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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lecture 11 Announcements\n",
+    "* Quiz 4 is due today (covers iteration, strings, lists)\n",
+    "* Quiz 5 is assigned today and due Friday (covers lists (including topics like sets and `sorted` vs `sort` from Lab 6), CSV files, and half of this lecture, dictionaries)\n",
+    "    * I recommend waiting until after Lab 7 to take this quiz, because Lab 7 will give you extra practice on working with CSV files and Dictionaries\n",
+    "* Please take the *Midterm Course Feedback Survey* by Friday. A link was emailed to you with the subject line \"Please Complete Your UW-Madison Course Evaluation Survey\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists `csv_data`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inspired by https://automatetheboringstuff.com/2e/chapter16/\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",
+    "    \n",
+    "    return example_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "993"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csv_data = process_csv('cs220_survey_data.csv') \n",
+    "\n",
+    "# TODO: compute the length of this list of lists \n",
+    "len(csv_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 2: store the first row in a variable called `cs220_header`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_header = csv_data[0] # TODO: change this\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 3: store the rest of the data in a variable called `cs220_data`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LEC001',\n",
+       " '22',\n",
+       " 'Engineering: Biomedical',\n",
+       " '53703',\n",
+       " '43.073051',\n",
+       " '-89.40123',\n",
+       " 'none (just cheese)',\n",
+       " 'neither',\n",
+       " 'No',\n",
+       " 'no preference',\n",
+       " 'Maybe']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data = csv_data[1:] # TODO: change this\n",
+    "cs220_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 4: show the last 3 rows of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['LEC001',\n",
+       "  '18',\n",
+       "  'Undecided',\n",
+       "  '53706',\n",
+       "  '44.8341',\n",
+       "  '87.377',\n",
+       "  'basil/spinach',\n",
+       "  'dog',\n",
+       "  'No',\n",
+       "  'no preference',\n",
+       "  'Yes'],\n",
+       " ['LEC003',\n",
+       "  '19',\n",
+       "  'Engineering: Mechanical',\n",
+       "  '53705',\n",
+       "  '46.589146',\n",
+       "  '-112.039108',\n",
+       "  'none (just cheese)',\n",
+       "  'cat',\n",
+       "  'No',\n",
+       "  'night owl',\n",
+       "  'Yes'],\n",
+       " ['LEC001',\n",
+       "  '20',\n",
+       "  'Economics',\n",
+       "  '53703',\n",
+       "  '39.631506',\n",
+       "  '118.143239',\n",
+       "  'pineapple',\n",
+       "  'dog',\n",
+       "  'No',\n",
+       "  'night owl',\n",
+       "  'Maybe']]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[-3:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 5: what is the output of `cs220_data[-1:]`\n",
+    "\n",
+    "- Be careful with slicing.\n",
+    "- Slicing a string gives a new string\n",
+    "- Slicing a list gives a new list\n",
+    "- Slicing a list of list will always give a new list of list (even if your slice only contains one of the inner lists)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['LEC001',\n",
+       "  '20',\n",
+       "  'Economics',\n",
+       "  '53703',\n",
+       "  '39.631506',\n",
+       "  '118.143239',\n",
+       "  'pineapple',\n",
+       "  'dog',\n",
+       "  'No',\n",
+       "  'night owl',\n",
+       "  'Maybe']]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[-1:] # slicing, returns a list of lists (in this case)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['LEC001',\n",
+       " '20',\n",
+       " 'Economics',\n",
+       " '53703',\n",
+       " '39.631506',\n",
+       " '118.143239',\n",
+       " 'pineapple',\n",
+       " 'dog',\n",
+       " 'No',\n",
+       " 'night owl',\n",
+       " 'Maybe']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cs220_data[-1] # indexing, returns a list (in this case)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Warmup 6: Write a function that counts the frequency of a value in a column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def column_frequency(value, col_name):\n",
+    "    ''' Returns the frequency of value in col_name. '''\n",
+    "    count = 0\n",
+    "    for row in cs220_data:\n",
+    "        if row[cs220_header.index(col_name)].lower() == value.lower():\n",
+    "            count += 1\n",
+    "    return count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "112"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Test your function\n",
+    "column_frequency(\"pineapple\", \"Pizza topping\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "331"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Try other test cases\n",
+    "column_frequency(\"53703\", \"Zip Code\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### TODO: Discuss: Is there an easy way to count *every* topping's frequency?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "- Use correct dictionary syntax \n",
+    "     - to create a dictionary using either {} or dict()\n",
+    "     - to lookup, insert, update, and pop key-value pairs\n",
+    "- Use a for loop, the in operator, and common methods when working with dictionaries.\n",
+    "- Write code that uses a dictionary\n",
+    "     - to store frequencies\n",
+    "     - to iterate through all key-value pairs\n",
+    "\n",
+    " - Handle key errors with get and pop using default values\n",
+    " - Understand the idea of nesting data structures\n",
+    " - Use a dictionary of lists to put rows of data into \"buckets\"\n",
+    " - Use a list of dictionaries to represent a table of data.\n",
+    " - Create a dictionary of dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 1: Dictionary intro"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Structure \n",
+    "A data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data (source: Wikipedia).\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:Lists.png\" width=\"500\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python contains built-in Data Structures called Collections\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:Collections.png\" width=\"500\"/>\n",
+    "</div>\n",
+    "\n",
+    "Today we'll learn how store data and perform various operations in Dictionaries. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mappings\n",
+    "\n",
+    "Common data structure approach:\n",
+    "- store many values\n",
+    "- give each value a label\n",
+    "- use labels to lookup values\n",
+    "\n",
+    "`list` is an example of a mapping-based data structure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "400"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# index       0    1    2    3\n",
+    "nums_list = [300, 200, 400, 100]\n",
+    "nums_list[2] # lookup using index label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Labels in a list are inflexible. They can only be consecutive `int`s starting at label 0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionary\n",
+    "\n",
+    "A dictionary (`dict`) is like a `list`, but more general. In a list, the indices have to be integers; but a dictionary they can be any **immutable** type. Just like lists, values can be anything.\n",
+    "\n",
+    "You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item. \n",
+    "\n",
+    "(from Think Python, Chapter 11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# empty dictionary\n",
+    "some_dict = {}       # we use curly braces to create a dictionary\n",
+    "# We'll shortly discuss about dict versus set\n",
+    "\n",
+    "# empty dictionary\n",
+    "some_other_dict = dict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(some_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like a `list`, `dict` key-value pairs are separated by a `,`.\n",
+    "\n",
+    "The `key` and the `value` are separated by a `:`. That is `key:value`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[300, 200, 400, 100]\n",
+      "{0: 300, 1: 200, 2: 400, 3: 100}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: let's define nums_dict\n",
+    "\n",
+    "nums_list = [300, 200, 400, 100]\n",
+    "print(nums_list)\n",
+    "\n",
+    "nums_dict = {0 : 300, 1: 200, 2: 400, 3:100}\n",
+    "print(nums_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'broccoli': 3.95,\n",
+       " 'spinach': 1.5,\n",
+       " 'donut': 1.25,\n",
+       " 'muffin': 2.25,\n",
+       " 'brownie': 3.15,\n",
+       " 'cookie': 0.79,\n",
+       " 'milk': 1.65,\n",
+       " 'loaf': 5.99,\n",
+       " 'cauliflower': 3.99}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# a dictionary that stores prices of bakery items\n",
+    "# Notice that a dict can span over more than one line, indentation doesn't matter\n",
+    "\n",
+    "price_dict = { 'broccoli': 3.95, \n",
+    "         'spinach': 1.50, \n",
+    "         'donut': 1.25,     'muffin': 2.25,\n",
+    "    'brownie': 3.15,\n",
+    "         'cookie': 0.79,  'milk': 1.65, 'loaf': 5.99,\n",
+    "        'cauliflower': 3.99}  # feel free to add some of your own here\n",
+    "price_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.95"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# indexing with lists\n",
+    "items = ['broccoli', 'spinach', 'donut']\n",
+    "price_list = [3.95, 1.50, 1.25]\n",
+    "\n",
+    "price_list[ items.index('broccoli') ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.95"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_dict['broccoli']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Dictionaries maintain insertion based ordering in recent versions of Python (3.7 and above versions).\n",
+    "\n",
+    "- Go back to the previous cell and add 'ice cream': 3.99 key-value pair before 'brownie': 3.15. Re-run the cell to see how the `dict` definition changes with respect to insertion order."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionary lookups\n",
+    "\n",
+    "- same syntax as `list` indexing\n",
+    "- `some_dict[key]`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lookup price of 'brownie', 'cookie', and 'ice cream'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.15\n",
+      "0.79\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'ice cream'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[31], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28mprint\u001b[39m(price_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbrownie\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(price_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcookie\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mprice_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mice cream\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'ice cream'"
+     ]
+    }
+   ],
+   "source": [
+    "print(price_dict['brownie'])\n",
+    "print(price_dict['cookie'])\n",
+    "print(price_dict['ice cream'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lookup price of 'sugar'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'sugar'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mprice_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msugar\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'sugar'"
+     ]
+    }
+   ],
+   "source": [
+    "print(price_dict['sugar'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Can you perform lookup using values? Answer is no, the mapping is one-way, that is key to value and not vice versa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "0.79",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[33], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mprice_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.79\u001b[39;49m\u001b[43m]\u001b[49m) \u001b[38;5;66;03m# KeyError\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 0.79"
+     ]
+    }
+   ],
+   "source": [
+    "print(price_dict[0.79]) # KeyError"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Parenthetical_characters_1.png\" width=\"700\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Parenthetical_characters_2.png\" width=\"500\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionaries are Mutable\n",
+    "\n",
+    "- update existing key's value\n",
+    "- insert a new key-value pair\n",
+    "- pop method to delete a key-value pair"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'broccoli': 3.95,\n",
+       " 'spinach': 1.5,\n",
+       " 'donut': 1.25,\n",
+       " 'muffin': 2.25,\n",
+       " 'brownie': 3.15,\n",
+       " 'cookie': 0.79,\n",
+       " 'milk': 1.65,\n",
+       " 'loaf': 5.99,\n",
+       " 'cauliflower': 2.99}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: change price of 'cauliflower' to 2.99\n",
+    "\n",
+    "price_dict['cauliflower'] = 2.99\n",
+    "price_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'broccoli': 3.95,\n",
+       " 'spinach': 1.5,\n",
+       " 'donut': 1.25,\n",
+       " 'muffin': 2.25,\n",
+       " 'brownie': 3.15,\n",
+       " 'cookie': 0.79,\n",
+       " 'milk': 1.65,\n",
+       " 'loaf': 5.99,\n",
+       " 'cauliflower': 2.99,\n",
+       " 'carrot': 1.99}"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: insert new key-value pair 'carrot' mapping to 1.99\n",
+    "\n",
+    "price_dict['carrot'] = 1.99\n",
+    "price_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20, 30, 40]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# indices.   0.   1.  2  \n",
+    "nums_list = [10, 20, 30]\n",
+    "# nums_list[3] = 40 # Recall that this doesn't work on a list due to IndexError\n",
+    "\n",
+    "# TODO: comment out line 2 and use proper syntax to add item 40 to nums_list\n",
+    "nums_list.append(40)\n",
+    "\n",
+    "nums_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'pizza'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[39], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m(price_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdonut\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# try deleting someting that is not there\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[43mprice_dict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpizza\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# KeyError\u001b[39;00m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'pizza'"
+     ]
+    }
+   ],
+   "source": [
+    "# use pop to delete the 'spinach' key-value pair\n",
+    "price_dict.pop('spinach')\n",
+    "\n",
+    "# Alternate\n",
+    "del(price_dict['donut'])\n",
+    "\n",
+    "# try deleting someting that is not there\n",
+    "price_dict.pop('pizza') # KeyError"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'broccoli': 3.95,\n",
+       " 'muffin': 2.25,\n",
+       " 'brownie': 3.15,\n",
+       " 'cookie': 0.79,\n",
+       " 'milk': 1.65,\n",
+       " 'loaf': 5.99,\n",
+       " 'cauliflower': 2.99,\n",
+       " 'carrot': 1.99}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `in` operator enables us to check whether a key exists in the dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "40 in nums_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Oops couldn't find it!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: fix the above example with a conditional\n",
+    "if 'pizza' in price_dict:\n",
+    "    price_dict.pop('pizza')\n",
+    "else:\n",
+    "    print(\"Oops couldn't find it!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `len` built-in function returns the number of key-value pairs in a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: print length of price_dict\n",
+    "len(price_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `for` loop enables us to iterate over keys in a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "broccoli 3.95\n",
+      "muffin 2.25\n",
+      "brownie 3.15\n",
+      "cookie 0.79\n",
+      "milk 1.65\n",
+      "loaf 5.99\n",
+      "cauliflower 2.99\n",
+      "carrot 1.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: iterate over price_dict and print each key-value pair in its own line\n",
+    "for key in price_dict:\n",
+    "    print(key, price_dict[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `keys` method\n",
+    "\n",
+    "- retrieves keys of a dictionary\n",
+    "- can be converted into a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_keys(['broccoli', 'muffin', 'brownie', 'cookie', 'milk', 'loaf', 'cauliflower', 'carrot'])\n",
+      "<class 'dict_keys'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get all keys and convert to a list\n",
+    "print(price_dict.keys())\n",
+    "print(type(price_dict.keys()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "broccoli 3.95\n",
+      "muffin 2.25\n",
+      "brownie 3.15\n",
+      "cookie 0.79\n",
+      "milk 1.65\n",
+      "loaf 5.99\n",
+      "cauliflower 2.99\n",
+      "carrot 1.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in price_dict.keys():\n",
+    "    print(key, price_dict[key])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `values` method\n",
+    "\n",
+    "- retrieves values of a dictionary\n",
+    "- can be converted into a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_values([3.95, 2.25, 3.15, 0.79, 1.65, 5.99, 2.99, 1.99])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get all values and convert to a list\n",
+    "print(price_dict.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "False\n",
+      "False\n",
+      "False\n",
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use 'in' price_dict, price_dict.keys(), price_dict.values()\n",
+    "\n",
+    "print('donut' in price_dict)          # default is to check the keys\n",
+    "print(9.95 in price_dict)             # default is NOT values\n",
+    "print('apple' in price_dict.keys())   # can call out the keys\n",
+    "print(3.95 in price_dict.values())    # can check the values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 1: find total cost of shopping order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['pie', 'donut', 'milk', 'cookie', 'tofu']\n",
+      "Couldn't find pie in price list!\n",
+      "Couldn't find donut in price list!\n",
+      "Couldn't find tofu in price list!\n",
+      "Your total cost is $2.44\n"
+     ]
+    }
+   ],
+   "source": [
+    "order = ['pie', 'donut', 'milk', 'cookie', 'tofu'] # add more items to the order\n",
+    "print(order)\n",
+    "\n",
+    "total_cost = 0\n",
+    "for key in order:\n",
+    "    # TODO: check if item is a key in price_dict\n",
+    " #            if yes, retrieve the value and add it to total_cost\n",
+    "    if key in price_dict:\n",
+    "        value = price_dict[key]\n",
+    "        total_cost += value\n",
+    "    #            if not, display \"Couldn't find <item> in price list!\"\n",
+    "    else:\n",
+    "        print(\"Couldn't find\", key, \"in price list!\")\n",
+    "        \n",
+    "# find the total of the items in the order\n",
+    "print (\"Your total cost is ${:.2f}\".format(total_cost))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2a: find the letter that occurred the most in a sentence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'M': 1, 'e': 6, 't': 6, 'm': 1, 'a': 5, 'h': 2, 'b': 1, 'i': 1, 'k': 2, 'r': 2, 'c': 2, 's': 2, 'f': 1, 'o': 3, 'l': 1, 'd': 1, 'y': 1}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# start with an empty dictionary\n",
+    "letter_freq = {} # KEY: unique letter; VALUE: count of unique letter\n",
+    "\n",
+    "sentence = \"Meet me at the bike racks after school at 3:30 today.\"\n",
+    "\n",
+    "for letter in sentence:\n",
+    "    if letter in [' ', ':', '.', '3', '0']:\n",
+    "        continue\n",
+    "    # TODO: check if letter is a key in letter_freq\n",
+    "    if letter in letter_freq.keys():\n",
+    "        letter_freq[letter] += 1\n",
+    "        # shorthand for:\n",
+    "        # letter_freq[letter] = letter_freq[letter] + 1\n",
+    "    #           if yes, increment letter frequency by 1\n",
+    "    else:\n",
+    "        letter_freq[letter] = 1\n",
+    "    #           if no, insert a new key-value pair\n",
+    "\n",
+    "print(letter_freq)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 2b: find the letter that occurred the most"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The character \"e\" appeared 6 times.\n"
+     ]
+    }
+   ],
+   "source": [
+    "most_used_key = None  \n",
+    "max_value = None\n",
+    "\n",
+    "for letter in letter_freq:\n",
+    "    # TODO: you already know how to use a for loop to compute max\n",
+    "    if max_value is None or letter_freq[letter]>max_value:\n",
+    "        most_used_key = letter\n",
+    "        max_value = letter_freq[letter]\n",
+    "\n",
+    "print(\"The character \\\"{}\\\" appeared {} times.\".format(str(most_used_key), max_value))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# TODO: discuss: why not use range-based for loop?\n",
+    "\n",
+    "for i in range(len(letter_freq)):\n",
+    "    print(i) # can you do anything with i in this letter_freq dictionary?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 3a: Survey dataset: count every primary major's frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "major_freq = {} # KEY: ???; VALUE: ???\n",
+    "\n",
+    "# TODO: iterate over each student's data from cs220_data\n",
+    "# TODO: extract \"Primary major\" column's value \n",
+    "# TODO: check if current student's major already a key in major_freq\n",
+    "#            - if yes, increase the corresponding value by 1\n",
+    "#            - if no, insert a new key-value pair\n",
+    "\n",
+    "major_freq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example 3b: find primary major with highest frequency"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Example 3b: use the algorithm from 2b to find the major with the highest frequency\n",
+    "\n",
+    "\n",
+    "print(\"The major \\\"{}\\\" appeared {} times.\".format(str(most_used_key), max_value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "End of material for Quiz 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After Lecture Practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Organize your data structure notes ...\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:DataStructure_notes.png\" width=\"700\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Review slide deck"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Review this summary of common dictionary methods:\n",
+    "https://www.w3schools.com/python/python_ref_dictionary.asp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Part 2: Operations and Nesting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Default values with `get` and `pop` methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'broccoli': 3.95,\n",
+       " 'muffin': 2.25,\n",
+       " 'brownie': 3.15,\n",
+       " 'cookie': 0.79,\n",
+       " 'milk': 1.65,\n",
+       " 'loaf': 5.99,\n",
+       " 'cauliflower': 2.99,\n",
+       " 'carrot': 1.99}"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the following two statements are equivalent\n",
+    "price_dict.get('broccoli')\n",
+    "price_dict['broccoli']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "st\n",
+      "None\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "4",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[61], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(suffix\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;241m4\u001b[39m))\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# TODO: what happens whey you try to pop a key that is not there? Try it.\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msuffix\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 4"
+     ]
+    }
+   ],
+   "source": [
+    "suffix = {1: \"st\", 2: 'nd', 3: \"rd\"}\n",
+    "print(suffix.get(1))\n",
+    "\n",
+    "# TODO: what happens when you try to get a key that is not there? Try it.\n",
+    "print(suffix.get(4))\n",
+    "\n",
+    "# TODO: what happens whey you try to pop a key that is not there? Try it.\n",
+    "print(suffix.pop(4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`get` and `pop` methods accept a second argument, which will be the default value if the first argument (key) does not exist.\n",
+    "\n",
+    "Syntax:\n",
+    "- `some_dict.get(some_key, default_value)`\n",
+    "- `some_dict.pop(some_key, default_value)`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rd\n",
+      "th\n",
+      "th\n",
+      "nd\n",
+      "{1: 'st', 3: 'rd'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# get(key, default value) \n",
+    "print(suffix.get(3, 'th'))\n",
+    "print(suffix.get(5, 'th')) #default value, but does not add the key-value pair to the dict\n",
+    "\n",
+    "# pop(key, default value)\n",
+    "print(suffix.pop(7, 'th')) # no key-value pair to remove\n",
+    "print(suffix.pop(2, 'th'))\n",
+    "print(suffix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What are nested data structures?\n",
+    "A data structure containing another data structure as item is called as nest data structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nesting part 1: Bucketizing/Binning"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Buckets.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step1.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Binning_step2.png\" width=\"600\"/>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bucketizing/Binning process objective: build dict of list of lists data structure\n",
+    "- Initialize an empty `dict`\n",
+    "- Iterate over every row in your dataset\n",
+    "    - Retrieve value of the column based on which you want to bucketize\n",
+    "    - Check if bucketizing column is already a key in your `dict`:\n",
+    "        - if no, insert a new key-value pair:\n",
+    "            - key: unique value of bucktizing column\n",
+    "            - value: initialize a new list, append current row as an item into the list, thereby creating a list of list data structure\n",
+    "        - if yes, append current row to the list of list data structure (value of the key).\n",
+    "\n",
+    "After this process, each row ends up in a bin, based on the value of the bucketize column.\n",
+    "Number of bins = number of unique values in the bucketize column\n",
+    "\n",
+    "Why bucketize data?\n",
+    "- A way to organize our data, without losing information in the process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's take another look at our 'cs220_survey_data.csv'\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's bucketize the data\n",
+    "buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+    "for row in cs220_data:\n",
+    "    lecture = row[cs220_header.index('Lecture')]\n",
+    "    if not lecture in buckets:\n",
+    "        buckets[lecture] = []\n",
+    "    buckets[lecture].append(row)\n",
+    "# buckets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's convert the above code into a function called 'bucketize'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def col_avg(data, header, col_name, min_bound, max_bound):\n",
+    "    \"\"\"\n",
+    "    data: list of list data structure representing rows\n",
+    "    col_name: name of the column for which we want to compute average\n",
+    "    min_bound, max_bound: bounds for the data (data cleaning)\n",
+    "    Returns average of that column.\n",
+    "    \n",
+    "    Assumes the data in col_name is numerical (float or int)\n",
+    "    \"\"\"\n",
+    "    buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+    "    for row in data:\n",
+    "        lecture = row[header.index(col_name)]\n",
+    "        if lecture < min_bound or lecture > max_bound:\n",
+    "            continue\n",
+    "        if not lecture in buckets:\n",
+    "            buckets[lecture] = []\n",
+    "        buckets[lecture].append(row)\n",
+    "        \n",
+    "    \n",
+    "    return buckets\n",
+    "\n",
+    "min_age = 0\n",
+    "max_age = 118\n",
+    "col_avg(cs220_data, cs220_header, \"Age\", min_age, max_age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Average per bucket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'LEC001': 20.049180327868854,\n",
+       " 'LEC006': 18.63157894736842,\n",
+       " 'LEC004': 19.98941798941799,\n",
+       " 'LEC005': 19.42222222222222,\n",
+       " 'LEC002': 255.91573033707866,\n",
+       " 'LEC003': 19.137096774193548}"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def avg_per_bucket(buckets, avg_col_name, header):\n",
+    "    \"\"\"\n",
+    "    Computes and returns column average per bucket\n",
+    "    \"\"\"\n",
+    "    bucket_avg = {}\n",
+    "    for bucket in buckets:\n",
+    "        # TODO find average age\n",
+    "        num_students = 0\n",
+    "        total_age = 0\n",
+    "        for row in buckets[bucket]:\n",
+    "            age = row[header.index(avg_col_name)]\n",
+    "            if age != '':\n",
+    "                total_age += int(age)\n",
+    "                num_students += 1\n",
+    "        bucket_avg[bucket] = total_age/num_students\n",
+    "    return bucket_avg\n",
+    "avg_per_bucket(buckets, \"Age\", cs220_header) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What is the average student age per lecture?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What is the average student age in each major?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nesting part 2: Tables\n",
+    "#### Use a list of dictionaries to represent a table of data.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:table_rep.png\" width=\"600\"/>\n",
+    "</div>\n",
+    "\n",
+    "Steps (build a list of dictionaries)\n",
+    "- Start with an empty list\n",
+    "- Each row of data is one dictionary\n",
+    "    - keys are the column names\n",
+    "    - values are the data in each cell\n",
+    "\n",
+    "Why put data in table form?\n",
+    "- It seems redundant, but is used often in Web apps for storing info.\n",
+    "- Its a little easier to access subsets of the data without worrying about the header index method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lecture',\n",
+       " 'Age',\n",
+       " 'Major',\n",
+       " 'Zip Code',\n",
+       " 'Latitude',\n",
+       " 'Longitude',\n",
+       " 'Pizza topping',\n",
+       " 'Pet preference',\n",
+       " 'Runner',\n",
+       " 'Sleep habit',\n",
+       " 'Procrastinator']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's put the student survey data into a list of dictionaries\n",
+    "cs220_header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transform(header, data):\n",
+    "    \"\"\"\n",
+    "    Transform data into a list of dictionaries\n",
+    "    \"\"\"\n",
+    "    list_of_dicts = []\n",
+    "    for row in data:\n",
+    "        row_dict = {}\n",
+    "        for col_name in header:\n",
+    "            row_dict[col_name] = row[ header.index(col_name) ]\n",
+    "        # alternate for loop option (both work):\n",
+    "#         for i in range(len(header)):\n",
+    "#             col_name = header[i]\n",
+    "#             row_dict[col_name] = row[i]\n",
+    "        list_of_dicts.append(row_dict) \n",
+    "    return list_of_dicts\n",
+    "    \n",
+    "transformed_data = transform(cs220_header, cs220_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'Lecture': 'LEC001',\n",
+       "  'Age': '22',\n",
+       "  'Major': 'Engineering: Biomedical',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Undecided',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Undecided',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC002',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Undecided',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Engineering: Other|Engineering: Computer',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Undecided',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '35.4',\n",
+       "  'Longitude': '119.11',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Mathematics/AMEP',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '44',\n",
+       "  'Longitude': '-93',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC002',\n",
+       "  'Age': '21',\n",
+       "  'Major': 'Engineering: Other',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '24.713552',\n",
+       "  'Longitude': '46.675297',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53705',\n",
+       "  'Latitude': '24.6806',\n",
+       "  'Longitude': '46.57936',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '24',\n",
+       "  'Major': 'Economics',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '43',\n",
+       "  'Longitude': '-89',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '36.102371',\n",
+       "  'Longitude': '-115.174553',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '22',\n",
+       "  'Major': 'Psychology',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '31.78',\n",
+       "  'Longitude': '119.95',\n",
+       "  'Pizza topping': 'mushroom',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53705',\n",
+       "  'Latitude': '37.8',\n",
+       "  'Longitude': '112.5',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '24',\n",
+       "  'Major': 'Science: Biology/Life',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '46.872131',\n",
+       "  'Longitude': '-113.994019',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '17',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '46.6242',\n",
+       "  'Longitude': '8.0414',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53726',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC002',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '57303',\n",
+       "  'Latitude': '41.878113',\n",
+       "  'Longitude': '-87.629799',\n",
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+       "  'Sleep habit': 'night owl',\n",
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+       "  'Procrastinator': 'Yes'},\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Longitude': '-87.68828',\n",
+       "  'Pizza topping': 'Other',\n",
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+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'No'},\n",
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+       "  'Longitude': '-106.038353',\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Procrastinator': 'Yes'},\n",
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+       "  'Longitude': '-87.632401',\n",
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+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
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+       "  'Runner': 'No',\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Runner': 'Yes',\n",
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+       " {'Lecture': 'LEC002',\n",
+       "  'Age': '20',\n",
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+       "  'Longitude': '-105.101189',\n",
+       "  'Pizza topping': 'pineapple',\n",
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+       "  'Major': 'Computer Science',\n",
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+       "  'Procrastinator': 'Yes'},\n",
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+       "  'Sleep habit': 'night owl',\n",
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+       "  'Longitude': '12.496365',\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Longitude': '30',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
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+       "  'Zip Code': '53706',\n",
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+       "  'Longitude': '-105.270546',\n",
+       "  'Pizza topping': 'green pepper',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
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+       "  'Longitude': '6.1058',\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Longitude': '-122.3',\n",
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+       "  'Runner': 'No',\n",
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+       "  'Longitude': '-87.906471',\n",
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+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
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+       "  'Longitude': '-83.378622',\n",
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+       "  'Longitude': '-87.629799',\n",
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+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
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+       "  'Longitude': '19.04',\n",
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+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
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+       "  'Procrastinator': 'Yes'},\n",
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+       "  'Longitude': '-71.21',\n",
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+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Runner': 'Yes',\n",
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+       "  'Sleep habit': 'night owl',\n",
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+       "  'Runner': 'No',\n",
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+       " {'Lecture': 'LEC002',\n",
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+       " {'Lecture': 'LEC004',\n",
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+       "  'Runner': 'No',\n",
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+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '36.462',\n",
+       "  'Longitude': '25.375465',\n",
+       "  'Pizza topping': 'basil/spinach',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '27',\n",
+       "  'Major': 'Environment & Resources',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '37.389091',\n",
+       "  'Longitude': '-5.984459',\n",
+       "  'Pizza topping': 'mushroom',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Business: Actuarial',\n",
+       "  'Zip Code': '53726',\n",
+       "  'Latitude': '32',\n",
+       "  'Longitude': '-117',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Science: Physics',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '46.2833',\n",
+       "  'Longitude': '-89.73',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '40.712776',\n",
+       "  'Longitude': '-74.005974',\n",
+       "  'Pizza topping': 'basil/spinach',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '40.712776',\n",
+       "  'Longitude': '-74.005974',\n",
+       "  'Pizza topping': 'Other',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '21',\n",
+       "  'Major': 'Business: Actuarial',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '39.19067',\n",
+       "  'Longitude': '-106.819199',\n",
+       "  'Pizza topping': 'macaroni/pasta',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '37.743042',\n",
+       "  'Longitude': '-122.415642',\n",
+       "  'Pizza topping': 'green pepper',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Economics',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '22.54',\n",
+       "  'Longitude': '114.05',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '59.93428',\n",
+       "  'Longitude': '30.335098',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '45.10994',\n",
+       "  'Longitude': '-87.209793',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC002',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Science: Biology/Life',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '51.507351',\n",
+       "  'Longitude': '-0.127758',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Environmental Studies',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '42.360081',\n",
+       "  'Longitude': '-71.058884',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '45',\n",
+       "  'Longitude': '-87',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '48.137',\n",
+       "  'Longitude': '11.575',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53711',\n",
+       "  'Latitude': '48.856613',\n",
+       "  'Longitude': '2.352222',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Science: Other',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '48.410648',\n",
+       "  'Longitude': '-114.338188',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Mathematics/AMEP',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '24.585445',\n",
+       "  'Longitude': '73.712479',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '36.974117',\n",
+       "  'Longitude': '-122.030792',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Computer Science',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '40.79254',\n",
+       "  'Longitude': '-98.70807',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53711',\n",
+       "  'Latitude': '30.572815',\n",
+       "  'Longitude': '104.066803',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC001',\n",
+       "  'Age': '21',\n",
+       "  'Major': 'Science: Chemistry',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '3.139003',\n",
+       "  'Longitude': '101.686852',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC006',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Data Science',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '40.46',\n",
+       "  'Longitude': '-90.67',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Science: Other|Environmental Science',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '43.073051',\n",
+       "  'Longitude': '-89.40123',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Engineering: Biomedical',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '30.328227',\n",
+       "  'Longitude': '-86.136975',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '21',\n",
+       "  'Major': 'Science: Biology/Life',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '41.385063',\n",
+       "  'Longitude': '2.173404',\n",
+       "  'Pizza topping': 'macaroni/pasta',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Mathematics/AMEP',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '42.99571',\n",
+       "  'Longitude': '-90',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC004',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '41.385063',\n",
+       "  'Longitude': '2.173404',\n",
+       "  'Pizza topping': 'sausage',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC001',\n",
+       "  'Age': '',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '40.7128',\n",
+       "  'Longitude': '74.006',\n",
+       "  'Pizza topping': 'pepperoni',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC005',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Psychology',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '9.167414',\n",
+       "  'Longitude': '77.876747',\n",
+       "  'Pizza topping': 'mushroom',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'No'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Industrial',\n",
+       "  'Zip Code': '53715',\n",
+       "  'Latitude': '24.713552',\n",
+       "  'Longitude': '46.675297',\n",
+       "  'Pizza topping': 'basil/spinach',\n",
+       "  'Pet preference': 'neither',\n",
+       "  'Runner': 'Yes',\n",
+       "  'Sleep habit': 'early bird',\n",
+       "  'Procrastinator': 'Maybe'},\n",
+       " {'Lecture': 'LEC001',\n",
+       "  'Age': '18',\n",
+       "  'Major': 'Undecided',\n",
+       "  'Zip Code': '53706',\n",
+       "  'Latitude': '44.8341',\n",
+       "  'Longitude': '87.377',\n",
+       "  'Pizza topping': 'basil/spinach',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'no preference',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC003',\n",
+       "  'Age': '19',\n",
+       "  'Major': 'Engineering: Mechanical',\n",
+       "  'Zip Code': '53705',\n",
+       "  'Latitude': '46.589146',\n",
+       "  'Longitude': '-112.039108',\n",
+       "  'Pizza topping': 'none (just cheese)',\n",
+       "  'Pet preference': 'cat',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Yes'},\n",
+       " {'Lecture': 'LEC001',\n",
+       "  'Age': '20',\n",
+       "  'Major': 'Economics',\n",
+       "  'Zip Code': '53703',\n",
+       "  'Latitude': '39.631506',\n",
+       "  'Longitude': '118.143239',\n",
+       "  'Pizza topping': 'pineapple',\n",
+       "  'Pet preference': 'dog',\n",
+       "  'Runner': 'No',\n",
+       "  'Sleep habit': 'night owl',\n",
+       "  'Procrastinator': 'Maybe'}]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What `Lecture` is the first student part of?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LEC001\n",
+      "LEC001\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(transformed_data[0]['Lecture'])\n",
+    "print(transformed_data[0].get('Lecture'))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What is the `Major` of the last student?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Economics\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(transformed_data[-1]['Major'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Nesting part 3: Dictionary of Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://www.w3schools.com/python/python_dictionaries_nested.asp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'shenanigans': {'definition': 'silly or high-spirited behavior; mischief.',\n",
+       "  'usage': 'widespread financial shenanigans had ruined the fortunes of many',\n",
+       "  'fun_to_say': 7},\n",
+       " 'bamboozle': {'definition': 'fool or cheat (someone).',\n",
+       "  'usage': 'Tom Sawyer bamboozled the neighborhood boys into painting for him',\n",
+       "  'fun_to_say': 8},\n",
+       " 'gubbins': {'definition': '(objects) of little to no value.',\n",
+       "  'usage': 'I cleared all the gubbins off my desk before I started working',\n",
+       "  'fun_to_say': 10},\n",
+       " 'malarkey': {'definition': 'meaningless talk; nonsense.',\n",
+       "  'usage': \"don't give me that malarkey\",\n",
+       "  'fun_to_say': 5},\n",
+       " 'gnarly': {'definition': 'gnarled.',\n",
+       "  'usage': 'twisted trees and gnarly roots',\n",
+       "  'fun_to_say': 2},\n",
+       " 'apple': {'definition': 'a round, red fruit',\n",
+       "  'usage': 'I ate an apple as a snack',\n",
+       "  'fun_to_say': 6}}"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# dict of dicts example:\n",
+    "\n",
+    "nested_english_dict = {\n",
+    "    \"shenanigans\": {\n",
+    "        \"definition\": \"silly or high-spirited behavior; mischief.\",\n",
+    "        \"usage\": \"widespread financial shenanigans had ruined the fortunes of many\",\n",
+    "        \"fun_to_say\": 7 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"bamboozle\": {\n",
+    "        \"definition\": \"fool or cheat (someone).\",\n",
+    "        \"usage\": \"Tom Sawyer bamboozled the neighborhood boys into painting for him\",\n",
+    "        \"fun_to_say\": 8 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gubbins\": {\n",
+    "        \"definition\": \"(objects) of little to no value.\",\n",
+    "        \"usage\": \"I cleared all the gubbins off my desk before I started working\",\n",
+    "        \"fun_to_say\": 10 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"malarkey\": {\n",
+    "        \"definition\": \"meaningless talk; nonsense.\",\n",
+    "        \"usage\": \"don't give me that malarkey\",\n",
+    "        \"fun_to_say\": 5 # on a scale of 1-10\n",
+    "    },\n",
+    "    \"gnarly\": {\n",
+    "        \"definition\": \"gnarled.\",\n",
+    "        \"usage\": \"twisted trees and gnarly roots\",\n",
+    "        \"fun_to_say\": 2 # on a scale of 1-10\n",
+    "    }\n",
+    "}\n",
+    "\n",
+    "# TODO: pick a word and add an inner dict\n",
+    "nested_english_dict['apple'] = {\n",
+    "    'definition': 'a round, red fruit',\n",
+    "    'usage': 'I ate an apple as a snack',\n",
+    "    'fun_to_say': 6\n",
+    "}\n",
+    "nested_english_dict"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How can we use \"bamboozle\"?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Tom Sawyer bamboozled the neighborhood boys into painting for him'"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_english_dict['bamboozle']['usage']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a list of words with fun_to_say score greater than 7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bamboozle', 'gubbins']"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fun_words = []\n",
+    "for word in nested_english_dict:\n",
+    "    if nested_english_dict[word]['fun_to_say'] > 7:\n",
+    "        fun_words.append(word)\n",
+    "fun_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}