan Allen, Cary-Hiroyuki Tagawa,Sarah Roemer",2009,93,8.1,0
+695,10 Years,"Comedy,Drama,Romance",Jamie Linden,"Channing Tatum, Rosario Dawson, Chris Pratt, Jenna Dewan Tatum",2011,100,6.1,0.2
+696,I Origins,"Drama,Romance,Sci-Fi",Mike Cahill,"Michael Pitt, Steven Yeun, Astrid Bergès-Frisbey, Brit Marling",2014,106,7.3,0.33
+697,Live Free or Die Hard,"Action,Adventure,Thriller",Len Wiseman,"Bruce Willis, Justin Long, Timothy Olyphant, Maggie Q",2007,128,7.2,134.52M
+698,The Matchbreaker,"Comedy,Romance",Caleb Vetter,"Wesley Elder, Christina Grimmie, Osric Chau, Olan Rogers",2016,94,5.5,0
+699,Funny Games,"Crime,Drama,Horror",Michael Haneke,"Naomi Watts, Tim Roth, Michael Pitt, Brady Corbet",2007,111,6.5,1.29
+700,Ted,"Comedy,Fantasy",Seth MacFarlane,"Mark Wahlberg, Mila Kunis, Seth MacFarlane, Joel McHale",2012,106,7,218.63
+701,RED,"Action,Comedy,Crime",Robert Schwentke,"Bruce Willis, Helen Mirren, Morgan Freeman,Mary-Louise Parker",2010,111,7.1,90.36
+702,Australia,"Adventure,Drama,Romance",Baz Luhrmann,"Nicole Kidman, Hugh Jackman, Shea Adams, Eddie Baroo",2008,165,6.6,49.55
+703,Faster,"Action,Crime,Drama",George Tillman Jr.,"Dwayne Johnson, Billy Bob Thornton, Maggie Grace, Mauricio Lopez",2010,98,6.5,23.23
+704,The Neighbor,"Crime,Horror,Thriller",Marcus Dunstan,"Josh Stewart, Bill Engvall, Alex Essoe, Ronnie Gene Blevins",2016,87,5.8,0
+705,The Adjustment Bureau,"Romance,Sci-Fi,Thriller",George Nolfi,"Matt Damon, Emily Blunt, Lisa Thoreson, Florence Kastriner",2011,106,7.1,62.45
+706,The Hollars,"Comedy,Drama,Romance",John Krasinski,"Sharlto Copley, Charlie Day, Richard Jenkins, Anna Kendrick",2016,88,6.5,1.02
+707,The Judge,"Crime,Drama",David Dobkin,"Robert Downey Jr., Robert Duvall, Vera Farmiga, Billy Bob Thornton",2014,141,7.4,47.11
+708,Closed Circuit,"Crime,Drama,Mystery",John Crowley,"Eric Bana, Rebecca Hall, Jim Broadbent, Ciarán Hinds",2013,96,6.2,5.73
+709,Transformers: Revenge of the Fallen,"Action,Adventure,Sci-Fi",Michael Bay,"Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson",2009,150,6,402.08
+710,La tortue rouge,"Animation,Fantasy",Michael Dudok de Wit,"Emmanuel Garijo, Tom Hudson, Baptiste Goy, Axel Devillers",2016,80,7.6,0.92
+711,The Book of Life,"Animation,Adventure,Comedy",Jorge R. Gutiérrez,"Diego Luna, Zoe Saldana, Channing Tatum, Ron Perlman",2014,95,7.3,50.15
+712,Incendies,"Drama,Mystery,War",Denis Villeneuve,"Lubna Azabal, Mélissa Désormeaux-Poulin, Maxim Gaudette, Mustafa Kamel",2010,131,8.2,6.86
+713,The Heartbreak Kid,"Comedy,Romance",Bobby Farrelly,"Ben Stiller, Michelle Monaghan,Malin Akerman, Jerry Stiller",2007,116,5.8,36.77
+714,Happy Feet,"Animation,Comedy,Family",George Miller,"Elijah Wood, Brittany Murphy, Hugh Jackman, Robin Williams",2006,108,6.5,197.99M
+715,Entourage,Comedy,Doug Ellin,"Adrian Grenier, Kevin Connolly, Jerry Ferrara, Kevin Dillon",2015,104,6.6,32.36
+716,The Strangers,"Horror,Mystery,Thriller",Bryan Bertino,"Scott Speedman, Liv Tyler, Gemma Ward, Alex Fisher",2008,86,6.2,52.53
+717,Noah,"Action,Adventure,Drama",Darren Aronofsky,"Russell Crowe, Jennifer Connelly, Anthony Hopkins, Emma Watson",2014,138,5.8,101.16M
+718,Neighbors,Comedy,Nicholas Stoller,"Seth Rogen, Rose Byrne, Zac Efron, Lisa Kudrow",2014,97,6.4,150.06M
+719,Nymphomaniac: Vol. II,Drama,Lars von Trier,"Charlotte Gainsbourg, Stellan Skarsgård, Willem Dafoe, Jamie Bell",2013,123,6.7,0.33
+720,Wild,"Adventure,Biography,Drama",Jean-Marc Vallée,"Reese Witherspoon, Laura Dern, Gaby Hoffmann,Michiel Huisman",2014,115,7.1,37.88
+721,Grown Ups,Comedy,Dennis Dugan,"Adam Sandler, Salma Hayek, Kevin James, Chris Rock",2010,102,6,162M
+722,Blair Witch,"Horror,Thriller",Adam Wingard,"James Allen McCune, Callie Hernandez, Corbin Reid, Brandon Scott",2016,89,5.1,20.75
+723,The Karate Kid,"Action,Drama,Family",Harald Zwart,"Jackie Chan, Jaden Smith, Taraji P. Henson, Wenwen Han",2010,140,6.2,176.59M
+724,Dark Shadows,"Comedy,Fantasy,Horror",Tim Burton,"Johnny Depp, Michelle Pfeiffer, Eva Green, Helena Bonham Carter",2012,113,6.2,79.71
+725,Friends with Benefits,"Comedy,Romance",Will Gluck,"Mila Kunis, Justin Timberlake, Patricia Clarkson, Jenna Elfman",2011,109,6.6,55.8
+726,The Illusionist,"Drama,Mystery,Romance",Neil Burger,"Edward Norton, Jessica Biel, Paul Giamatti, Rufus Sewell",2006,110,7.6,39.83
+727,The A-Team,"Action,Adventure,Comedy",Joe Carnahan,"Liam Neeson, Bradley Cooper, Sharlto Copley,Jessica Biel",2010,117,6.8,77.21
+728,The Guest,Thriller,Adam Wingard,"Dan Stevens, Sheila Kelley, Maika Monroe, Joel David Moore",2014,100,6.7,0.32
+729,The Internship,Comedy,Shawn Levy,"Vince Vaughn, Owen Wilson, Rose Byrne, Aasif Mandvi",2013,119,6.3,44.67
+730,Paul,"Adventure,Comedy,Sci-Fi",Greg Mottola,"Simon Pegg, Nick Frost, Seth Rogen, Mia Stallard",2011,104,7,37.37
+731,This Beautiful Fantastic,"Comedy,Drama,Fantasy",Simon Aboud,"Jessica Brown Findlay, Andrew Scott, Jeremy Irvine,Tom Wilkinson",2016,100,6.9,0
+732,The Da Vinci Code,"Mystery,Thriller",Ron Howard,"Tom Hanks, Audrey Tautou, Jean Reno, Ian McKellen",2006,149,6.6,217.54
+733,Mr. Church,"Comedy,Drama",Bruce Beresford,"Eddie Murphy, Britt Robertson, Natascha McElhone, Xavier Samuel",2016,104,7.7,0.69
+734,Hugo,"Adventure,Drama,Family",Martin Scorsese,"Asa Butterfield, Chloë Grace Moretz, Christopher Lee, Ben Kingsley",2011,126,7.5,73.82
+735,The Blackcoat's Daughter,"Horror,Thriller",Oz Perkins,"Emma Roberts, Kiernan Shipka, Lauren Holly, Lucy Boynton",2015,93,5.6,0.02
+736,Body of Lies,"Action,Drama,Romance",Ridley Scott,"Leonardo DiCaprio, Russell Crowe, Mark Strong,Golshifteh Farahani",2008,128,7.1,39.38
+737,Knight of Cups,"Drama,Romance",Terrence Malick,"Christian Bale, Cate Blanchett, Natalie Portman,Brian Dennehy",2015,118,5.7,0.56
+738,The Mummy: Tomb of the Dragon Emperor,"Action,Adventure,Fantasy",Rob Cohen,"Brendan Fraser, Jet Li, Maria Bello, Michelle Yeoh",2008,112,5.2,102.18M
+739,The Boss,Comedy,Ben Falcone,"Melissa McCarthy, Kristen Bell, Peter Dinklage, Ella Anderson",2016,99,5.4,63.03
+740,Hands of Stone,"Action,Biography,Drama",Jonathan Jakubowicz,"Edgar Ramírez, Usher Raymond, Robert De Niro, Rubén Blades",2016,111,6.6,4.71
+741,El secreto de sus ojos,"Drama,Mystery,Romance",Juan José Campanella,"Ricardo Darín, Soledad Villamil, Pablo Rago,Carla Quevedo",2009,129,8.2,20.17
+742,True Grit,"Adventure,Drama,Western",Ethan Coen,"Jeff Bridges, Matt Damon, Hailee Steinfeld,Josh Brolin",2010,110,7.6,171.03M
+743,We Are Your Friends,"Drama,Music,Romance",Max Joseph,"Zac Efron, Wes Bentley, Emily Ratajkowski, Jonny Weston",2015,96,6.2,3.59
+744,A Million Ways to Die in the West,"Comedy,Romance,Western",Seth MacFarlane,"Seth MacFarlane, Charlize Theron, Liam Neeson,Amanda Seyfried",2014,116,6.1,42.62
+745,Only for One Night,Thriller,Chris Stokes,"Brian White, Karrueche Tran, Angelique Pereira,Jessica Vanessa DeLeon",2016,86,4.6,0
+746,Rules Don't Apply,"Comedy,Drama,Romance",Warren Beatty,"Lily Collins, Haley Bennett, Taissa Farmiga, Steve Tom",2016,127,5.7,3.65
+747,Ouija: Origin of Evil,"Horror,Thriller",Mike Flanagan,"Elizabeth Reaser, Lulu Wilson, Annalise Basso,Henry Thomas",2016,99,6.1,34.9
+748,Percy Jackson: Sea of Monsters,"Adventure,Family,Fantasy",Thor Freudenthal,"Logan Lerman, Alexandra Daddario, Brandon T. Jackson, Nathan Fillion",2013,106,5.9,68.56
+749,Fracture,"Crime,Drama,Mystery",Gregory Hoblit,"Anthony Hopkins, Ryan Gosling, David Strathairn,Rosamund Pike",2007,113,7.2,39
+750,Oculus,"Horror,Mystery",Mike Flanagan,"Karen Gillan, Brenton Thwaites, Katee Sackhoff,Rory Cochrane",2013,104,6.5,27.69
+751,In Bruges,"Comedy,Crime,Drama",Martin McDonagh,"Colin Farrell, Brendan Gleeson, Ciarán Hinds,Elizabeth Berrington",2008,107,7.9,7.76
+752,This Means War,"Action,Comedy,Romance",McG,"Reese Witherspoon, Chris Pine, Tom Hardy, Til Schweiger",2012,103,6.3,54.76
+753,Lída Baarová,"Biography,Drama,History",Filip Renc,"Tatiana Pauhofová, Karl Markovics, Gedeon Burkhard,Simona Stasová",2016,106,5,0
+754,The Road,"Adventure,Drama",John Hillcoat,"Viggo Mortensen, Charlize Theron, Kodi Smit-McPhee,Robert Duvall",2009,111,7.3,0.06
+755,Lavender,"Drama,Thriller",Ed Gass-Donnelly,"Abbie Cornish, Dermot Mulroney, Justin Long,Diego Klattenhoff",2016,92,5.2,0
+756,Deuces,Drama,Jamal Hill,"Larenz Tate, Meagan Good, Rotimi, Rick Gonzalez",2016,87,6.6,0
+757,Conan the Barbarian,"Action,Adventure,Fantasy",Marcus Nispel,"Jason Momoa, Ron Perlman, Rose McGowan,Stephen Lang",2011,113,5.2,21.27
+758,The Fighter,"Action,Biography,Drama",David O. Russell,"Mark Wahlberg, Christian Bale, Amy Adams,Melissa Leo",2010,116,7.8,93.57
+759,August Rush,"Drama,Music",Kirsten Sheridan,"Freddie Highmore, Keri Russell, Jonathan Rhys Meyers, Terrence Howard",2007,114,7.5,31.66
+760,Chef,"Comedy,Drama",Jon Favreau,"Jon Favreau, Robert Downey Jr., Scarlett Johansson,Dustin Hoffman",2014,114,7.3,31.24
+761,Eye in the Sky,"Drama,Thriller,War",Gavin Hood,"Helen Mirren, Aaron Paul, Alan Rickman, Barkhad Abdi",2015,102,7.3,18.7
+762,Eagle Eye,"Action,Mystery,Thriller",D.J. Caruso,"Shia LaBeouf, Michelle Monaghan, Rosario Dawson,Michael Chiklis",2008,118,6.6,101.11M
+763,The Purge,"Horror,Sci-Fi,Thriller",James DeMonaco,"Ethan Hawke, Lena Headey, Max Burkholder,Adelaide Kane",2013,85,5.7,64.42
+764,PK,"Comedy,Drama,Romance",Rajkumar Hirani,"Aamir Khan, Anushka Sharma, Sanjay Dutt,Boman Irani",2014,153,8.2,10.57
+765,Ender's Game,"Action,Sci-Fi",Gavin Hood,"Harrison Ford, Asa Butterfield, Hailee Steinfeld, Abigail Breslin",2013,114,6.7,61.66
+766,Indiana Jones and the Kingdom of the Crystal Skull,"Action,Adventure,Fantasy",Steven Spielberg,"Harrison Ford, Cate Blanchett, Shia LaBeouf,Karen Allen",2008,122,6.2,317.01
+767,Paper Towns,"Drama,Mystery,Romance",Jake Schreier,"Nat Wolff, Cara Delevingne, Austin Abrams, Justice Smith",2015,109,6.3,31.99
+768,High-Rise,Drama,Ben Wheatley,"Tom Hiddleston, Jeremy Irons, Sienna Miller, Luke Evans",2015,119,5.7,0.34
+769,Quantum of Solace,"Action,Adventure,Thriller",Marc Forster,"Daniel Craig, Olga Kurylenko, Mathieu Amalric, Judi Dench",2008,106,6.6,168.37M
+770,The Assignment,"Action,Crime,Thriller",Walter Hill,"Sigourney Weaver, Michelle Rodriguez, Tony Shalhoub,Anthony LaPaglia",2016,95,4.5,0
+771,How to Train Your Dragon,"Animation,Action,Adventure",Dean DeBlois,"Jay Baruchel, Gerard Butler,Christopher Mintz-Plasse, Craig Ferguson",2010,98,8.1,217.39
+772,Lady in the Water,"Drama,Fantasy,Mystery",M. Night Shyamalan,"Paul Giamatti, Bryce Dallas Howard, Jeffrey Wright, Bob Balaban",2006,110,5.6,42.27
+773,The Fountain,"Drama,Sci-Fi",Darren Aronofsky,"Hugh Jackman, Rachel Weisz, Sean Patrick Thomas, Ellen Burstyn",2006,96,7.3,10.14
+774,Cars 2,"Animation,Adventure,Comedy",John Lasseter,"Owen Wilson, Larry the Cable Guy,Michael Caine, Emily Mortimer",2011,106,6.2,191.45M
+775,31,"Horror,Thriller",Rob Zombie,"Malcolm McDowell, Richard Brake, Jeff Daniel Phillips,Sheri Moon Zombie",2016,102,5.1,0.78
+776,Final Girl,"Action,Thriller",Tyler Shields,"Abigail Breslin, Wes Bentley, Logan Huffman,Alexander Ludwig",2015,90,4.7,0
+777,Chalk It Up,Comedy,Hisonni Johnson,"Maddy Curley, John DeLuca, Nikki SooHoo, Drew Seeley",2016,90,4.8,0
+778,The Man Who Knew Infinity,"Biography,Drama",Matt Brown,"Dev Patel, Jeremy Irons, Malcolm Sinclair, Raghuvir Joshi",2015,108,7.2,3.86
+779,Unknown,"Action,Mystery,Thriller",Jaume Collet-Serra,"Liam Neeson, Diane Kruger, January Jones,Aidan Quinn",2011,113,6.9,61.09
+780,Self/less,"Action,Mystery,Sci-Fi",Tarsem Singh,"Ryan Reynolds, Natalie Martinez, Matthew Goode,Ben Kingsley",2015,117,6.5,12.28
+781,Mr. Brooks,"Crime,Drama,Thriller",Bruce A. Evans,"Kevin Costner, Demi Moore, William Hurt, Dane Cook",2007,120,7.3,28.48
+782,Tramps,"Comedy,Romance",Adam Leon,"Callum Turner, Grace Van Patten, Michal Vondel, Mike Birbiglia",2016,82,6.5,0
+783,Before We Go,"Comedy,Drama,Romance",Chris Evans,"Chris Evans, Alice Eve, Emma Fitzpatrick, John Cullum",2014,95,6.9,0.04
+784,Captain Phillips,"Biography,Drama,Thriller",Paul Greengrass,"Tom Hanks, Barkhad Abdi, Barkhad Abdirahman,Catherine Keener",2013,134,7.8,107.1M
+785,The Secret Scripture,Drama,Jim Sheridan,"Rooney Mara, Eric Bana, Theo James, Aidan Turner",2016,108,6.8,0
+786,Max Steel,"Action,Adventure,Family",Stewart Hendler,"Ben Winchell, Josh Brener, Maria Bello, Andy Garcia",2016,92,4.6,3.77
+787,Hotel Transylvania 2,"Animation,Comedy,Family",Genndy Tartakovsky,"Adam Sandler, Andy Samberg, Selena Gomez, Kevin James",2015,89,6.7,169.69M
+788,Hancock,"Action,Crime,Drama",Peter Berg,"Will Smith, Charlize Theron, Jason Bateman, Jae Head",2008,92,6.4,227.95
+789,Sisters,Comedy,Jason Moore,"Amy Poehler, Tina Fey, Maya Rudolph, Ike Barinholtz",2015,118,6,87.03
+790,The Family,"Comedy,Crime,Thriller",Luc Besson,"Robert De Niro, Michelle Pfeiffer, Dianna Agron, John D'Leo",2013,111,6.3,36.92
+791,Zack and Miri Make a Porno,"Comedy,Romance",Kevin Smith,"Seth Rogen, Elizabeth Banks, Craig Robinson, Gerry Bednob",2008,101,6.6,31.45
+792,Ma vie de Courgette,"Animation,Comedy,Drama",Claude Barras,"Gaspard Schlatter, Sixtine Murat, Paulin Jaccoud,Michel Vuillermoz",2016,66,7.8,0.29
+793,Man on a Ledge,"Action,Crime,Thriller",Asger Leth,"Sam Worthington, Elizabeth Banks, Jamie Bell, Mandy Gonzalez",2012,102,6.6,18.6
+794,No Strings Attached,"Comedy,Romance",Ivan Reitman,"Natalie Portman, Ashton Kutcher, Kevin Kline, Cary Elwes",2011,108,6.2,70.63
+795,Rescue Dawn,"Adventure,Biography,Drama",Werner Herzog,"Christian Bale, Steve Zahn, Jeremy Davies, Zach Grenier",2006,120,7.3,5.48
+796,Despicable Me 2,"Animation,Adventure,Comedy",Pierre Coffin,"Steve Carell, Kristen Wiig, Benjamin Bratt, Miranda Cosgrove",2013,98,7.4,368.05
+797,A Walk Among the Tombstones,"Crime,Drama,Mystery",Scott Frank,"Liam Neeson, Dan Stevens, David Harbour, Boyd Holbrook",2014,114,6.5,25.98
+798,The World's End,"Action,Comedy,Sci-Fi",Edgar Wright,"Simon Pegg, Nick Frost, Martin Freeman, Rosamund Pike",2013,109,7,26
+799,Yoga Hosers,"Comedy,Fantasy,Horror",Kevin Smith,"Lily-Rose Depp, Harley Quinn Smith, Johnny Depp,Adam Brody",2016,88,4.3,0
+800,Seven Psychopaths,"Comedy,Crime",Martin McDonagh,"Colin Farrell, Woody Harrelson, Sam Rockwell,Christopher Walken",2012,110,7.2,14.99
+801,Beowulf,"Animation,Action,Adventure",Robert Zemeckis,"Ray Winstone, Crispin Glover, Angelina Jolie,Robin Wright",2007,115,6.2,82.16
+802,Jack Ryan: Shadow Recruit,"Action,Drama,Thriller",Kenneth Branagh,"Chris Pine, Kevin Costner, Keira Knightley,Kenneth Branagh",2014,105,6.2,50.55
+803,1408,"Fantasy,Horror",Mikael Håfström,"John Cusack, Samuel L. Jackson, Mary McCormack, Paul Birchard",2007,104,6.8,71.98
+804,The Gambler,"Crime,Drama,Thriller",Rupert Wyatt,"Mark Wahlberg, Jessica Lange, John Goodman, Brie Larson",2014,111,6,33.63
+805,Prince of Persia: The Sands of Time,"Action,Adventure,Fantasy",Mike Newell,"Jake Gyllenhaal, Gemma Arterton, Ben Kingsley,Alfred Molina",2010,116,6.6,90.76
+806,The Spectacular Now,"Comedy,Drama,Romance",James Ponsoldt,"Miles Teller, Shailene Woodley, Kyle Chandler,Jennifer Jason Leigh",2013,95,7.1,6.85
+807,A United Kingdom,"Biography,Drama,Romance",Amma Asante,"David Oyelowo, Rosamund Pike, Tom Felton, Jack Davenport",2016,111,6.8,3.9
+808,USS Indianapolis: Men of Courage,"Action,Drama,History",Mario Van Peebles,"Nicolas Cage, Tom Sizemore, Thomas Jane,Matt Lanter",2016,128,5.2,0
+809,Turbo Kid,"Action,Adventure,Comedy",François Simard,"Munro Chambers, Laurence Leboeuf, Michael Ironside, Edwin Wright",2015,93,6.7,0.05
+810,Mama,"Horror,Thriller",Andrés Muschietti,"Jessica Chastain, Nikolaj Coster-Waldau, Megan Charpentier, Isabelle Nélisse",2013,100,6.2,71.59
+811,Orphan,"Horror,Mystery,Thriller",Jaume Collet-Serra,"Vera Farmiga, Peter Sarsgaard, Isabelle Fuhrman, CCH Pounder",2009,123,7,41.57
+812,To Rome with Love,"Comedy,Romance",Woody Allen,"Woody Allen, Penélope Cruz, Jesse Eisenberg, Ellen Page",2012,112,6.3,16.68
+813,Fantastic Mr. Fox,"Animation,Adventure,Comedy",Wes Anderson,"George Clooney, Meryl Streep, Bill Murray, Jason Schwartzman",2009,87,7.8,21
+814,Inside Man,"Crime,Drama,Mystery",Spike Lee,"Denzel Washington, Clive Owen, Jodie Foster,Christopher Plummer",2006,129,7.6,88.5
+815,I.T.,"Crime,Drama,Mystery",John Moore,"Pierce Brosnan, Jason Barry, Karen Moskow, Kai Ryssdal",2016,95,5.4,0
+816,127 Hours,"Adventure,Biography,Drama",Danny Boyle,"James Franco, Amber Tamblyn, Kate Mara, Sean Bott",2010,94,7.6,18.33
+817,Annabelle,"Horror,Mystery,Thriller",John R. Leonetti,"Ward Horton, Annabelle Wallis, Alfre Woodard,Tony Amendola",2014,99,5.4,84.26
+818,Wolves at the Door,"Horror,Thriller",John R. Leonetti,"Katie Cassidy, Elizabeth Henstridge, Adam Campbell, Miles Fisher",2016,73,4.6,0
+819,Suite Française,"Drama,Romance,War",Saul Dibb,"Michelle Williams, Kristin Scott Thomas, Margot Robbie,Eric Godon",2014,107,6.9,0
+820,The Imaginarium of Doctor Parnassus,"Adventure,Fantasy,Mystery",Terry Gilliam,"Christopher Plummer, Lily Cole, Heath Ledger,Andrew Garfield",2009,123,6.8,7.69
+821,G.I. Joe: The Rise of Cobra,"Action,Adventure,Sci-Fi",Stephen Sommers,"Dennis Quaid, Channing Tatum, Marlon Wayans,Adewale Akinnuoye-Agbaje",2009,118,5.8,150.17M
+822,Christine,"Biography,Drama",Antonio Campos,"Rebecca Hall, Michael C. Hall, Tracy Letts, Maria Dizzia",2016,119,7,0.3
+823,Man Down,"Drama,Thriller",Dito Montiel,"Shia LaBeouf, Jai Courtney, Gary Oldman, Kate Mara",2015,90,5.8,0
+824,Crawlspace,"Horror,Thriller",Phil Claydon,"Michael Vartan, Erin Moriarty, Nadine Velazquez,Ronnie Gene Blevins",2016,88,5.3,0
+825,Shut In,"Drama,Horror,Thriller",Farren Blackburn,"Naomi Watts, Charlie Heaton, Jacob Tremblay,Oliver Platt",2016,91,4.6,6.88
+826,The Warriors Gate,"Action,Adventure,Fantasy",Matthias Hoene,"Mark Chao, Ni Ni, Dave Bautista, Sienna Guillory",2016,108,5.3,0
+827,Grindhouse,"Action,Horror,Thriller",Robert Rodriguez,"Kurt Russell, Rose McGowan, Danny Trejo, Zoë Bell",2007,191,7.6,25.03
+828,Disaster Movie,Comedy,Jason Friedberg,"Carmen Electra, Vanessa Lachey,Nicole Parker, Matt Lanter",2008,87,1.9,14.17
+829,Rocky Balboa,"Drama,Sport",Sylvester Stallone,"Sylvester Stallone, Antonio Tarver, Milo Ventimiglia, Burt Young",2006,102,7.2,70.27
+830,Diary of a Wimpy Kid: Dog Days,"Comedy,Family",David Bowers,"Zachary Gordon, Robert Capron, Devon Bostick,Steve Zahn",2012,94,6.4,49
+831,Jane Eyre,"Drama,Romance",Cary Joji Fukunaga,"Mia Wasikowska, Michael Fassbender, Jamie Bell, Su Elliot",2011,120,7.4,11.23
+832,Fool's Gold,"Action,Adventure,Comedy",Andy Tennant,"Matthew McConaughey, Kate Hudson, Donald Sutherland, Alexis Dziena",2008,112,5.7,70.22
+833,The Dictator,Comedy,Larry Charles,"Sacha Baron Cohen, Anna Faris, John C. Reilly, Ben Kingsley",2012,83,6.4,59.62
+834,The Loft,"Mystery,Romance,Thriller",Erik Van Looy,"Karl Urban, James Marsden, Wentworth Miller, Eric Stonestreet",2014,108,6.3,5.98
+835,Bacalaureat,"Crime,Drama",Cristian Mungiu,"Adrian Titieni, Maria-Victoria Dragus, Lia Bugnar,Malina Manovici",2016,128,7.5,0.13
+836,You Don't Mess with the Zohan,"Action,Comedy",Dennis Dugan,"Adam Sandler, John Turturro, Emmanuelle Chriqui,Nick Swardson",2008,113,5.5,100.02M
+837,Exposed,"Crime,Drama,Mystery",Gee Malik Linton,"Ana de Armas, Keanu Reeves, Christopher McDonald, Mira Sorvino",2016,102,4.2,0
+838,Maudie,"Biography,Drama,Romance",Aisling Walsh,"Ethan Hawke, Sally Hawkins, Kari Matchett, Zachary Bennett",2016,115,7.8,0
+839,Horrible Bosses 2,"Comedy,Crime",Sean Anders,"Jason Bateman, Jason Sudeikis, Charlie Day, Jennifer Aniston",2014,108,6.3,54.41
+840,A Bigger Splash,"Drama,Thriller",Luca Guadagnino,"Tilda Swinton, Matthias Schoenaerts, Ralph Fiennes, Dakota Johnson",2015,125,6.4,1.98
+841,Melancholia,Drama,Lars von Trier,"Kirsten Dunst, Charlotte Gainsbourg, Kiefer Sutherland, Alexander Skarsgård",2011,135,7.1,3.03
+842,The Princess and the Frog,"Animation,Adventure,Comedy",Ron Clements,"Anika Noni Rose, Keith David, Oprah Winfrey, Bruno Campos",2009,97,7.1,104.37M
+843,Unstoppable,"Action,Thriller",Tony Scott,"Denzel Washington, Chris Pine, Rosario Dawson, Ethan Suplee",2010,98,6.8,81.56
+844,Flight,"Drama,Thriller",Robert Zemeckis,"Denzel           Washington, Nadine Velazquez, Don Cheadle, John Goodman",2012,138,7.3,93.75
+845,Home,"Animation,Adventure,Comedy",Tim Johnson,"Jim Parsons, Rihanna, Steve Martin, Jennifer Lopez",2015,94,6.7,177.34M
+846,La migliore offerta,"Crime,Drama,Mystery",Giuseppe Tornatore,"Geoffrey Rush, Jim Sturgess, Sylvia Hoeks,Donald Sutherland",2013,131,7.8,0.09
+847,Mean Dreams,Thriller,Nathan Morlando,"Sophie Nélisse, Josh Wiggins, Joe Cobden, Bill Paxton",2016,108,6.3,0
+848,42,"Biography,Drama,Sport",Brian Helgeland,"Chadwick Boseman, T.R. Knight, Harrison Ford,Nicole Beharie",2013,128,7.5,95
+849,21,"Crime,Drama,Thriller",Robert Luketic,"Jim Sturgess, Kate Bosworth, Kevin Spacey, Aaron Yoo",2008,123,6.8,81.16
+850,Begin Again,"Drama,Music",John Carney,"Keira Knightley, Mark Ruffalo, Adam Levine, Hailee Steinfeld",2013,104,7.4,16.17
+851,Out of the Furnace,"Crime,Drama,Thriller",Scott Cooper,"Christian Bale, Casey Affleck, Zoe Saldana, Woody Harrelson",2013,116,6.8,11.33
+852,Vicky Cristina Barcelona,"Drama,Romance",Woody Allen,"Rebecca Hall, Scarlett Johansson, Javier Bardem,Christopher Evan Welch",2008,96,7.1,23.21
+853,Kung Fu Panda,"Animation,Action,Adventure",Mark Osborne,"Jack Black, Ian McShane,Angelina Jolie, Dustin Hoffman",2008,92,7.6,215.4
+854,Barbershop: The Next Cut,"Comedy,Drama",Malcolm D. Lee,"Ice Cube, Regina Hall, Anthony Anderson, Eve",2016,111,5.9,54.01
+855,Terminator Salvation,"Action,Adventure,Drama",McG,"Christian Bale, Sam Worthington, Anton Yelchin, Moon Bloodgood",2009,115,6.6,125.32M
+856,Freedom Writers,"Biography,Crime,Drama",Richard LaGravenese,"Hilary Swank, Imelda Staunton, Patrick Dempsey, Scott Glenn",2007,123,7.5,36.58
+857,The Hills Have Eyes,Horror,Alexandre Aja,"Ted Levine, Kathleen Quinlan, Dan Byrd, Emilie de Ravin",2006,107,6.4,41.78
+858,Changeling,"Biography,Drama,Mystery",Clint Eastwood,"Angelina Jolie, Colm Feore, Amy Ryan, Gattlin Griffith",2008,141,7.8,35.71
+859,Remember Me,"Drama,Romance",Allen Coulter,"Robert Pattinson, Emilie de Ravin, Caitlyn Rund,Moisés Acevedo",2010,113,7.2,19.06
+860,Koe no katachi,"Animation,Drama,Romance",Naoko Yamada,"Miyu Irino, Saori Hayami, Aoi Yuki, Kenshô Ono",2016,129,8.4,0
+861,"Alexander and the Terrible, Horrible, No Good, Very Bad Day","Comedy,Family",Miguel Arteta,"Steve Carell, Jennifer Garner, Ed Oxenbould, Dylan Minnette",2014,81,6.2,66.95
+862,Locke,Drama,Steven Knight,"Tom Hardy, Olivia Colman, Ruth Wilson, Andrew Scott",2013,85,7.1,1.36
+863,The 9th Life of Louis Drax,"Mystery,Thriller",Alexandre Aja,"Jamie Dornan, Aiden Longworth, Sarah Gadon,Aaron Paul",2016,108,6.3,0
+864,Horns,"Drama,Fantasy,Horror",Alexandre Aja,"Daniel Radcliffe, Juno Temple, Max Minghella, Joe Anderson",2013,120,6.5,0.16
+865,Indignation,"Drama,Romance",James Schamus,"Logan Lerman, Sarah Gadon, Tijuana Ricks, Sue Dahlman",2016,110,6.9,3.4
+866,The Stanford Prison Experiment,"Biography,Drama,History",Kyle Patrick Alvarez,"Ezra Miller, Tye Sheridan, Billy Crudup, Olivia Thirlby",2015,122,6.9,0.64
+867,Diary of a Wimpy Kid: Rodrick Rules,"Comedy,Family",David Bowers,"Zachary Gordon, Devon Bostick, Robert Capron,Rachael Harris",2011,99,6.6,52.69
+868,Mission: Impossible III,"Action,Adventure,Thriller",J.J. Abrams,"Tom Cruise, Michelle Monaghan, Ving Rhames, Philip Seymour Hoffman",2006,126,6.9,133.38M
+869,En man som heter Ove,"Comedy,Drama",Hannes Holm,"Rolf Lassgård, Bahar Pars, Filip Berg, Ida Engvoll",2015,116,7.7,3.36
+870,Dragonball Evolution,"Action,Adventure,Fantasy",James Wong,"Justin Chatwin, James Marsters, Yun-Fat Chow, Emmy Rossum",2009,85,2.7,9.35
+871,Red Dawn,"Action,Thriller",Dan Bradley,"Chris Hemsworth, Isabel Lucas, Josh Hutcherson, Josh Peck",2012,93,5.4,44.8
+872,One Day,"Drama,Romance",Lone Scherfig,"Anne Hathaway, Jim Sturgess, Patricia Clarkson,Tom Mison",2011,107,7,13.77
+873,Life as We Know It,"Comedy,Drama,Romance",Greg Berlanti,"Katherine Heigl, Josh Duhamel, Josh Lucas, Alexis Clagett",2010,114,6.6,53.36
+874,28 Weeks Later,"Drama,Horror,Sci-Fi",Juan Carlos Fresnadillo,"Jeremy Renner, Rose Byrne, Robert Carlyle, Harold Perrineau",2007,100,7,28.64
+875,Warm Bodies,"Comedy,Horror,Romance",Jonathan Levine,"Nicholas Hoult, Teresa Palmer, John Malkovich,Analeigh Tipton",2013,98,6.9,66.36
+876,Blue Jasmine,Drama,Woody Allen,"Cate Blanchett, Alec Baldwin, Peter Sarsgaard, Sally Hawkins",2013,98,7.3,33.4
+877,Wrath of the Titans,"Action,Adventure,Fantasy",Jonathan Liebesman,"Sam Worthington, Liam Neeson, Rosamund Pike, Ralph Fiennes",2012,99,5.8,83.64
+878,Shin Gojira,"Action,Adventure,Drama",Hideaki Anno,"Hiroki Hasegawa, Yutaka Takenouchi,Satomi Ishihara, Ren Ôsugi",2016,120,6.9,1.91
+879,Saving Mr. Banks,"Biography,Comedy,Drama",John Lee Hancock,"Emma Thompson, Tom Hanks, Annie Rose Buckley, Colin Farrell",2013,125,7.5,83.3
+880,Transcendence,"Drama,Mystery,Romance",Wally Pfister,"Johnny Depp, Rebecca Hall, Morgan Freeman, Cillian Murphy",2014,119,6.3,23.01
+881,Rio,"Animation,Adventure,Comedy",Carlos Saldanha,"Jesse Eisenberg, Anne Hathaway, George Lopez,Karen Disher",2011,96,6.9,143.62M
+882,Equals,"Drama,Romance,Sci-Fi",Drake Doremus,"Nicholas Hoult, Kristen Stewart, Vernetta Lopez,Scott Lawrence",2015,101,6.1,0.03
+883,Babel,Drama,Alejandro González Iñárritu,"Brad Pitt, Cate Blanchett, Gael García Bernal, Mohamed Akhzam",2006,143,7.5,34.3
+884,The Tree of Life,"Drama,Fantasy",Terrence Malick,"Brad Pitt, Sean Penn, Jessica Chastain, Hunter McCracken",2011,139,6.8,13.3
+885,The Lucky One,"Drama,Romance",Scott Hicks,"Zac Efron, Taylor Schilling, Blythe Danner, Riley Thomas Stewart",2012,101,6.5,60.44
+886,Piranha 3D,"Comedy,Horror,Thriller",Alexandre Aja,"Elisabeth Shue, Jerry O'Connell, Richard Dreyfuss,Ving Rhames",2010,88,5.5,25
+887,50/50,"Comedy,Drama,Romance",Jonathan Levine,"Joseph Gordon-Levitt, Seth Rogen, Anna Kendrick, Bryce Dallas Howard",2011,100,7.7,34.96
+888,The Intent,"Crime,Drama",Femi Oyeniran,"Dylan Duffus, Scorcher,Shone Romulus, Jade Asha",2016,104,3.5,0
+889,This Is 40,"Comedy,Romance",Judd Apatow,"Paul Rudd, Leslie Mann, Maude Apatow, Iris Apatow",2012,134,6.2,67.52
+890,Real Steel,"Action,Drama,Family",Shawn Levy,"Hugh Jackman, Evangeline Lilly, Dakota Goyo,Anthony Mackie",2011,127,7.1,85.46
+891,Sex and the City,"Comedy,Drama,Romance",Michael Patrick King,"Sarah Jessica Parker, Kim Cattrall, Cynthia Nixon, Kristin Davis",2008,145,5.5,152.64M
+892,Rambo,"Action,Thriller,War",Sylvester Stallone,"Sylvester Stallone, Julie Benz, Matthew Marsden, Graham McTavish",2008,92,7.1,42.72
+893,Planet Terror,"Action,Comedy,Horror",Robert Rodriguez,"Rose McGowan, Freddy Rodríguez, Josh Brolin,Marley Shelton",2007,105,7.1,0
+894,Concussion,"Biography,Drama,Sport",Peter Landesman,"Will Smith, Alec Baldwin, Albert Brooks, David Morse",2015,123,7.1,34.53
+895,The Fall,"Adventure,Comedy,Drama",Tarsem Singh,"Lee Pace, Catinca Untaru, Justine Waddell, Kim Uylenbroek",2006,117,7.9,2.28
+896,The Ugly Truth,"Comedy,Romance",Robert Luketic,"Katherine Heigl, Gerard Butler, Bree Turner, Eric Winter",2009,96,6.5,88.92
+897,Bride Wars,"Comedy,Romance",Gary Winick,"Kate Hudson, Anne Hathaway, Candice Bergen, Bryan Greenberg",2009,89,5.5,58.72
+898,Sleeping with Other People,"Comedy,Drama,Romance",Leslye Headland,"Jason Sudeikis, Alison Brie, Jordan Carlos,Margarita Levieva",2015,101,6.5,0.81
+899,Snakes on a Plane,"Action,Adventure,Crime",David R. Ellis,"Samuel L. Jackson, Julianna Margulies, Nathan Phillips, Rachel Blanchard",2006,105,5.6,34.01
+900,What If,"Comedy,Romance",Michael Dowse,"Daniel Radcliffe, Zoe Kazan, Megan Park, Adam Driver",2013,98,6.8,3.45
+901,How to Train Your Dragon 2,"Animation,Action,Adventure",Dean DeBlois,"Jay Baruchel, Cate Blanchett, Gerard Butler, Craig Ferguson",2014,102,7.9,177M
+902,RoboCop,"Action,Crime,Sci-Fi",José Padilha,"Joel Kinnaman, Gary Oldman, Michael Keaton, Abbie Cornish",2014,117,6.2,58.61
+903,In Dubious Battle,Drama,James Franco,"Nat Wolff, James Franco, Vincent D'Onofrio, Selena Gomez",2016,110,6.2,0
+904,"Hello, My Name Is Doris","Comedy,Drama,Romance",Michael Showalter,"Sally Field, Max Greenfield, Tyne Daly, Wendi McLendon-Covey",2015,95,6.7,14.44
+905,Ocean's Thirteen,"Crime,Thriller",Steven Soderbergh,"George Clooney, Brad Pitt, Matt Damon,Michael Mantell",2007,122,6.9,117.14M
+906,Slither,"Comedy,Horror,Sci-Fi",James Gunn,"Nathan Fillion, Elizabeth Banks, Michael Rooker, Don Thompson",2006,95,6.5,7.77
+907,Contagion,"Drama,Thriller",Steven Soderbergh,"Matt Damon, Kate Winslet, Jude Law, Gwyneth Paltrow",2011,106,6.6,75.64
+908,Il racconto dei racconti - Tale of Tales,"Drama,Fantasy,Horror",Matteo Garrone,"Salma Hayek, Vincent Cassel, Toby Jones, John C. Reilly",2015,133,6.4,0.08
+909,I Am the Pretty Thing That Lives in the House,Thriller,Oz Perkins,"Ruth Wilson, Paula Prentiss, Lucy Boynton, Bob Balaban",2016,87,4.7,0
+910,Bridge to Terabithia,"Adventure,Drama,Family",Gabor Csupo,"Josh Hutcherson, AnnaSophia Robb, Zooey Deschanel, Robert Patrick",2007,96,7.2,82.23
+911,Coherence,"Mystery,Sci-Fi,Thriller",James Ward Byrkit,"Emily Baldoni, Maury Sterling, Nicholas Brendon, Elizabeth Gracen",2013,89,7.2,0.07
+912,Notorious,"Biography,Crime,Drama",George Tillman Jr.,"Jamal Woolard, Anthony Mackie, Derek Luke,Momo Dione",2009,122,6.7,36.84
+913,Goksung,"Drama,Fantasy,Horror",Hong-jin Na,"Jun Kunimura, Jung-min Hwang, Do-won Kwak, Woo-hee Chun",2016,156,7.5,0.79
+914,The Expendables 2,"Action,Adventure,Thriller",Simon West,"Sylvester Stallone, Liam Hemsworth, Randy Couture,Jean-Claude Van Damme",2012,103,6.6,85.02
+915,The Girl Next Door,"Crime,Drama,Horror",Gregory Wilson,"William Atherton, Blythe Auffarth, Blanche Baker,Kevin Chamberlin",2007,91,6.7,0
+916,Perfume: The Story of a Murderer,"Crime,Drama,Fantasy",Tom Tykwer,"Ben Whishaw, Dustin Hoffman, Alan Rickman,Francesc Albiol",2006,147,7.5,2.21
+917,The Golden Compass,"Adventure,Family,Fantasy",Chris Weitz,"Nicole Kidman, Daniel Craig, Dakota Blue Richards, Ben Walker",2007,113,6.1,70.08
+918,Centurion,"Action,Adventure,Drama",Neil Marshall,"Michael      fassbender, Dominic West, Olga Kurylenko,Andreas Wisniewski",2010,97,6.4,0.12
+919,Scouts Guide to the Zombie Apocalypse,"Action,Comedy,Horror",Christopher Landon,"Tye Sheridan, Logan Miller, Joey Morgan,Sarah Dumont",2015,93,6.3,3.64
+920,17 Again,"Comedy,Drama,Family",Burr Steers,"Zac Efron, Matthew Perry, Leslie Mann, Thomas Lennon",2009,102,6.4,64.15
+921,No Escape,"Action,Thriller",John Erick Dowdle,"Lake Bell, Pierce Brosnan, Owen Wilson,Chatchawai Kamonsakpitak",2015,103,6.8,27.29
+922,Superman Returns,"Action,Adventure,Sci-Fi",Bryan Singer,"Brandon Routh, Kevin Spacey, Kate Bosworth, James Marsden",2006,154,6.1,200.07
+923,The Twilight Saga: Breaking Dawn - Part 1,"Adventure,Drama,Fantasy",Bill Condon,"Kristen Stewart, Robert Pattinson, Taylor Lautner, Gil Birmingham",2011,117,4.9,281.28
+924,Precious,Drama,Lee Daniels,"Gabourey Sidibe, Mo'Nique, Paula Patton, Mariah Carey",2009,110,7.3,47.54
+925,The Sea of Trees,Drama,Gus Van Sant,"Matthew McConaughey, Naomi Watts, Ken Watanabe,Ryoko Seta",2015,110,5.9,0.02
+926,Good Kids,Comedy,Chris McCoy,"Zoey Deutch, Nicholas Braun, Mateo Arias, Israel Broussard",2016,86,6.1,0
+927,The Master,Drama,Paul Thomas Anderson,"Philip Seymour Hoffman, Joaquin Phoenix,Amy Adams, Jesse Plemons",2012,144,7.1,16.38
+928,Footloose,"Comedy,Drama,Music",Craig Brewer,"Kenny Wormald, Julianne Hough, Dennis Quaid,Andie MacDowell",2011,113,5.9,51.78
+929,If I Stay,"Drama,Fantasy,Music",R.J. Cutler,"Chloë Grace Moretz, Mireille Enos, Jamie Blackley,Joshua Leonard",2014,107,6.8,50.46
+930,The Ticket,Drama,Ido Fluk,"Dan Stevens, Malin Akerman, Oliver Platt, Kerry Bishé",2016,97,5.4,0
+931,Detour,Thriller,Christopher Smith,"Tye Sheridan, Emory Cohen, Bel Powley,Stephen Moyer",2016,97,6.3,0
+932,The Love Witch,"Comedy,Horror",Anna Biller,"Samantha Robinson, Jeffrey Vincent Parise, Laura Waddell, Gian Keys",2016,120,6.2,0.22
+933,Talladega Nights: The Ballad of Ricky Bobby,"Action,Comedy,Sport",Adam McKay,"Will Ferrell, John C. Reilly, Sacha Baron Cohen, Gary Cole",2006,108,6.6,148.21M
+934,The Human Centipede (First Sequence),Horror,Tom Six,"Dieter Laser, Ashley C. Williams, Ashlynn Yennie, Akihiro Kitamura",2009,92,4.4,0.18
+935,Super,"Comedy,Drama",James Gunn,"Rainn Wilson, Ellen Page, Liv Tyler, Kevin Bacon",2010,96,6.8,0.32
+936,The Siege of Jadotville,"Action,Drama,Thriller",Richie Smyth,"Jamie Dornan, Mark Strong, Jason O'Mara, Michael McElhatton",2016,108,7.3,0
+937,Up in the Air,"Drama,Romance",Jason Reitman,"George Clooney, Vera Farmiga, Anna Kendrick,Jason Bateman",2009,109,7.4,83.81
+938,The Midnight Meat Train,"Horror,Mystery",Ryûhei Kitamura,"Vinnie Jones, Bradley Cooper, Leslie Bibb, Brooke Shields",2008,98,6.1,0.07
+939,The Twilight Saga: Eclipse,"Adventure,Drama,Fantasy",David Slade,"Kristen Stewart, Robert Pattinson, Taylor Lautner,Xavier Samuel",2010,124,4.9,300.52
+940,Transpecos,Thriller,Greg Kwedar,"Johnny Simmons, Gabriel Luna, Clifton Collins Jr.,David Acord",2016,86,5.8,0
+941,What's Your Number?,"Comedy,Romance",Mark Mylod,"Anna Faris, Chris Evans, Ari Graynor, Blythe Danner",2011,106,6.1,13.99
+942,Riddick,"Action,Sci-Fi,Thriller",David Twohy,"Vin Diesel, Karl Urban, Katee Sackhoff, Jordi Mollà",2013,119,6.4,42
+943,Triangle,"Fantasy,Mystery,Thriller",Christopher Smith,"Melissa George, Joshua McIvor, Jack Taylor,Michael Dorman",2009,99,6.9,0
+944,The Butler,"Biography,Drama",Lee Daniels,"Forest Whitaker, Oprah Winfrey, John Cusack, Jane Fonda",2013,132,7.2,116.63M
+945,King Cobra,"Crime,Drama",Justin Kelly,"Garrett Clayton, Christian Slater, Molly Ringwald,James Kelley",2016,91,5.6,0.03
+946,After Earth,"Action,Adventure,Sci-Fi",M. Night Shyamalan,"Jaden Smith, David Denman, Will Smith,Sophie Okonedo",2013,100,4.9,60.52
+947,Kicks,Adventure,Justin Tipping,"Jahking Guillory, Christopher Jordan Wallace,Christopher Meyer, Kofi Siriboe",2016,80,6.1,0.15
+948,Me and Earl and the Dying Girl,"Comedy,Drama",Alfonso Gomez-Rejon,"Thomas Mann, RJ Cyler, Olivia Cooke, Nick Offerman",2015,105,7.8,6.74
+949,The Descendants,"Comedy,Drama",Alexander Payne,"George Clooney, Shailene Woodley, Amara Miller, Nick Krause",2011,115,7.3,82.62
+950,Sex and the City 2,"Comedy,Drama,Romance",Michael Patrick King,"Sarah Jessica Parker, Kim Cattrall, Kristin Davis, Cynthia Nixon",2010,146,4.3,95.33
+951,The Kings of Summer,"Adventure,Comedy,Drama",Jordan Vogt-Roberts,"Nick Robinson, Gabriel Basso, Moises Arias,Nick Offerman",2013,95,7.2,1.29
+952,Death Race,"Action,Sci-Fi,Thriller",Paul W.S. Anderson,"Jason Statham, Joan Allen, Tyrese Gibson, Ian McShane",2008,105,6.4,36.06
+953,That Awkward Moment,"Comedy,Romance",Tom Gormican,"Zac Efron, Michael B. Jordan, Miles Teller, Imogen Poots",2014,94,6.2,26.05
+954,Legion,"Action,Fantasy,Horror",Scott Stewart,"Paul Bettany, Dennis Quaid, Charles S. Dutton, Lucas Black",2010,100,5.2,40.17
+955,End of Watch,"Crime,Drama,Thriller",David Ayer,"Jake Gyllenhaal, Michael Peña, Anna Kendrick, America Ferrera",2012,109,7.7,40.98
+956,3 Days to Kill,"Action,Drama,Thriller",McG,"Kevin Costner, Hailee Steinfeld, Connie Nielsen, Amber Heard",2014,117,6.2,30.69
+957,Lucky Number Slevin,"Crime,Drama,Mystery",Paul McGuigan,"Josh Hartnett, Ben Kingsley, Morgan Freeman, Lucy Liu",2006,110,7.8,22.49
+958,Trance,"Crime,Drama,Mystery",Danny Boyle,"James McAvoy, Rosario Dawson, Vincent Cassel,Danny Sapani",2013,101,7,2.32
+959,Into the Forest,"Drama,Sci-Fi,Thriller",Patricia Rozema,"Ellen Page, Evan Rachel Wood, Max Minghella,Callum Keith Rennie",2015,101,5.9,0.01
+960,The Other Boleyn Girl,"Biography,Drama,History",Justin Chadwick,"Natalie Portman, Scarlett Johansson, Eric Bana,Jim Sturgess",2008,115,6.7,26.81
+961,I Spit on Your Grave,"Crime,Horror,Thriller",Steven R. Monroe,"Sarah Butler, Jeff Branson, Andrew Howard,Daniel Franzese",2010,108,6.3,0.09
+962,Custody,Drama,James Lapine,"Viola Davis, Hayden Panettiere, Catalina Sandino Moreno, Ellen Burstyn",2016,104,6.9,0
+963,Inland Empire,"Drama,Mystery,Thriller",David Lynch,"Laura Dern, Jeremy Irons, Justin Theroux, Karolina Gruszka",2006,180,7,0
+964,L'odyssée,"Adventure,Biography",Jérôme Salle,"Lambert Wilson, Pierre Niney, Audrey Tautou,Laurent Lucas",2016,122,6.7,0
+965,The Walk,"Adventure,Biography,Crime",Robert Zemeckis,"Joseph Gordon-Levitt, Charlotte Le Bon,Guillaume Baillargeon, Émilie Leclerc",2015,123,7.3,10.14
+966,Wrecker,"Action,Horror,Thriller",Micheal Bafaro,"Anna Hutchison, Andrea Whitburn, Jennifer Koenig,Michael Dickson",2015,83,3.5,0
+967,The Lone Ranger,"Action,Adventure,Western",Gore Verbinski,"Johnny Depp, Armie Hammer, William Fichtner,Tom Wilkinson",2013,150,6.5,89.29
+968,Texas Chainsaw 3D,"Horror,Thriller",John Luessenhop,"Alexandra Daddario, Tania Raymonde, Scott Eastwood, Trey Songz",2013,92,4.8,34.33M
+969,Disturbia,"Drama,Mystery,Thriller",D.J. Caruso,"Shia LaBeouf, David Morse, Carrie-Anne Moss, Sarah Roemer",2007,105,6.9,80.05
+970,Rock of Ages,"Comedy,Drama,Musical",Adam Shankman,"Julianne Hough, Diego Boneta, Tom Cruise, Alec Baldwin",2012,123,5.9,38.51
+971,Scream 4,"Horror,Mystery",Wes Craven,"Neve Campbell, Courteney Cox, David Arquette, Lucy Hale",2011,111,6.2,38.18
+972,Queen of Katwe,"Biography,Drama,Sport",Mira Nair,"Madina Nalwanga, David Oyelowo, Lupita Nyong'o, Martin Kabanza",2016,124,7.4,8.81
+973,My Big Fat Greek Wedding 2,"Comedy,Family,Romance",Kirk Jones,"Nia Vardalos, John Corbett, Michael Constantine, Lainie Kazan",2016,94,6,59.57
+974,Dark Places,"Drama,Mystery,Thriller",Gilles Paquet-Brenner,"Charlize Theron, Nicholas Hoult, Christina Hendricks, Chloë Grace Moretz",2015,113,6.2,0
+975,Amateur Night,Comedy,Lisa Addario,"Jason Biggs, Janet Montgomery,Ashley Tisdale, Bria L. Murphy",2016,92,5,0
+976,It's Only the End of the World,Drama,Xavier Dolan,"Nathalie Baye, Vincent Cassel, Marion Cotillard, Léa Seydoux",2016,97,7,0
+977,The Skin I Live In,"Drama,Thriller",Pedro Almodóvar,"Antonio Banderas, Elena Anaya, Jan Cornet,Marisa Paredes",2011,120,7.6,3.19
+978,Miracles from Heaven,"Biography,Drama,Family",Patricia Riggen,"Jennifer Garner, Kylie Rogers, Martin Henderson,Brighton Sharbino",2016,109,7,61.69
+979,Annie,"Comedy,Drama,Family",Will Gluck,"Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, Rose Byrne",2014,118,5.3,85.91
+980,Across the Universe,"Drama,Fantasy,Musical",Julie Taymor,"Evan Rachel Wood, Jim Sturgess, Joe Anderson, Dana Fuchs",2007,133,7.4,24.34
+981,Let's Be Cops,Comedy,Luke Greenfield,"Jake Johnson, Damon Wayans Jr., Rob Riggle, Nina Dobrev",2014,104,6.5,82.39
+982,Max,"Adventure,Family",Boaz Yakin,"Thomas Haden Church, Josh Wiggins, Luke Kleintank,Lauren Graham",2015,111,6.8,42.65
+983,Your Highness,"Adventure,Comedy,Fantasy",David Gordon Green,"Danny McBride, Natalie Portman, James Franco, Rasmus Hardiker",2011,102,5.6,21.56
+984,Final Destination 5,"Horror,Thriller",Steven Quale,"Nicholas D'Agosto, Emma Bell, Arlen Escarpeta, Miles Fisher",2011,92,5.9,42.58
+985,Endless Love,"Drama,Romance",Shana Feste,"Gabriella Wilde, Alex Pettyfer, Bruce Greenwood,Robert Patrick",2014,104,6.3,23.39
+986,Martyrs,Horror,Pascal Laugier,"Morjana Alaoui, Mylène Jampanoï, Catherine Bégin,Robert Toupin",2008,99,7.1,0
+987,Selma,"Biography,Drama,History",Ava DuVernay,"David Oyelowo, Carmen Ejogo, Tim Roth, Lorraine Toussaint",2014,128,7.5,52.07
+988,Underworld: Rise of the Lycans,"Action,Adventure,Fantasy",Patrick Tatopoulos,"Rhona Mitra, Michael Sheen, Bill Nighy, Steven Mackintosh",2009,92,6.6,45.8
+989,Taare Zameen Par,"Drama,Family,Music",Aamir Khan,"Darsheel Safary, Aamir Khan, Tanay Chheda, Sachet Engineer",2007,165,8.5,1.2
+990,Take Me Home Tonight,"Comedy,Drama,Romance",Michael Dowse,"Topher Grace, Anna Faris, Dan Fogler, Teresa Palmer",2011,97,6.3,6.92
+991,Resident Evil: Afterlife,"Action,Adventure,Horror",Paul W.S. Anderson,"Milla Jovovich, Ali Larter, Wentworth Miller,Kim Coates",2010,97,5.9,60.13
+992,Project X,Comedy,Nima Nourizadeh,"Thomas Mann, Oliver Cooper, Jonathan Daniel Brown, Dax Flame",2012,88,6.7,54.72
+993,Secret in Their Eyes,"Crime,Drama,Mystery",Billy Ray,"Chiwetel Ejiofor, Nicole Kidman, Julia Roberts, Dean Norris",2015,111,6.2,0
+994,Hostel: Part II,Horror,Eli Roth,"Lauren German, Heather Matarazzo, Bijou Phillips, Roger Bart",2007,94,5.5,17.54
+995,Step Up 2: The Streets,"Drama,Music,Romance",Jon M. Chu,"Robert Hoffman, Briana Evigan, Cassie Ventura, Adam G. Sevani",2008,98,6.2,58.01
+996,Search Party,"Adventure,Comedy",Scot Armstrong,"Adam Pally, T.J. Miller, Thomas Middleditch,Shannon Woodward",2014,93,5.6,0
+997,Nine Lives,"Comedy,Family,Fantasy",Barry Sonnenfeld,"Kevin Spacey, Jennifer Garner, Robbie Amell,Cheryl Hines",2016,87,5.3,19.64
diff --git a/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Solution.ipynb b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Solution.ipynb
new file mode 100644
index 0000000..05e5bab
--- /dev/null
+++ b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Solution.ipynb	
@@ -0,0 +1,4590 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0: Importing from `pandas`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "# We can explictly import Series and DataFrame, why might we do this?\n",
+    "from pandas import Series, DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Series from a list\n",
+    "\n",
+    "In the **Series** below,\n",
+    "1. What name do we call the  0, 1, 2, ... ?\n",
+    "\n",
+    "   **index**\n",
+    "   \n",
+    "2. What name do we call the  54, 22, 19, .... ?\n",
+    "   \n",
+    "   **value**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    54\n",
+       "1    22\n",
+       "2    19\n",
+       "3    73\n",
+       "4    80\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_list = [54, 22, 19, 73, 80]\n",
+    "scores_series = Series(scores_list)\n",
+    "scores_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 2: What are all the scores `> 50`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    54\n",
+       "3    73\n",
+       "4    80\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "scores_series[scores_series > 50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are really writing a \"mask\" for our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     True\n",
+       "1    False\n",
+       "2    False\n",
+       "3     True\n",
+       "4     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_series > 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    54\n",
+       "1    22\n",
+       "4    80\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_series[[True, True, False, False, True]] # often called a \"mask\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    54\n",
+       "3    73\n",
+       "4    80\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_series[scores_series > 50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 3: Series from a dict\n",
+    "\n",
+    "Imagine we hire students and track their weekly hours. For everyone in Week 1, add 3 to their hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rita       5\n",
+      "Therese    3\n",
+      "Janice     6\n",
+      "dtype: int64\n",
+      "Rita       3\n",
+      "Therese    7\n",
+      "Janice     4\n",
+      "dtype: int64\n",
+      "Therese    5\n",
+      "Janice     5\n",
+      "Rita       8\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "week1 = Series({\"Rita\":5, \"Therese\":3, \"Janice\": 6})\n",
+    "week2 = Series({\"Rita\":3, \"Therese\":7, \"Janice\": 4})\n",
+    "week3 = Series({\"Therese\":5, \"Janice\":5, \"Rita\": 8}) # Wrong order! Will this matter?\n",
+    "print(week1)\n",
+    "print(week2)\n",
+    "print(week3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Rita       8\n",
+       "Therese    6\n",
+       "Janice     9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "week1 += 3\n",
+    "week1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 4: Find the total hours of everyone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Janice     18\n",
+       "Rita       19\n",
+       "Therese    18\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "total_hours = week1 + week2 + week3\n",
+    "total_hours"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 5: What is `week1/week3`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Janice     1.8\n",
+       "Rita       1.0\n",
+       "Therese    1.2\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "week1/week3 # notice that we don't have to worry about the order of indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 6: What type of values are stored in `week1 > week2`?\n",
+    "\n",
+    "**bool**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rita       8\n",
+      "Therese    6\n",
+      "Janice     9\n",
+      "dtype: int64\n",
+      "Rita       3\n",
+      "Therese    7\n",
+      "Janice     4\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Rita        True\n",
+       "Therese    False\n",
+       "Janice      True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(week1)\n",
+    "print(week2)\n",
+    "week1 > week2 # note that the indices are ordered the same"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 7:  What is `week1 > week3`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rita       8\n",
+      "Therese    6\n",
+      "Janice     9\n",
+      "dtype: int64\n",
+      "Therese    5\n",
+      "Janice     5\n",
+      "Rita       8\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Janice      True\n",
+       "Rita       False\n",
+       "Therese     True\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(week1)\n",
+    "print(week3)\n",
+    "# week1 > week3 # indices not in same order\n",
+    "week1.sort_index() > week3.sort_index() # proper way"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lecture 28:  Pandas 2 - DataFrames\n",
+    "\n",
+    "Learning Objectives:\n",
+    "- Create a **DataFrame** from \n",
+    " - a **dictionary** of **Series**, **lists**, or **dicts**\n",
+    " - a **list** of **Series**, **lists**, **dicts**\n",
+    "- Select a column, row, cell, or rectangular region of a **DataFrame**\n",
+    "- Convert CSV files into **DataFrames** and **DataFrames** into CSV Files\n",
+    "- Access the head or tail of a **DataFrame**\n",
+    "\n",
+    "**Big Idea**: Data Frames store 2-dimensional data in tables! It is a collection of Series."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## You can create a DataFrame in a variety of ways!\n",
+    "### From a dictionary of Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "0       Alice      6\n",
+       "1         Bob      7\n",
+       "2       Cindy      8\n",
+       "3         Dan      9"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names = Series([\"Alice\", \"Bob\", \"Cindy\", \"Dan\"])\n",
+    "scores = Series([6, 7, 8, 9])\n",
+    "\n",
+    "# to make a dictionary of Series, need to write column names for the keys\n",
+    "DataFrame({\n",
+    "    \"Player name\": names,\n",
+    "    \"Score\": scores\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a dictionary of lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "0       Alice      6\n",
+       "1         Bob      7\n",
+       "2       Cindy      8\n",
+       "3         Dan      9"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "name_list = [\"Alice\", \"Bob\", \"Cindy\", \"Dan\"]\n",
+    "score_list = [6, 7, 8, 9]\n",
+    "# this is the same as above, reminding us that Series act like lists\n",
+    "DataFrame({\n",
+    "    \"Player name\": name_list,\n",
+    "    \"Score\": score_list\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a dictionary of dictionaries\n",
+    "We need to make up keys to match the things in each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "0       Alice      6\n",
+       "1         Bob      7\n",
+       "2       Cindy      8\n",
+       "3         Dan      9"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = {\n",
+    "    \"Player name\": {0: \"Alice\", 1: \"Bob\", 2: \"Cindy\", 3: \"Dan\"},\n",
+    "    \"Score\": {0: 6, 1: 7, 2: 8, 3: 9}\n",
+    "}\n",
+    "DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a list of lists\n",
+    "We have to add the column names, we do this with `columns = [name1, name2, ....]` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "0       Alice      6\n",
+       "1         Bob      7\n",
+       "2       Cindy      8\n",
+       "3         Dan      9"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    [\"Alice\", 6],\n",
+    "    [\"Bob\", 7],\n",
+    "    [\"Cindy\", 8],\n",
+    "    [\"Dan\", 9]\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data, columns=[\"Player name\", \"Score\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a list of dicts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "0       Alice      6\n",
+       "1         Bob      7\n",
+       "2       Cindy      8\n",
+       "3         Dan      9"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    {\"Player name\": \"Alice\", \"Score\": 6},\n",
+    "    {\"Player name\": \"Bob\", \"Score\": 7},\n",
+    "    {\"Player name\": \"Cindy\", \"Score\": 8},\n",
+    "    {\"Player name\": \"Dan\", \"Score\": 9}\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explicitly naming the indices\n",
+    "We can use `index = [name1, name2, ...]` to rename the index of each row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice      6\n",
+       "B         Bob      7\n",
+       "C       Cindy      8\n",
+       "D         Dan      9"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = [\n",
+    "    {\"Player name\": \"Alice\", \"Score\": 6},\n",
+    "    {\"Player name\": \"Bob\", \"Score\": 7},\n",
+    "    {\"Player name\": \"Cindy\", \"Score\": 8},\n",
+    "    {\"Player name\": \"Dan\", \"Score\": 9}\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data, index=[\"A\", \"B\", \"C\", \"D\"]) # must have a name for each row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: Make a DataFrame of 4 people you know with different ages. Give names to both the columns and rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Age\n",
+       "A  Alice    6\n",
+       "B    Bob    7\n",
+       "C  Cindy    8\n",
+       "D    Dan    9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "ages = [\n",
+    "    [\"Alice\", 6],\n",
+    "    [\"Bob\", 7],\n",
+    "    [\"Cindy\", 8],\n",
+    "    [\"Dan\", 9]\n",
+    "]\n",
+    "DataFrame(ages, index=[\"A\", \"B\", \"C\", \"D\"], columns=[\"Name\", \"Age\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select a column, row, cell, or rectangular region of a DataFrame\n",
+    "### Data lookup: Series\n",
+    "- `s.loc[X]`   <- lookup by pandas index\n",
+    "- `s.iloc[X]`  <- lookup by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alice    6\n",
+       "Bob      7\n",
+       "Cindy    8\n",
+       "Dan      9\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hours = Series({\"Alice\":6, \"Bob\":7, \"Cindy\":8, \"Dan\":9})\n",
+    "hours"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lookup using Series index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hours.loc[\"Bob\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lookup using Series integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hours.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Find Cindy's hours using both the Series index and the integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(hours.loc[\"Cindy\"])\n",
+    "print(hours.iloc[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Data lookup: DataFrame\n",
+    "\n",
+    "\n",
+    "- `d.loc[r]`     lookup ROW by pandas ROW index\n",
+    "- `d.iloc[r]`    lookup ROW by ROW integer position\n",
+    "- `d[c]`         lookup COL by pandas COL index\n",
+    "- `d.loc[r, c]`  lookup by pandas ROW index and pandas COL index\n",
+    "- `d.iloc[r, c]`  lookup by ROW integer position and COL integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Dan</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice      6\n",
+       "B         Bob      7\n",
+       "C       Cindy      8\n",
+       "D         Dan      9"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We often call the object that we make df\n",
+    "data = [\n",
+    "    [\"Alice\", 6],\n",
+    "    [\"Bob\", 7],\n",
+    "    [\"Cindy\", 8],\n",
+    "    [\"Dan\", 9]\n",
+    "]\n",
+    "df = DataFrame(data, index=[\"A\", \"B\", \"C\", \"D\"], columns = [\"Player name\", \"Score\"])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Different ways of accessing row D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Player name    Dan\n",
+      "Score            9\n",
+      "Name: D, dtype: object\n",
+      "Player name    Dan\n",
+      "Score            9\n",
+      "Name: D, dtype: object\n",
+      "Player name    Dan\n",
+      "Score            9\n",
+      "Name: D, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(df[\"D\"]) # Nope!\n",
+    "print(df.loc[\"D\"])\n",
+    "print(df.iloc[3])\n",
+    "print(df.iloc[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How about accessing a column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A    Alice\n",
+      "B      Bob\n",
+      "C    Cindy\n",
+      "D      Dan\n",
+      "Name: Player name, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# print(df[0]) # Nope!\n",
+    "print(df[\"Player name\"])\n",
+    "# print(df.loc[\"Player name\"]) # Nope!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Different ways to access a single cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cindy\n",
+      "Cindy\n",
+      "Cindy\n",
+      "Cindy\n",
+      "Cindy\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Player name\"][\"C\"])\n",
+    "print(df.loc[\"C\"][\"Player name\"])\n",
+    "print(df.loc[\"C\", \"Player name\"])\n",
+    "# print(df[\"C\", \"Player name\"]) # Nope!\n",
+    "print(df[\"Player name\"].loc[\"C\"])\n",
+    "print(df.iloc[2, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to set values for a specific entry?\n",
+    "\n",
+    "- `d.loc[r, c] = new_val`\n",
+    "- `d.iloc[r, c] = new_val`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Change player D's name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice      6\n",
+       "B         Bob      7\n",
+       "C       Cindy      8\n",
+       "D      Bianca      9"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"D\", \"Player name\"] = \"Bianca\"\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 3 to that player's score using `.loc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice      6\n",
+       "B         Bob      7\n",
+       "C       Cindy      8\n",
+       "D      Bianca     12"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"D\", \"Score\"] += 3\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 7 to a player's score using `.iloc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice     13\n",
+       "B         Bob      7\n",
+       "C       Cindy      8\n",
+       "D      Bianca     12"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[0, 1] += 7\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the max score and the mean score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13 10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Score\"].max(), df[\"Score\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the highest scoring player"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Alice'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "highest_scorer = df[\"Score\"].idxmax()\n",
+    "df[\"Player name\"].loc[highest_scorer]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Slicing a DataFrame\n",
+    "\n",
+    "- `df.iloc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using integer positions\n",
+    "- `df.loc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "B         Bob      7\n",
+       "C       Cindy      8"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iloc[1:3, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "B         Bob      7\n",
+       "C       Cindy      8"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"B\":\"C\", \"Player name\":\"Score\"] # notice that this way is inclusive of endpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set values for sliced DataFrame\n",
+    "\n",
+    "- `d.loc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW INDEX and COL INDEX\n",
+    "- `d.iloc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW Integer position and COL Integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice     13\n",
+       "B         Bob     12\n",
+       "C       Cindy     13\n",
+       "D      Bianca     12"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[\"B\":\"C\", \"Score\"] += 5\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pandas allows slicing of non-contiguous columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Just get Player name for Index B and D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "B       Bob\n",
+       "D    Bianca\n",
+       "Name: Player name, dtype: object"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[[\"B\", \"D\"], \"Player name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 3 to the people in rows B and D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>Cindy</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "A       Alice     13\n",
+       "B         Bob     15\n",
+       "C       Cindy     13\n",
+       "D      Bianca     15"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[[\"B\", \"D\"], \"Score\"] += 3\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean indexing on a DataFrame\n",
+    "\n",
+    "- `d[BOOL SERIES]`  <- makes a new DF of all rows that lined up were True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a Series of Booleans based on Score >= 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "A    False\n",
+       "B     True\n",
+       "C    False\n",
+       "D     True\n",
+       "Name: Score, dtype: bool"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool_index = df[\"Score\"] >= 15\n",
+    "bool_index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use `bool_index` to slice the DataFrame\n",
+    "\n",
+    "If `bool_index` is `True`, include this row in the new df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "B         Bob     15\n",
+       "D      Bianca     15"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[bool_index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Do the last two things in a single step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Player name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>Bianca</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Player name  Score\n",
+       "B         Bob     15\n",
+       "D      Bianca     15"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Score\"] >= 15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating DataFrame from CSV file\n",
+    "\n",
+    "It's that easy! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>270.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>993</td>\n",
+       "      <td>Secret in Their Eyes</td>\n",
+       "      <td>Crime,Drama,Mystery</td>\n",
+       "      <td>Billy Ray</td>\n",
+       "      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>111</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994</th>\n",
+       "      <td>994</td>\n",
+       "      <td>Hostel: Part II</td>\n",
+       "      <td>Horror</td>\n",
+       "      <td>Eli Roth</td>\n",
+       "      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>94</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>17.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>995</td>\n",
+       "      <td>Step Up 2: The Streets</td>\n",
+       "      <td>Drama,Music,Romance</td>\n",
+       "      <td>Jon M. Chu</td>\n",
+       "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>98</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>58.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>996</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Adventure,Comedy</td>\n",
+       "      <td>Scot Armstrong</td>\n",
+       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>93</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>997</td>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Comedy,Family,Fantasy</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>19.64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>998 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Index                    Title                     Genre  \\\n",
+       "0        0  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
+       "1        1               Prometheus  Adventure,Mystery,Sci-Fi   \n",
+       "2        2                    Split           Horror,Thriller   \n",
+       "3        3                     Sing   Animation,Comedy,Family   \n",
+       "4        4            Suicide Squad  Action,Adventure,Fantasy   \n",
+       "..     ...                      ...                       ...   \n",
+       "993    993     Secret in Their Eyes       Crime,Drama,Mystery   \n",
+       "994    994          Hostel: Part II                    Horror   \n",
+       "995    995   Step Up 2: The Streets       Drama,Music,Romance   \n",
+       "996    996             Search Party          Adventure,Comedy   \n",
+       "997    997               Nine Lives     Comedy,Family,Fantasy   \n",
+       "\n",
+       "                 Director                                               Cast  \\\n",
+       "0              James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...   \n",
+       "1            Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...   \n",
+       "2      M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...   \n",
+       "3    Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...   \n",
+       "4              David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...   \n",
+       "..                    ...                                                ...   \n",
+       "993             Billy Ray  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...   \n",
+       "994              Eli Roth  Lauren German, Heather Matarazzo, Bijou Philli...   \n",
+       "995            Jon M. Chu  Robert Hoffman, Briana Evigan, Cassie Ventura,...   \n",
+       "996        Scot Armstrong  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...   \n",
+       "997      Barry Sonnenfeld  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...   \n",
+       "\n",
+       "     Year  Runtime  Rating  Revenue  \n",
+       "0    2014      121     8.1   333.13  \n",
+       "1    2012      124     7.0  126.46M  \n",
+       "2    2016      117     7.3  138.12M  \n",
+       "3    2016      108     7.2   270.32  \n",
+       "4    2016      123     6.2   325.02  \n",
+       "..    ...      ...     ...      ...  \n",
+       "993  2015      111     6.2        0  \n",
+       "994  2007       94     5.5    17.54  \n",
+       "995  2008       98     6.2    58.01  \n",
+       "996  2014       93     5.6        0  \n",
+       "997  2016       87     5.3    19.64  \n",
+       "\n",
+       "[998 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"IMDB-Movie-Data.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: View the first few lines of the DataFrame\n",
+    "- `.head(n)` gets the first n lines, 5 is the default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>270.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Index                    Title                     Genre  \\\n",
+       "0      0  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
+       "1      1               Prometheus  Adventure,Mystery,Sci-Fi   \n",
+       "2      2                    Split           Horror,Thriller   \n",
+       "3      3                     Sing   Animation,Comedy,Family   \n",
+       "4      4            Suicide Squad  Action,Adventure,Fantasy   \n",
+       "\n",
+       "               Director                                               Cast  \\\n",
+       "0            James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...   \n",
+       "1          Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael   ...   \n",
+       "2    M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...   \n",
+       "3  Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...   \n",
+       "4            David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...   \n",
+       "\n",
+       "   Year  Runtime  Rating  Revenue  \n",
+       "0  2014      121     8.1   333.13  \n",
+       "1  2012      124     7.0  126.46M  \n",
+       "2  2016      117     7.3  138.12M  \n",
+       "3  2016      108     7.2   270.32  \n",
+       "4  2016      123     6.2   325.02  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Get the first 2 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Index                    Title                     Genre      Director  \\\n",
+       "0      0  Guardians of the Galaxy   Action,Adventure,Sci-Fi    James Gunn   \n",
+       "1      1               Prometheus  Adventure,Mystery,Sci-Fi  Ridley Scott   \n",
+       "\n",
+       "                                                Cast  Year  Runtime  Rating  \\\n",
+       "0  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1   \n",
+       "1  Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0   \n",
+       "\n",
+       "   Revenue  \n",
+       "0   333.13  \n",
+       "1  126.46M  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: View the last few lines of the DataFrame\n",
+    "- `.tail(n)` gets the last n lines, 5 is the default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>995</td>\n",
+       "      <td>Step Up 2: The Streets</td>\n",
+       "      <td>Drama,Music,Romance</td>\n",
+       "      <td>Jon M. Chu</td>\n",
+       "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>98</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>58.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>996</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Adventure,Comedy</td>\n",
+       "      <td>Scot Armstrong</td>\n",
+       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>93</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>997</td>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Comedy,Family,Fantasy</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>19.64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Index                   Title                  Genre          Director  \\\n",
+       "995    995  Step Up 2: The Streets    Drama,Music,Romance        Jon M. Chu   \n",
+       "996    996            Search Party       Adventure,Comedy    Scot Armstrong   \n",
+       "997    997              Nine Lives  Comedy,Family,Fantasy  Barry Sonnenfeld   \n",
+       "\n",
+       "                                                  Cast  Year  Runtime  Rating  \\\n",
+       "995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2   \n",
+       "996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6   \n",
+       "997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3   \n",
+       "\n",
+       "    Revenue  \n",
+       "995   58.01  \n",
+       "996       0  \n",
+       "997   19.64  "
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4: What is the last year in our DataFrame?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2016"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Year\"].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: What are the rows that correspond to movies whose title contains `\"Harry Potter\"`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Index</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>114</td>\n",
+       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
+       "      <td>Adventure,Drama,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, M...</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>130</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>380.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>314</td>\n",
+       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, B...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>138</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>417</td>\n",
+       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, B...</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>146</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>294.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>472</td>\n",
+       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, M...</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>153</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>301.96</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Index                                         Title  \\\n",
+       "114    114  Harry Potter and the Deathly Hallows: Part 2   \n",
+       "314    314     Harry Potter and the Order of the Phoenix   \n",
+       "417    417  Harry Potter and the Deathly Hallows: Part 1   \n",
+       "472    472        Harry Potter and the Half-Blood Prince   \n",
+       "\n",
+       "                        Genre     Director  \\\n",
+       "114   Adventure,Drama,Fantasy  David Yates   \n",
+       "314  Adventure,Family,Fantasy  David Yates   \n",
+       "417  Adventure,Family,Fantasy  David Yates   \n",
+       "472  Adventure,Family,Fantasy  David Yates   \n",
+       "\n",
+       "                                                  Cast  Year  Runtime  Rating  \\\n",
+       "114  Daniel Radcliffe, Emma Watson, Rupert Grint, M...  2011      130     8.1   \n",
+       "314  Daniel Radcliffe, Emma Watson, Rupert Grint, B...  2007      138     7.5   \n",
+       "417  Daniel Radcliffe, Emma Watson, Rupert Grint, B...  2010      146     7.7   \n",
+       "472  Daniel Radcliffe, Emma Watson, Rupert Grint, M...  2009      153     7.5   \n",
+       "\n",
+       "    Revenue  \n",
+       "114  380.96  \n",
+       "314     292  \n",
+       "417  294.98  \n",
+       "472  301.96  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Title\"].str.contains(\"Harry Potter\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: What is the movie at index 6 ? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index                                                       6\n",
+       "Title                                              La La Land\n",
+       "Genre                                      Comedy,Drama,Music\n",
+       "Director                                      Damien Chazelle\n",
+       "Cast        Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....\n",
+       "Year                                                     2016\n",
+       "Runtime                                                   128\n",
+       "Rating                                                    8.3\n",
+       "Revenue                                               151.06M\n",
+       "Name: 6, dtype: object"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notice that there are two index columns\n",
+    "- That happened because when you write a csv from pandas to a file, it writes a new index column\n",
+    "- So if the dataFrame already contains an index, you are going to get two index columns\n",
+    "- Let's fix that problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can you use slicing to get just columns with Title and Year?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Year</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Split</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sing</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>Secret in Their Eyes</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994</th>\n",
+       "      <td>Hostel: Part II</td>\n",
+       "      <td>2007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>Step Up 2: The Streets</td>\n",
+       "      <td>2008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>2014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>2016</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>998 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Title  Year\n",
+       "0    Guardians of the Galaxy  2014\n",
+       "1                 Prometheus  2012\n",
+       "2                      Split  2016\n",
+       "3                       Sing  2016\n",
+       "4              Suicide Squad  2016\n",
+       "..                       ...   ...\n",
+       "993     Secret in Their Eyes  2015\n",
+       "994          Hostel: Part II  2007\n",
+       "995   Step Up 2: The Streets  2008\n",
+       "996             Search Party  2014\n",
+       "997               Nine Lives  2016\n",
+       "\n",
+       "[998 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = df[[\"Title\", \"Year\"]]\n",
+    "df2 # notice that this does not have the 'index' column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can you use slicing to get rid of the first column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>270.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>Secret in Their Eyes</td>\n",
+       "      <td>Crime,Drama,Mystery</td>\n",
+       "      <td>Billy Ray</td>\n",
+       "      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>111</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>994</th>\n",
+       "      <td>Hostel: Part II</td>\n",
+       "      <td>Horror</td>\n",
+       "      <td>Eli Roth</td>\n",
+       "      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>94</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>17.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>Step Up 2: The Streets</td>\n",
+       "      <td>Drama,Music,Romance</td>\n",
+       "      <td>Jon M. Chu</td>\n",
+       "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>98</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>58.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Adventure,Comedy</td>\n",
+       "      <td>Scot Armstrong</td>\n",
+       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>93</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Comedy,Family,Fantasy</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>19.64</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>998 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Title                     Genre              Director  \\\n",
+       "0    Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn   \n",
+       "1                 Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott   \n",
+       "2                      Split           Horror,Thriller    M. Night Shyamalan   \n",
+       "3                       Sing   Animation,Comedy,Family  Christophe Lourdelet   \n",
+       "4              Suicide Squad  Action,Adventure,Fantasy            David Ayer   \n",
+       "..                       ...                       ...                   ...   \n",
+       "993     Secret in Their Eyes       Crime,Drama,Mystery             Billy Ray   \n",
+       "994          Hostel: Part II                    Horror              Eli Roth   \n",
+       "995   Step Up 2: The Streets       Drama,Music,Romance            Jon M. Chu   \n",
+       "996             Search Party          Adventure,Comedy        Scot Armstrong   \n",
+       "997               Nine Lives     Comedy,Family,Fantasy      Barry Sonnenfeld   \n",
+       "\n",
+       "                                                  Cast  Year  Runtime  Rating  \\\n",
+       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1   \n",
+       "1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0   \n",
+       "2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3   \n",
+       "3    Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2   \n",
+       "4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2   \n",
+       "..                                                 ...   ...      ...     ...   \n",
+       "993  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.2   \n",
+       "994  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     5.5   \n",
+       "995  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     6.2   \n",
+       "996  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     5.6   \n",
+       "997  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.3   \n",
+       "\n",
+       "     Revenue  \n",
+       "0     333.13  \n",
+       "1    126.46M  \n",
+       "2    138.12M  \n",
+       "3     270.32  \n",
+       "4     325.02  \n",
+       "..       ...  \n",
+       "993        0  \n",
+       "994    17.54  \n",
+       "995    58.01  \n",
+       "996        0  \n",
+       "997    19.64  \n",
+       "\n",
+       "[998 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.iloc[:, 1:] # all the rows, not column 0\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write a DataFrame to a CSV file\n",
+    "\n",
+    "It's that easy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"better_movies.csv\", index=False) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Analysis with Data Frames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: What are all the movies that have above average run time? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>La La Land</td>\n",
+       "      <td>Comedy,Drama,Music</td>\n",
+       "      <td>Damien Chazelle</td>\n",
+       "      <td>Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>128</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>151.06M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>977</th>\n",
+       "      <td>The Skin I Live In</td>\n",
+       "      <td>Drama,Thriller</td>\n",
+       "      <td>Pedro Almodóvar</td>\n",
+       "      <td>Antonio Banderas, Elena Anaya, Jan Cornet,Mari...</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>120</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>3.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>979</th>\n",
+       "      <td>Annie</td>\n",
+       "      <td>Comedy,Drama,Family</td>\n",
+       "      <td>Will Gluck</td>\n",
+       "      <td>Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, R...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>118</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>85.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>980</th>\n",
+       "      <td>Across the Universe</td>\n",
+       "      <td>Drama,Fantasy,Musical</td>\n",
+       "      <td>Julie Taymor</td>\n",
+       "      <td>Evan Rachel Wood, Jim Sturgess, Joe Anderson, ...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>133</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>24.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>987</th>\n",
+       "      <td>Selma</td>\n",
+       "      <td>Biography,Drama,History</td>\n",
+       "      <td>Ava DuVernay</td>\n",
+       "      <td>David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>128</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>52.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>989</th>\n",
+       "      <td>Taare Zameen Par</td>\n",
+       "      <td>Drama,Family,Music</td>\n",
+       "      <td>Aamir Khan</td>\n",
+       "      <td>Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>165</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>432 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Title                     Genre            Director  \\\n",
+       "0    Guardians of the Galaxy   Action,Adventure,Sci-Fi          James Gunn   \n",
+       "1                 Prometheus  Adventure,Mystery,Sci-Fi        Ridley Scott   \n",
+       "2                      Split           Horror,Thriller  M. Night Shyamalan   \n",
+       "4              Suicide Squad  Action,Adventure,Fantasy          David Ayer   \n",
+       "6                 La La Land        Comedy,Drama,Music     Damien Chazelle   \n",
+       "..                       ...                       ...                 ...   \n",
+       "977       The Skin I Live In            Drama,Thriller     Pedro Almodóvar   \n",
+       "979                    Annie       Comedy,Drama,Family          Will Gluck   \n",
+       "980      Across the Universe     Drama,Fantasy,Musical        Julie Taymor   \n",
+       "987                    Selma   Biography,Drama,History        Ava DuVernay   \n",
+       "989         Taare Zameen Par        Drama,Family,Music          Aamir Khan   \n",
+       "\n",
+       "                                                  Cast  Year  Runtime  Rating  \\\n",
+       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1   \n",
+       "1    Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0   \n",
+       "2    James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3   \n",
+       "4    Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2   \n",
+       "6    Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....  2016      128     8.3   \n",
+       "..                                                 ...   ...      ...     ...   \n",
+       "977  Antonio Banderas, Elena Anaya, Jan Cornet,Mari...  2011      120     7.6   \n",
+       "979  Quvenzhané Wallis, Cameron Diaz, Jamie Foxx, R...  2014      118     5.3   \n",
+       "980  Evan Rachel Wood, Jim Sturgess, Joe Anderson, ...  2007      133     7.4   \n",
+       "987  David Oyelowo, Carmen Ejogo, Tim Roth, Lorrain...  2014      128     7.5   \n",
+       "989  Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...  2007      165     8.5   \n",
+       "\n",
+       "     Revenue  \n",
+       "0     333.13  \n",
+       "1    126.46M  \n",
+       "2    138.12M  \n",
+       "4     325.02  \n",
+       "6    151.06M  \n",
+       "..       ...  \n",
+       "977     3.19  \n",
+       "979    85.91  \n",
+       "980    24.34  \n",
+       "987    52.07  \n",
+       "989      1.2  \n",
+       "\n",
+       "[432 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "long_movies = df [df[\"Runtime\"] > df[\"Runtime\"].mean()]\n",
+    "long_movies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: Of these movies, what was the minimum rating?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.2"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "min_rating = long_movies[\"Rating\"].min()\n",
+    "min_rating"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5: Which movies had this minimum rating?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>646</th>\n",
+       "      <td>Tall Men</td>\n",
+       "      <td>Fantasy,Horror,Thriller</td>\n",
+       "      <td>Jonathan Holbrook</td>\n",
+       "      <td>Dan Crisafulli, Kay Whitney, Richard Garcia, P...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>133</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Title                    Genre           Director  \\\n",
+       "646  Tall Men  Fantasy,Horror,Thriller  Jonathan Holbrook   \n",
+       "\n",
+       "                                                  Cast  Year  Runtime  Rating  \\\n",
+       "646  Dan Crisafulli, Kay Whitney, Richard Garcia, P...  2016      133     3.2   \n",
+       "\n",
+       "    Revenue  \n",
+       "646       0  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "long_movies[long_movies[\"Rating\"] == min_rating]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 6: What are all `long_movies` with someone in the cast named `\"Emma\"`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>La La Land</td>\n",
+       "      <td>Comedy,Drama,Music</td>\n",
+       "      <td>Damien Chazelle</td>\n",
+       "      <td>Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>128</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>151.06M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>The Help</td>\n",
+       "      <td>Drama</td>\n",
+       "      <td>Tate Taylor</td>\n",
+       "      <td>Emma Stone, Viola Davis, Octavia Spencer, Bryc...</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>146</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>169.71M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
+       "      <td>Adventure,Drama,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, M...</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>130</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>380.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>Crazy, Stupid, Love.</td>\n",
+       "      <td>Comedy,Drama,Romance</td>\n",
+       "      <td>Glenn Ficarra</td>\n",
+       "      <td>Steve Carell, Ryan Gosling, Julianne Moore, Em...</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>118</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>84.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>The Amazing Spider-Man 2</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>Marc Webb</td>\n",
+       "      <td>Andrew Garfield, Emma Stone, Jamie Foxx, Paul ...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>142</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>202.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>314</th>\n",
+       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, B...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>138</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>367</th>\n",
+       "      <td>The Amazing Spider-Man</td>\n",
+       "      <td>Action,Adventure</td>\n",
+       "      <td>Marc Webb</td>\n",
+       "      <td>Andrew Garfield, Emma Stone, Rhys Ifans, Irrfa...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>136</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>262.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, B...</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>146</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>294.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>472</th>\n",
+       "      <td>Harry Potter and the Half-Blood Prince</td>\n",
+       "      <td>Adventure,Family,Fantasy</td>\n",
+       "      <td>David Yates</td>\n",
+       "      <td>Daniel Radcliffe, Emma Watson, Rupert Grint, M...</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>153</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>301.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>609</th>\n",
+       "      <td>Beautiful Creatures</td>\n",
+       "      <td>Drama,Fantasy,Romance</td>\n",
+       "      <td>Richard LaGravenese</td>\n",
+       "      <td>Alice Englert, Viola Davis, Emma Thompson,Alde...</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>124</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>19.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>717</th>\n",
+       "      <td>Noah</td>\n",
+       "      <td>Action,Adventure,Drama</td>\n",
+       "      <td>Darren Aronofsky</td>\n",
+       "      <td>Russell Crowe, Jennifer Connelly, Anthony Hopk...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>138</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>101.16M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>879</th>\n",
+       "      <td>Saving Mr. Banks</td>\n",
+       "      <td>Biography,Comedy,Drama</td>\n",
+       "      <td>John Lee Hancock</td>\n",
+       "      <td>Emma Thompson, Tom Hanks, Annie Rose Buckley, ...</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>125</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>83.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            Title                     Genre  \\\n",
+       "6                                      La La Land        Comedy,Drama,Music   \n",
+       "92                                       The Help                     Drama   \n",
+       "114  Harry Potter and the Deathly Hallows: Part 2   Adventure,Drama,Fantasy   \n",
+       "157                          Crazy, Stupid, Love.      Comedy,Drama,Romance   \n",
+       "253                      The Amazing Spider-Man 2   Action,Adventure,Sci-Fi   \n",
+       "314     Harry Potter and the Order of the Phoenix  Adventure,Family,Fantasy   \n",
+       "367                        The Amazing Spider-Man          Action,Adventure   \n",
+       "417  Harry Potter and the Deathly Hallows: Part 1  Adventure,Family,Fantasy   \n",
+       "472        Harry Potter and the Half-Blood Prince  Adventure,Family,Fantasy   \n",
+       "609                           Beautiful Creatures     Drama,Fantasy,Romance   \n",
+       "717                                          Noah    Action,Adventure,Drama   \n",
+       "879                              Saving Mr. Banks    Biography,Comedy,Drama   \n",
+       "\n",
+       "                Director                                               Cast  \\\n",
+       "6        Damien Chazelle  Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....   \n",
+       "92           Tate Taylor  Emma Stone, Viola Davis, Octavia Spencer, Bryc...   \n",
+       "114          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, M...   \n",
+       "157        Glenn Ficarra  Steve Carell, Ryan Gosling, Julianne Moore, Em...   \n",
+       "253            Marc Webb  Andrew Garfield, Emma Stone, Jamie Foxx, Paul ...   \n",
+       "314          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, B...   \n",
+       "367            Marc Webb  Andrew Garfield, Emma Stone, Rhys Ifans, Irrfa...   \n",
+       "417          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, B...   \n",
+       "472          David Yates  Daniel Radcliffe, Emma Watson, Rupert Grint, M...   \n",
+       "609  Richard LaGravenese  Alice Englert, Viola Davis, Emma Thompson,Alde...   \n",
+       "717     Darren Aronofsky  Russell Crowe, Jennifer Connelly, Anthony Hopk...   \n",
+       "879     John Lee Hancock  Emma Thompson, Tom Hanks, Annie Rose Buckley, ...   \n",
+       "\n",
+       "     Year  Runtime  Rating  Revenue  \n",
+       "6    2016      128     8.3  151.06M  \n",
+       "92   2011      146     8.1  169.71M  \n",
+       "114  2011      130     8.1   380.96  \n",
+       "157  2011      118     7.4    84.24  \n",
+       "253  2014      142     6.7   202.85  \n",
+       "314  2007      138     7.5      292  \n",
+       "367  2012      136     7.0   262.03  \n",
+       "417  2010      146     7.7   294.98  \n",
+       "472  2009      153     7.5   301.96  \n",
+       "609  2013      124     6.2    19.45  \n",
+       "717  2014      138     5.8  101.16M  \n",
+       "879  2013      125     7.5     83.3  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "long_movies[long_movies[\"Cast\"].str.contains(\"Emma\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 7: What is the title of the shortest movie?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Ma vie de Courgette'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "df[df[\"Runtime\"] == df[\"Runtime\"].min()][\"Title\"].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 7: What movie had the highest revenue?\n",
+    "\n",
+    "**Note:** `df[\"Revnue\"].max()` does not work, we need to clean our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_revenue(revenue):\n",
+    "    if type(revenue) == float: # why do we need this? we need this in here if we run code multiple times\n",
+    "        return revenue\n",
+    "    elif revenue[-1] == 'M':\n",
+    "        return float(revenue[:-1])  * 1e6\n",
+    "    else:\n",
+    "        return float(revenue) * 1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    333130000.0\n",
+      "1    126460000.0\n",
+      "2    138120000.0\n",
+      "3    270320000.0\n",
+      "4    325020000.0\n",
+      "Name: Revenue, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Revenue as float</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "      <td>333130000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "      <td>126460000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "      <td>138120000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>270.32</td>\n",
+       "      <td>270320000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "      <td>325020000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Title                     Genre              Director  \\\n",
+       "0  Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn   \n",
+       "1               Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott   \n",
+       "2                    Split           Horror,Thriller    M. Night Shyamalan   \n",
+       "3                     Sing   Animation,Comedy,Family  Christophe Lourdelet   \n",
+       "4            Suicide Squad  Action,Adventure,Fantasy            David Ayer   \n",
+       "\n",
+       "                                                Cast  Year  Runtime  Rating  \\\n",
+       "0  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1   \n",
+       "1  Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0   \n",
+       "2  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3   \n",
+       "3  Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2   \n",
+       "4  Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2   \n",
+       "\n",
+       "   Revenue  Revenue as float  \n",
+       "0   333.13       333130000.0  \n",
+       "1  126.46M       126460000.0  \n",
+       "2  138.12M       138120000.0  \n",
+       "3   270.32       270320000.0  \n",
+       "4   325.02       325020000.0  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "revenue = df[\"Revenue\"].apply(format_revenue) # apply a function to a column\n",
+    "print(revenue.head())\n",
+    "max_revenue = revenue.max()\n",
+    "\n",
+    "# make a copy of our df\n",
+    "rev_df = df.copy()\n",
+    "rev_df[\"Revenue as float\"] = revenue\n",
+    "rev_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now we can answer the question!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Revenue as float</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>J.J. Abrams</td>\n",
+       "      <td>Daisy Ridley, John Boyega, Oscar Isaac, Domhna...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>136</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>936.63</td>\n",
+       "      <td>936630000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         Title                     Genre  \\\n",
+       "50  Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy   \n",
+       "\n",
+       "       Director                                               Cast  Year  \\\n",
+       "50  J.J. Abrams  Daisy Ridley, John Boyega, Oscar Isaac, Domhna...  2015   \n",
+       "\n",
+       "    Runtime  Rating Revenue  Revenue as float  \n",
+       "50      136     8.1  936.63       936630000.0  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rev_df[rev_df[\"Revenue as float\"] == max_revenue]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Or more generally..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "      <th>Revenue as float</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>J.J. Abrams</td>\n",
+       "      <td>Daisy Ridley, John Boyega, Oscar Isaac, Domhna...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>136</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>936.63</td>\n",
+       "      <td>936630000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>Avatar</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>James Cameron</td>\n",
+       "      <td>Sam Worthington, Zoe Saldana, Sigourney Weaver...</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>162</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>760.51</td>\n",
+       "      <td>760510000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>Jurassic World</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>Colin Trevorrow</td>\n",
+       "      <td>Chris Pratt, Bryce Dallas Howard, Ty Simpkins,...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>652.18</td>\n",
+       "      <td>652180000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76</th>\n",
+       "      <td>The Avengers</td>\n",
+       "      <td>Action,Sci-Fi</td>\n",
+       "      <td>Joss Whedon</td>\n",
+       "      <td>Robert Downey Jr., Chris Evans, Scarlett Johan...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>143</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>623.28</td>\n",
+       "      <td>623280000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>The Dark Knight</td>\n",
+       "      <td>Action,Crime,Drama</td>\n",
+       "      <td>Christopher Nolan</td>\n",
+       "      <td>Christian Bale, Heath Ledger, Aaron Eckhart,Mi...</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>152</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>533.32</td>\n",
+       "      <td>533320000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         Title                     Genre  \\\n",
+       "50  Star Wars: Episode VII - The Force Awakens  Action,Adventure,Fantasy   \n",
+       "87                                      Avatar  Action,Adventure,Fantasy   \n",
+       "85                              Jurassic World   Action,Adventure,Sci-Fi   \n",
+       "76                                The Avengers             Action,Sci-Fi   \n",
+       "54                             The Dark Knight        Action,Crime,Drama   \n",
+       "\n",
+       "             Director                                               Cast  \\\n",
+       "50        J.J. Abrams  Daisy Ridley, John Boyega, Oscar Isaac, Domhna...   \n",
+       "87      James Cameron  Sam Worthington, Zoe Saldana, Sigourney Weaver...   \n",
+       "85    Colin Trevorrow  Chris Pratt, Bryce Dallas Howard, Ty Simpkins,...   \n",
+       "76        Joss Whedon  Robert Downey Jr., Chris Evans, Scarlett Johan...   \n",
+       "54  Christopher Nolan  Christian Bale, Heath Ledger, Aaron Eckhart,Mi...   \n",
+       "\n",
+       "    Year  Runtime  Rating Revenue  Revenue as float  \n",
+       "50  2015      136     8.1  936.63       936630000.0  \n",
+       "87  2009      162     7.8  760.51       760510000.0  \n",
+       "85  2015      124     7.0  652.18       652180000.0  \n",
+       "76  2012      143     8.1  623.28       623280000.0  \n",
+       "54  2008      152     9.0  533.32       533320000.0  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rev_df.sort_values(by=\"Revenue as float\", ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Note that because we made a copy of `df` to define `rev_df`, `df` has not been modified"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Cast</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>333.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael   ...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>126.46M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>138.12M</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.2</td>\n",
+       "      <td>270.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>325.02</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     Title                     Genre              Director  \\\n",
+       "0  Guardians of the Galaxy   Action,Adventure,Sci-Fi            James Gunn   \n",
+       "1               Prometheus  Adventure,Mystery,Sci-Fi          Ridley Scott   \n",
+       "2                    Split           Horror,Thriller    M. Night Shyamalan   \n",
+       "3                     Sing   Animation,Comedy,Family  Christophe Lourdelet   \n",
+       "4            Suicide Squad  Action,Adventure,Fantasy            David Ayer   \n",
+       "\n",
+       "                                                Cast  Year  Runtime  Rating  \\\n",
+       "0  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.1   \n",
+       "1  Noomi Rapace, Logan Marshall-Green, Michael   ...  2012      124     7.0   \n",
+       "2  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...  2016      117     7.3   \n",
+       "3  Matthew McConaughey,Reese Witherspoon, Seth Ma...  2016      108     7.2   \n",
+       "4  Will Smith, Jared Leto, Margot Robbie, Viola D...  2016      123     6.2   \n",
+       "\n",
+       "   Revenue  \n",
+       "0   333.13  \n",
+       "1  126.46M  \n",
+       "2  138.12M  \n",
+       "3   270.32  \n",
+       "4   325.02  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 8: What is the average runtime for movies by `\"Francis Lawrence\"`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "126.75"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# write your code here\n",
+    "fl_movies = df[df[\"Director\"] == \"Francis Lawrence\"]\n",
+    "fl_movies[\"Runtime\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More complicated questions..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 8: Among the directors who have directed more than 4 movies, which director had the highest average rating?\n",
+    "\n",
+    "One way is to make a python dict of director, list of ratings, use it to create another dict of director to average rating, and then find the director with the highest average rating."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Christopher Nolan': 8.680000000000001,\n",
+       " 'Martin Scorsese': 7.92,\n",
+       " 'David Fincher': 7.8199999999999985,\n",
+       " 'Denis Villeneuve': 7.76,\n",
+       " 'J.J. Abrams': 7.58,\n",
+       " 'David Yates': 7.433333333333334,\n",
+       " 'Danny Boyle': 7.42,\n",
+       " 'Antoine Fuqua': 7.040000000000001,\n",
+       " 'Zack Snyder': 7.040000000000001,\n",
+       " 'Woody Allen': 7.019999999999999,\n",
+       " 'Peter Berg': 6.860000000000001,\n",
+       " 'Ridley Scott': 6.85,\n",
+       " 'Justin Lin': 6.82,\n",
+       " 'Michael Bay': 6.483333333333334,\n",
+       " 'Paul W.S. Anderson': 5.766666666666666,\n",
+       " 'M. Night Shyamalan': 5.533333333333332}"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "director_dict = dict()\n",
+    "\n",
+    "# make the dictionary: key is director, value is list of ratings\n",
+    "for i in range(len(df)):\n",
+    "    director = df.loc[i, \"Director\"]\n",
+    "    rating = df.loc[i, \"Rating\"]\n",
+    "    if director not in director_dict:\n",
+    "        director_dict[director] = []\n",
+    "    director_dict[director].append(rating)\n",
+    "\n",
+    "# make a ratings dict key is directory, value is average\n",
+    "# only include directors with > 4 movies\n",
+    "ratings_dict = {k:sum(v)/len(v) for (k,v) in director_dict.items() if len(v) > 4}\n",
+    "\n",
+    "# sort a dict by values\n",
+    "dict(sorted(ratings_dict.items(), key=lambda t:t[-1], reverse=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We will now do the same using Pandas.\n",
+    "\n",
+    "##### FOR DEMONSTRATION PURPOSES ONLY\n",
+    "We haven't (and will not) learn about `groupby`. Pandas has many operations which will be helpful! Consider what you already know, and what Pandas can solve when formulating your solutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Director\n",
+       "Christopher Nolan     8.680000\n",
+       "Martin Scorsese       7.920000\n",
+       "David Fincher         7.820000\n",
+       "Denis Villeneuve      7.760000\n",
+       "J.J. Abrams           7.580000\n",
+       "David Yates           7.433333\n",
+       "Danny Boyle           7.420000\n",
+       "Antoine Fuqua         7.040000\n",
+       "Zack Snyder           7.040000\n",
+       "Woody Allen           7.020000\n",
+       "Peter Berg            6.860000\n",
+       "Ridley Scott          6.850000\n",
+       "Justin Lin            6.820000\n",
+       "Michael Bay           6.483333\n",
+       "Paul W.S. Anderson    5.766667\n",
+       "M. Night Shyamalan    5.533333\n",
+       "Name: Rating, dtype: float64"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rating_groups = df.groupby(\"Director\")[\"Rating\"]\n",
+    "rating_groups.mean()[rating_groups.count() > 4].sort_values(ascending=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Template.ipynb b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Template.ipynb
new file mode 100644
index 0000000..83f5cb2
--- /dev/null
+++ b/s24/AmFam_Ashwin/28_Pandas-2/Lecture Code/Lec28_Pandas2_Template.ipynb	
@@ -0,0 +1,1219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 0: Importing from `pandas`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "# We can explictly import Series and DataFrame, why might we do this?\n",
+    "from pandas import Series, DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 1: Series from a list\n",
+    "\n",
+    "In the **Series** below,\n",
+    "1. What name do we call the  0, 1, 2, ... ?\n",
+    "2. What name do we call the  54, 22, 19, .... ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_list = [54, 22, 19, 73, 80]\n",
+    "scores_series = Series(scores_list)\n",
+    "scores_series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 2: What are all the scores `> 50`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are really writing a \"mask\" for our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_series > 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_series[[True, True, False, False, True]] # often called a \"mask\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_series[scores_series > 50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 3: Series from a dict\n",
+    "\n",
+    "Imagine we hire students and track their weekly hours. For everyone in Week 1, add 3 to their hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "week1 = Series({\"Rita\":5, \"Therese\":3, \"Janice\": 6})\n",
+    "week2 = Series({\"Rita\":3, \"Therese\":7, \"Janice\": 4})\n",
+    "week3 = Series({\"Therese\":5, \"Janice\":5, \"Rita\": 8}) # Wrong order! Will this matter?\n",
+    "print(week1)\n",
+    "print(week2)\n",
+    "print(week3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 4: Find the total hours of everyone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here\n",
+    "total_hours = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 5: What is `week1/week3`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "week1/week3 # notice that we don't have to worry about the order of indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 6: What type of values are stored in `week1 > week2`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(week1)\n",
+    "print(week2)\n",
+    "week1 > week2 # note that the indices are ordered the same"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup 7:  What is `week1 > week3`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(week1)\n",
+    "print(week3)\n",
+    "week1 > week3 # indices not in same order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lecture 28:  Pandas 2 - DataFrames\n",
+    "\n",
+    "Learning Objectives:\n",
+    "- Create a **DataFrame** from \n",
+    " - a **dictionary** of **Series**, **lists**, or **dicts**\n",
+    " - a **list** of **Series**, **lists**, **dicts**\n",
+    "- Select a column, row, cell, or rectangular region of a **DataFrame**\n",
+    "- Convert CSV files into **DataFrames** and **DataFrames** into CSV Files\n",
+    "- Access the head or tail of a **DataFrame**\n",
+    "\n",
+    "**Big Idea**: Data Frames store 2-dimensional data in tables! It is a collection of Series."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## You can create a DataFrame in a variety of ways!\n",
+    "### From a dictionary of Series"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = Series([\"Alice\", \"Bob\", \"Cindy\", \"Dan\"])\n",
+    "scores = Series([6, 7, 8, 9])\n",
+    "\n",
+    "# to make a dictionary of Series, need to write column names for the keys\n",
+    "DataFrame({\n",
+    "    \"Player name\": names,\n",
+    "    \"Score\": scores\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a dictionary of lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name_list = [\"Alice\", \"Bob\", \"Cindy\", \"Dan\"]\n",
+    "score_list = [6, 7, 8, 9]\n",
+    "# this is the same as above, reminding us that Series act like lists\n",
+    "DataFrame({\n",
+    "    \"Player name\": name_list,\n",
+    "    \"Score\": score_list\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a dictionary of dictionaries\n",
+    "We need to make up keys to match the things in each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {\n",
+    "    \"Player name\": {0: \"Alice\", 1: \"Bob\", 2: \"Cindy\", 3: \"Dan\"},\n",
+    "    \"Score\": {0: 6, 1: 7, 2: 8, 3: 9}\n",
+    "}\n",
+    "DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a list of lists\n",
+    "We have to add the column names, we do this with `columns = [name1, name2, ....]` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    [\"Alice\", 6],\n",
+    "    [\"Bob\", 7],\n",
+    "    [\"Cindy\", 8],\n",
+    "    [\"Dan\", 9]\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data, columns=[\"Player name\", \"Score\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From a list of dicts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    {\"Player name\": \"Alice\", \"Score\": 6},\n",
+    "    {\"Player name\": \"Bob\", \"Score\": 7},\n",
+    "    {\"Player name\": \"Cindy\", \"Score\": 8},\n",
+    "    {\"Player name\": \"Dan\", \"Score\": 9}\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explicitly naming the indices\n",
+    "We can use `index = [name1, name2, ...]` to rename the index of each row"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [\n",
+    "    {\"Player name\": \"Alice\", \"Score\": 6},\n",
+    "    {\"Player name\": \"Bob\", \"Score\": 7},\n",
+    "    {\"Player name\": \"Cindy\", \"Score\": 8},\n",
+    "    {\"Player name\": \"Dan\", \"Score\": 9}\n",
+    "]\n",
+    "data\n",
+    "DataFrame(data, index=[\"A\", \"B\", \"C\", \"D\"]) # must have a name for each row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 1: Make a DataFrame of 4 people you know with different ages. Give names to both the columns and rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Select a column, row, cell, or rectangular region of a DataFrame\n",
+    "### Data lookup: Series\n",
+    "- `s.loc[X]`   <- lookup by pandas index\n",
+    "- `s.iloc[X]`  <- lookup by integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hours = Series({\"Alice\":6, \"Bob\":7, \"Cindy\":8, \"Dan\":9})\n",
+    "hours"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lookup using Series index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hours.loc[\"Bob\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lookup using Series integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hours.iloc[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 2: Find Cindy's hours using both the Series index and the integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(hours.loc[\"Cindy\"])\n",
+    "print(hours.iloc[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Data lookup: DataFrame\n",
+    "\n",
+    "\n",
+    "- `d.loc[r]`     lookup ROW by pandas ROW index\n",
+    "- `d.iloc[r]`    lookup ROW by ROW integer position\n",
+    "- `d[c]`         lookup COL by pandas COL index\n",
+    "- `d.loc[r, c]`  lookup by pandas ROW index and pandas COL index\n",
+    "- `d.iloc[r, c]`  lookup by ROW integer position and COL integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We often call the object that we make df\n",
+    "data = [\n",
+    "    [\"Alice\", 6],\n",
+    "    [\"Bob\", 7],\n",
+    "    [\"Cindy\", 8],\n",
+    "    [\"Dan\", 9]\n",
+    "]\n",
+    "df = DataFrame(data, index=[\"A\", \"B\", \"C\", \"D\"], columns = [\"Player name\", \"Score\"])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Different ways of accessing row D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df[\"D\"])\n",
+    "print(df.loc[\"D\"])\n",
+    "print(df.iloc[3])\n",
+    "print(df.iloc[-1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How about accessing a column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df[0])\n",
+    "print(df[\"Player name\"])\n",
+    "print(df.loc[\"Player name\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Different ways to access a single cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df[\"Player name\"][\"C\"])\n",
+    "print(df.loc[\"C\"][\"Player name\"])\n",
+    "print(df.loc[\"C\", \"Player name\"])\n",
+    "print(df[\"C\", \"Player name\"])\n",
+    "print(df[\"Player name\"].loc[\"C\"])\n",
+    "print(df.iloc[2, 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to set values for a specific entry?\n",
+    "\n",
+    "- `d.loc[r, c] = new_val`\n",
+    "- `d.iloc[r, c] = new_val`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Change player D's name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[\"D\", \"Player name\"] = \"Bianca\"\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 3 to that player's score using `.loc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[\"D\", \"Score\"] += 3\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 7 to a player's score using `.iloc`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.iloc[0, 1] += 7\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the max score and the mean score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(df[\"Score\"].max(), df[\"Score\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find the highest scoring player"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "highest_scorer = df[\"Score\"].idxmax()\n",
+    "df[\"Player name\"].loc[highest_scorer]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Slicing a DataFrame\n",
+    "\n",
+    "- `df.iloc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using integer positions\n",
+    "- `df.loc[ROW_SLICE, COL_SLICE]` <- make a rectangular slice from the DataFrame using index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.iloc[1:3, 0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[\"B\":\"C\", \"Player name\":\"Score\"] # notice that this way is inclusive of endpoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set values for sliced DataFrame\n",
+    "\n",
+    "- `d.loc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW INDEX and COL INDEX\n",
+    "- `d.iloc[ROW_SLICE, COL_SLICE] = new_val` <- set value by ROW Integer position and COL Integer position"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[\"B\":\"C\", \"Score\"] += 5\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pandas allows slicing of non-contiguous columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Just get Player name for Index B and D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[[\"B\", \"D\"], \"Player name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Add 3 to the people in rows B and D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[[\"B\", \"D\"], \"Score\"] += 3\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boolean indexing on a DataFrame\n",
+    "\n",
+    "- `d[BOOL SERIES]`  <- makes a new DF of all rows that lined up were True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a Series of Booleans based on Score >= 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bool_index = df[\"Score\"] >= 15\n",
+    "bool_index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Use `bool_index` to slice the DataFrame\n",
+    "\n",
+    "If `bool_index` is `True`, include this row in the new df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[bool_index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Do the last two things in a single step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df[\"Score\"] >= 15]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating DataFrame from CSV file\n",
+    "\n",
+    "It's that easy! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"IMDB-Movie-Data.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 1: View the first few lines of the DataFrame\n",
+    "- `.head(n)` gets the first n lines, 5 is the default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2: Get the first 2 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 3: View the last few lines of the DataFrame\n",
+    "- `.tail(n)` gets the last n lines, 5 is the default"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 4: What is the last year in our DataFrame?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Year\"].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 5: What are the rows that correspond to movies whose title contains `\"Harry Potter\"`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df[\"Title\"].str.contains(\"Harry Potter\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 6: What is the movie at index 6 ? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notice that there are two index columns\n",
+    "- That happened because when you write a csv from pandas to a file, it writes a new index column\n",
+    "- So if the dataFrame already contains an index, you are going to get two index columns\n",
+    "- Let's fix that problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can you use slicing to get just columns with Title and Year?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = df[[\"Title\", \"Year\"]]\n",
+    "df2 # notice that this does not have the 'index' column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can you use slicing to get rid of the first column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.iloc[:, 1:] # all the rows, not column 0\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Write a DataFrame to a CSV file\n",
+    "\n",
+    "It's that easy!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"better_movies.csv\", index=False) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Analysis with Data Frames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: What are all the movies that have above average run time? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here\n",
+    "long_movies = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: Of these movies, what was the minimum rating?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here\n",
+    "min_rating = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 5: Which movies had this minimum rating?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 6: What are all `long_movies` with someone in the cast named `\"Emma\"`? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 7: What is the title of the shortest movie?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 7: What movie had the highest revenue?\n",
+    "\n",
+    "**Note:** `df[\"Revnue\"].max()` does not work, we need to clean our data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def format_revenue(revenue):\n",
+    "    if type(revenue) == float: # why do we need this?\n",
+    "        return revenue\n",
+    "    elif revenue[-1] == 'M':\n",
+    "        return float(revenue[:-1])  * 1e6\n",
+    "    else:\n",
+    "        return float(revenue) * 1e6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "revenue = df[\"Revenue\"].apply(format_revenue) # apply a function to a column\n",
+    "print(revenue.head())\n",
+    "max_revenue = revenue.max()\n",
+    "\n",
+    "# make a copy of our df\n",
+    "rev_df = df.copy()\n",
+    "rev_df[\"Revenue as float\"] = revenue\n",
+    "rev_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Now we can answer the question!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_df[rev_df[\"Revenue as float\"] == max_revenue]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Or more generally..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rev_df.sort_values(by=\"Revenue as float\", ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Note that because we made a copy of `df` to define `rev_df`, `df` has not been modified"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise 8: What is the average runtime for movies by `\"Francis Lawrence\"`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More complicated questions..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 8: Among the directors who have directed more than 4 movies, which director had the highest average rating?\n",
+    "\n",
+    "One way is to make a python dict of director, list of ratings, use it to create another dict of director to average rating, and then find the director with the highest average rating."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "director_dict = dict()\n",
+    "\n",
+    "# make the dictionary: key is director, value is list of ratings\n",
+    "for i in range(len(df)):\n",
+    "    director = df.loc[i, \"Director\"]\n",
+    "    rating = df.loc[i, \"Rating\"]\n",
+    "    if director not in director_dict:\n",
+    "        director_dict[director] = []\n",
+    "    director_dict[director].append(rating)\n",
+    "\n",
+    "# make a ratings dict key is directory, value is average\n",
+    "# only include directors with > 4 movies\n",
+    "ratings_dict = {k:sum(v)/len(v) for (k,v) in director_dict.items() if len(v) > 4}\n",
+    "\n",
+    "# sort a dict by values\n",
+    "dict(sorted(ratings_dict.items(), key=lambda t:t[-1], reverse=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We will now do the same using Pandas.\n",
+    "\n",
+    "##### FOR DEMONSTRATION PURPOSES ONLY\n",
+    "We haven't (and will not) learn about `groupby`. Pandas has many operations which will be helpful! Consider what you already know, and what Pandas can solve when formulating your solutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rating_groups = df.groupby(\"Director\")[\"Rating\"]\n",
+    "rating_groups.mean()[rating_groups.count() > 4].sort_values(ascending=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s24/AmFam_Ashwin/28_Pandas-2/lec-28-worksheet.pdf b/s24/AmFam_Ashwin/28_Pandas-2/lec-28-worksheet.pdf
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literal 0
HcmV?d00001

diff --git a/s24/AmFam_Ashwin/28_Pandas-2/storms.csv b/s24/AmFam_Ashwin/28_Pandas-2/storms.csv
new file mode 100644
index 0000000..50e06e7
--- /dev/null
+++ b/s24/AmFam_Ashwin/28_Pandas-2/storms.csv
@@ -0,0 +1,6 @@
+name,year,type,speed,place
+alice,2016,tornado,100,o
+bob,2016,hurricane,200,p
+cindy,2017,tornado,150,o
+dan,2018,tornado,300,o
+eve,2018,hurricane,250,a
\ No newline at end of file
-- 
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