diff --git a/s24/AmFam_Ashwin/35_Plotting2/Lecture Code/Lec35_Plotting2_Solution.ipynb b/s24/AmFam_Ashwin/35_Plotting2/Lecture Code/Lec35_Plotting2_Solution.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8e61abcf00b930ab4ea5cda5aa75024d8bfc946a
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@@ -0,0 +1,2022 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "\n",
+ "import sqlite3\n",
+ "import os\n",
+ "\n",
+ "import matplotlib\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import requests\n",
+ "matplotlib.rcParams[\"font.size\"] = 12"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Titanic dataset: https://www.kaggle.com/datasets/yasserh/titanic-dataset\n",
+ "\n",
+ "A **copy** can be found at: `https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/raw/main/s24/AmFam_Ashwin/35_Plotting2/Lecture%20Code/titanic.csv`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 1: Requests and file writing\n",
+ "\n",
+ "Download this file and save it locally in the file `titanic.csv`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here\n",
+ "\n",
+ "# use requests to get this file\n",
+ "r = requests.get(\"https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/raw/main/s24/AmFam_Ashwin/35_Plotting2/Lecture%20Code/titanic.csv\")\n",
+ "\n",
+ "# check that the request was successful\n",
+ "r.raise_for_status()\n",
+ "\n",
+ "# open a file called \"titanic.csv\" for writing the data locally to avoid spamming the server\n",
+ "f = open(\"titanic.csv\", \"w\", encoding='utf-8')\n",
+ "\n",
+ "# write the text of response to the file object\n",
+ "f.write(r.text)\n",
+ "\n",
+ "# close the file object\n",
+ "f.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 2: Making a DataFrame\n",
+ "\n",
+ "Read the `\"titanic.csv\"` file into a Pandas DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "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>PassengerId</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Pclass</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>SibSp</th>\n",
+ " <th>Parch</th>\n",
+ " <th>Ticket</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Braund, Mr. Owen Harris</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " <td>7.2500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+ " <td>female</td>\n",
+ " <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17599</td>\n",
+ " <td>71.2833</td>\n",
+ " <td>C85</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>Heikkinen, Miss. Laina</td>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>STON/O2. 3101282</td>\n",
+ " <td>7.9250</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Allen, Mr. William Henry</td>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>373450</td>\n",
+ " <td>8.0500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PassengerId Survived Pclass \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age SibSp \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 1 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
+ "4 Allen, Mr. William Henry male 35.0 0 \n",
+ "\n",
+ " Parch Ticket Fare Cabin Embarked \n",
+ "0 0 A/5 21171 7.2500 NaN S \n",
+ "1 0 PC 17599 71.2833 C85 C \n",
+ "2 0 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 0 113803 53.1000 C123 S \n",
+ "4 0 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "titanic_df = pd.read_csv(\"titanic.csv\")\n",
+ "\n",
+ "# display the head of the data frame\n",
+ "titanic_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 3: Some of our column names are not very clear, let's change them.\n",
+ "These should be our headers: `\"ID\", \"Survived\", \"Passenger Class\", \"Name\", \"Sex\", \"Age\", \"No. of Siblings/Spouses aboard\", \"No. of Parents/Children aboard\", \"Ticket Number\", \"Fare\", \"Cabin\", \"Location Embarked\"`\n",
+ "\n",
+ "Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "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>ID</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Passenger Class</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>No. of Siblings/Spouses aboard</th>\n",
+ " <th>No. of Parents/Children aboard</th>\n",
+ " <th>Ticket Number</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Location Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Braund, Mr. Owen Harris</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " <td>7.2500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+ " <td>female</td>\n",
+ " <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17599</td>\n",
+ " <td>71.2833</td>\n",
+ " <td>C85</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>Heikkinen, Miss. Laina</td>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>STON/O2. 3101282</td>\n",
+ " <td>7.9250</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Allen, Mr. William Henry</td>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>373450</td>\n",
+ " <td>8.0500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID Survived Passenger Class \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
+ "4 Allen, Mr. William Henry male 35.0 \n",
+ "\n",
+ " No. of Siblings/Spouses aboard No. of Parents/Children aboard \\\n",
+ "0 1 0 \n",
+ "1 1 0 \n",
+ "2 0 0 \n",
+ "3 1 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " Ticket Number Fare Cabin Location Embarked \n",
+ "0 A/5 21171 7.2500 NaN S \n",
+ "1 PC 17599 71.2833 C85 C \n",
+ "2 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 113803 53.1000 C123 S \n",
+ "4 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "titanic_df = pd.read_csv(\"titanic.csv\",\n",
+ " names=[\"ID\", \"Survived\", \"Passenger Class\", \"Name\", \"Sex\", \"Age\", \"No. of Siblings/Spouses aboard\", \n",
+ " \"No. of Parents/Children aboard\", \"Ticket Number\", \"Fare\", \"Cabin\", \"Location Embarked\"],\n",
+ " header=0)\n",
+ "titanic_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 4: Connect to our database version of this data!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This following code will create a `titanic.db` file and write the contents of `titanic_df` into this Database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "891"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic_conn = sqlite3.connect(\"titanic.db\")\n",
+ "titanic_df.to_sql(\"titanic\", titanic_conn, if_exists=\"replace\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "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>type</th>\n",
+ " <th>name</th>\n",
+ " <th>tbl_name</th>\n",
+ " <th>rootpage</th>\n",
+ " <th>sql</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>table</td>\n",
+ " <td>titanic</td>\n",
+ " <td>titanic</td>\n",
+ " <td>2</td>\n",
+ " <td>CREATE TABLE \"titanic\" (\\n\"ID\" INTEGER,\\n \"Su...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " type name tbl_name rootpage \\\n",
+ "0 table titanic titanic 2 \n",
+ "\n",
+ " sql \n",
+ "0 CREATE TABLE \"titanic\" (\\n\"ID\" INTEGER,\\n \"Su... "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "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>ID</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Passenger Class</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>No. of Siblings/Spouses aboard</th>\n",
+ " <th>No. of Parents/Children aboard</th>\n",
+ " <th>Ticket Number</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Location Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Braund, Mr. Owen Harris</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " <td>7.2500</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+ " <td>female</td>\n",
+ " <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17599</td>\n",
+ " <td>71.2833</td>\n",
+ " <td>C85</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>Heikkinen, Miss. Laina</td>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>STON/O2. 3101282</td>\n",
+ " <td>7.9250</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Allen, Mr. William Henry</td>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>373450</td>\n",
+ " <td>8.0500</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID Survived Passenger Class \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
+ "4 Allen, Mr. William Henry male 35.0 \n",
+ "\n",
+ " No. of Siblings/Spouses aboard No. of Parents/Children aboard \\\n",
+ "0 1 0 \n",
+ "1 1 0 \n",
+ "2 0 0 \n",
+ "3 1 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " Ticket Number Fare Cabin Location Embarked \n",
+ "0 A/5 21171 7.2500 None S \n",
+ "1 PC 17599 71.2833 C85 C \n",
+ "2 STON/O2. 3101282 7.9250 None S \n",
+ "3 113803 53.1000 C123 S \n",
+ "4 373450 8.0500 None S "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.read_sql(\"SELECT * FROM titanic LIMIT 5\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 5: Using SQL, get the 10 oldest male Titanic passengers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "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>ID</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Passenger Class</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>No. of Siblings/Spouses aboard</th>\n",
+ " <th>No. of Parents/Children aboard</th>\n",
+ " <th>Ticket Number</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Location Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>631</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
+ " <td>male</td>\n",
+ " <td>80.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>27042</td>\n",
+ " <td>30.0000</td>\n",
+ " <td>A23</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>852</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Svensson, Mr. Johan</td>\n",
+ " <td>male</td>\n",
+ " <td>74.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>347060</td>\n",
+ " <td>7.7750</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>97</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>Goldschmidt, Mr. George B</td>\n",
+ " <td>male</td>\n",
+ " <td>71.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17754</td>\n",
+ " <td>34.6542</td>\n",
+ " <td>A5</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>494</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>Artagaveytia, Mr. Ramon</td>\n",
+ " <td>male</td>\n",
+ " <td>71.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17609</td>\n",
+ " <td>49.5042</td>\n",
+ " <td>None</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>117</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Connors, Mr. Patrick</td>\n",
+ " <td>male</td>\n",
+ " <td>70.5</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>370369</td>\n",
+ " <td>7.7500</td>\n",
+ " <td>None</td>\n",
+ " <td>Q</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>673</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>Mitchell, Mr. Henry Michael</td>\n",
+ " <td>male</td>\n",
+ " <td>70.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>C.A. 24580</td>\n",
+ " <td>10.5000</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>746</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>Crosby, Capt. Edward Gifford</td>\n",
+ " <td>male</td>\n",
+ " <td>70.0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>WE/P 5735</td>\n",
+ " <td>71.0000</td>\n",
+ " <td>B22</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>34</td>\n",
+ " <td>0</td>\n",
+ " <td>2</td>\n",
+ " <td>Wheadon, Mr. Edward H</td>\n",
+ " <td>male</td>\n",
+ " <td>66.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>C.A. 24579</td>\n",
+ " <td>10.5000</td>\n",
+ " <td>None</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>55</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>Ostby, Mr. Engelhart Cornelius</td>\n",
+ " <td>male</td>\n",
+ " <td>65.0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>113509</td>\n",
+ " <td>61.9792</td>\n",
+ " <td>B30</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>281</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Duane, Mr. Frank</td>\n",
+ " <td>male</td>\n",
+ " <td>65.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>336439</td>\n",
+ " <td>7.7500</td>\n",
+ " <td>None</td>\n",
+ " <td>Q</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID Survived Passenger Class Name Sex \\\n",
+ "0 631 1 1 Barkworth, Mr. Algernon Henry Wilson male \n",
+ "1 852 0 3 Svensson, Mr. Johan male \n",
+ "2 97 0 1 Goldschmidt, Mr. George B male \n",
+ "3 494 0 1 Artagaveytia, Mr. Ramon male \n",
+ "4 117 0 3 Connors, Mr. Patrick male \n",
+ "5 673 0 2 Mitchell, Mr. Henry Michael male \n",
+ "6 746 0 1 Crosby, Capt. Edward Gifford male \n",
+ "7 34 0 2 Wheadon, Mr. Edward H male \n",
+ "8 55 0 1 Ostby, Mr. Engelhart Cornelius male \n",
+ "9 281 0 3 Duane, Mr. Frank male \n",
+ "\n",
+ " Age No. of Siblings/Spouses aboard No. of Parents/Children aboard \\\n",
+ "0 80.0 0 0 \n",
+ "1 74.0 0 0 \n",
+ "2 71.0 0 0 \n",
+ "3 71.0 0 0 \n",
+ "4 70.5 0 0 \n",
+ "5 70.0 0 0 \n",
+ "6 70.0 1 1 \n",
+ "7 66.0 0 0 \n",
+ "8 65.0 0 1 \n",
+ "9 65.0 0 0 \n",
+ "\n",
+ " Ticket Number Fare Cabin Location Embarked \n",
+ "0 27042 30.0000 A23 S \n",
+ "1 347060 7.7750 None S \n",
+ "2 PC 17754 34.6542 A5 C \n",
+ "3 PC 17609 49.5042 None C \n",
+ "4 370369 7.7500 None Q \n",
+ "5 C.A. 24580 10.5000 None S \n",
+ "6 WE/P 5735 71.0000 B22 S \n",
+ "7 C.A. 24579 10.5000 None S \n",
+ "8 113509 61.9792 B30 C \n",
+ "9 336439 7.7500 None Q "
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "pd.read_sql(\"\"\"\n",
+ " SELECT *\n",
+ " FROM titanic\n",
+ " WHERE sex = 'male'\n",
+ " ORDER BY age DESC\n",
+ " LIMIT 10\n",
+ "\"\"\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 6: Using SQL, get the average Fare for each Passenger Class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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>Passenger Class</th>\n",
+ " <th>Average Fare</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>84.154687</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>20.662183</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>13.675550</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Passenger Class Average Fare\n",
+ "0 1 84.154687\n",
+ "1 2 20.662183\n",
+ "2 3 13.675550"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "pd.read_sql(\"\"\"\n",
+ " SELECT `Passenger Class`, AVG(Fare) AS `Average Fare`\n",
+ " FROM titanic\n",
+ " GROUP BY `Passenger CLass`\n",
+ "\"\"\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 35: Scatter Plots\n",
+ "**Learning Objectives**\n",
+ "- Set the marker, color, and size of scatter plot data\n",
+ "- Calculate correlation between DataFrame columns\n",
+ "- Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set the marker, color, and size of scatter plot data\n",
+ "\n",
+ "To start, let's look at some made-up data about Trees.\n",
+ "The city of Madison maintains a database of all the trees they care for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "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>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>1.5</td>\n",
+ " <td>0.8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1</td>\n",
+ " <td>1.9</td>\n",
+ " <td>1.2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>1</td>\n",
+ " <td>1.8</td>\n",
+ " <td>1.4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2</td>\n",
+ " <td>1.8</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>2</td>\n",
+ " <td>2.5</td>\n",
+ " <td>1.5</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "0 1 1.5 0.8\n",
+ "1 1 1.9 1.2\n",
+ "2 1 1.8 1.4\n",
+ "3 2 1.8 0.9\n",
+ "4 2 2.5 1.5"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "trees = [\n",
+ " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
+ " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
+ " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
+ " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
+ " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
+ " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
+ " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
+ " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
+ " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
+ " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
+ " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
+ " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
+ " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
+ " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
+ "]\n",
+ "trees_df = DataFrame(trees)\n",
+ "trees_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Scatter Plots\n",
+ "We can make a scatter plot of a DataFrame using the following function...\n",
+ "\n",
+ "`df_name.plot.scatter(x=\"x_col_name\", y=\"y_col_name\", color=\"peachpuff\")`\n",
+ "\n",
+ "## Example 1: Plot the trees data comparing a tree's age to its height\n",
+ "<pre>\n",
+ " - What is `df_name`?\n",
+ " - What is `x_col_name`?\n",
+ " - What is `y_col_name`?\n",
+ "</pre>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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hCRMmuF0UAABAd3I78Ozfv192u73NdVeuXNGBAwfcLgoAAKA7eeSS1pdffqk+ffp44q0BAAA6LaQzG+/Zs0d79uxpeb1y5UoNGDDAaZv6+nrt379ft912W/dUCAAA0EWdCjyfffaZduzYIUmyWCx6//33FRTkfJIoNDRUqampWr9+vUvv+cknn2jx4sUqLy/XpUuX1Lt3byUnJys/P195eXkd9t2yZYt+9rOftbnuyy+/1KBBg1yqAQAAmFunAs+iRYu0aNEiSVJQUJDKyso0duzYLhVQU1Oj+Ph4TZs2TYMHD1ZdXZ22bdumRx55RGfPntWSJUtu+B6bN2/WyJEjndqio6O7VBcAADAPi2EYhq+LaEtGRoYuXLigL774ot1tms/wHD16VGPGjOnS59ntdkVERKi2tlbh4eFdei8AAOAdrn5/d+oMT3suXbqk+vr6Vu1Dhgxx+z1jYmJUVVXVlbIAAAAkdSHw2O12PfXUUyosLNS3337b5jYOh8Pl92tqalJTU5O+/vpr7dixQ8XFxfrNb37jUt9Jkybp0qVLioiI0F133aUXXnhBKSkpHfZpaGhQQ0OD0/4AAABzcjvwPPnkk3r77bc1e/ZspaWlKTQ0tEuFzJkzR6+//rokqVevXnrllVf0+OOPd9hn0KBBWrx4sTIyMhQeHq7y8nKtWbNGGRkZOnjwoNLT09vtu3r1aq1YsaJLNQMAgMDg9j08gwYN0qJFizR//vxuKeSLL75QVVWVqqqqtHfvXr3xxhv65S9/qaeffrpT73P27FmlpqZqwoQJTo/Q/1BbZ3ji4+O5hwcAgADi8Xt4vv32W6WmprrbvZUhQ4a03PNzzz33SLr2VNiMGTNazfXTkWHDhmn8+PE6fPhwh9uFhoZ2+awUAAAIDG7PtHzPPffoT3/6U3fW4mTs2LFqbGzU6dOnO93XMIxW8wMBAICeq1NneKqrq1v+ecmSJXrwwQfVr18/TZ48uc15b6KiotwurKysTEFBQRo+fHin+p05c0YHDx5Udna2258NAADMpVP38AQFBclisbS8NgzD6fUPufKU1mOPPabw8HCNHTtWAwcO1OXLl7Vjxw5t375dzzzzjF5++WVJ0uzZs7V161ZZrVYNHTpUkpSdna0777xTaWlpLTctv/zyy7py5YoOHTp0wye1rsc8PAAABB6P3MPz/PPPdxhw3JGZmanNmzdr69atqqmpUVhYmNLT0/XWW285/bSEw+GQw+HQ9fksNTVV27dv19q1a1VfX6/Y2FhNmDBBS5cu1YgRI7q1TgAAELj8dqZlb+MMDwAAgcfV72/u7AUAAKbn9mPpL7zwQrvrgoKC1L9/f40ZM0YZGRnufgQAAEC3cPuSVvMNzG11b263WCzKysrSO++8o7CwsC4X60lc0gIAIPB4/JKW1WpVYmKiVq9erbNnz6q+vl5nzpzRSy+9pISEBB05ckRvvfWWjh07pqVLl7r7MQAAAF3m9hmeyZMna9y4cVqyZEmrdatWrdLBgwe1b98+rVy5Ur/73e909uzZrtbqUZzhAQAg8Hj8DM/+/fuVmZnZ5rrMzEz9+c9/bvnnL7/80t2PAQAA6DK3A0+vXr308ccft7nu2LFj6tWrlySpqalJffv2dfdjAAAAusztp7Tuv/9+LVu2TBEREXrooYfUv39/1dTUaPv27XrhhRf0z//8z5Kk8vJyJSYmdlvBAAAAneX2PTy1tbWaNGmSDh48KIvFopCQEDU2NsowDN1xxx169913FRERod///vfq16+f7r333u6uvVtxDw8AAIHH1e/vLs20bBiG9u3bpw8++EA2m03R0dHKyspSbm5ut/8EhacReAAACDxeCTxmQuABACDw8NMSAAAA3+vUTcvDhw/X7t27lZ6erh/96EcdXrayWCyyWq1dLhAAAKCrOhV4srKyWk4XZWVlBdx9OgAAoGfiHp7vcQ8PAACBh3t4AAAAvtelwHPp0iUtWrRImZmZSkpK0okTJyRJr7/+eruzMAMA/FeFrUL7Kvep0lbp61KAbuX2TMtnzpzRHXfcodraWqWnp+v06dNqaGiQJP3lL3/R4cOHtXnz5m4rFADgOdX11Zq+a7qKrcUtbTkJOSqcWqjI3pE+rAzoHm6f4Vm4cKH69++vyspKffDBB7r+VqDx48fr4MGD3VIgAMDzpu+arpLTJU5tJadLNG3XNB9VBHQvtwNPaWmpli1bpri4uFZPa/3N3/yNLly40OXiAACeV2GrULG1WA7D4dTuMBwqthZzeQum4Hbg+fbbbxUVFdXmurq6OgUFcT80AAQCa3XHc6adqj7lpUoAz3E7lSQnJ6ukpKTNdR988IFSUlLcLgoA4D0JUQkdrk+MSvRSJYDnuB14Hn30Ua1fv17r16/X119/LUn67rvvtHPnTm3cuFGPP/54txUJAPCcEdEjlJOQo2BLsFN7sCVYOQk5SopO8lFlQPfp0sSDjz32mDZt2qSgoCA1NTUpKChIhmHo0Ucf1W9/+9vurNPjmHgQQE/2df3XmrZrGk9pIeB47dfSDx8+rHfffVdVVVWKiYnRpEmT9Pd///ddeUufIPAAgFRpq9Sp6lNKjErkzA4CgtcCj3RtAsL6+vpW7UOGDOnqW3sNgQeBpsJWIWu1lS8mAD2aq9/fbk88eOXKFT355JMqLCzUt99+2+Y2DoejzXYA7mOCOADoPLcDz4IFC/T2229r9uzZSktLU2hoaHfWBaAdHU0QV5RX5KOqAMC/uR143nvvPa1Zs0bz58/vznoAdKB5grgfun6COC5vAUBrXZp4MDU1tTtrAXADTBAHAO5xO/Dcc889+tOf/tSdtQC4ASaIAwD3dOqSVnV1dcs/L1myRA8++KD69eunyZMnKzo6utX27f30BAD3NE8QV3K6xOl3j4Itwcoens3lLABoR6ceSw8KCnL6oVDDMFr9cOj1AukpLR5LR6BggjgA+D8eeSz9+eef7zDguOOTTz7R4sWLVV5erkuXLql3795KTk5Wfn6+8vLybti/qqpKCxcu1LvvvqtvvvlG6enpWrVqlSZOnNitdQL+IrJ3pIryipggDgA6oVOBZ/ny5d1eQE1NjeLj4zVt2jQNHjxYdXV12rZtmx555BGdPXtWS5YsabdvQ0ODJk6cqJqaGq1fv16xsbHasGGDcnNzVVJSoqysrG6vF/AXhro8ZyjQChNawqy6ZaZlT8jIyNCFCxf0xRdftLvNxo0blZ+fr0OHDikzM1OS1NjYqPT0dIWFhenIkSMufx6XtBAomHgQnsC4QqBy9fvb7ae0PC0mJkYhIR2fgNq9e7eSk5Nbwo4khYSEKC8vTx9++KHOnz/v6TIBr+to4kHAXYwrmJ3fBJ6mpiY1Njbq0qVL2rhxo4qLi/Xss8922Of48eNKS0tr1d7cduLEiXb7NjQ0yG63Oy2Av2ueePD6J7Qk54kHgc5iXKEn8JvAM2fOHN10002KjY3Vk08+qVdeeUWPP/54h31sNlubj743t9lstnb7rl69WhERES1LfHx813YA8AImHoQnMK7QE/hN4Hnuued09OhRvffee5o1a5bmzp2rtWvX3rBfR0+NdbRu0aJFqq2tbVnOnTvnVt2ANzHxIDyBcYWewO3f0upuQ4YM0ZAhQyRdm8VZuhZKZsyYoQEDBrTZJzo6us2zOM0TJHY08WFoaCg/eIqAw8SD8ATGFXoCvznD80Njx45VY2OjTp8+3e42qampKi8vb9Xe3JaSkuKx+gBfKZxaqOzh2U5t2cOzVTi10EcVwQwYVzA7vznD80NlZWUKCgrS8OHD291mypQpmjNnjo4cOaJx48ZJuvZYekFBgcaNG6e4uDhvlQt4DRMPwhMYVzA7nweexx57TOHh4Ro7dqwGDhyoy5cva8eOHdq+fbueeeaZlstZs2fP1tatW2W1WjV06FBJ0qxZs7RhwwY99NBDWrNmjWJjY7Vx40adPHlSJSUlHX0sEPCYeBCekBSdRNCBKfk88GRmZmrz5s3aunWrampqFBYWpvT0dL311ltOPy3hcDjkcDh0/TyJoaGhKi0t1cKFCzVv3jx98803uvXWW7Vv3z5mWYZpMUEcAHSe38607G3MtIxAkVuQ2+7NpUV5RT6sDAC8L+BnWgbQGhPEAYB7CDxAAGGCOABwD4EHCCBMEAcA7iHwAAGkeYK4YEuwU3uwJVg5CTk8XQMA7SDwAAGGCeIAoPN8/lg6gM5hgjgA6DwCDxCgmCAOAFzHJS0AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6Pg8877//vmbNmqWRI0eqb9++Gjx4sO677z4dO3bshn23bNkii8XS5nLx4kUvVA8AAAJBiK8LeO2112Sz2TR//nyNGjVKly5d0rp165SRkaHi4mJNmDDhhu+xefNmjRw50qktOjraUyUDfqHCViFrtVWJUYlKik7ydTkA0C5/+Hvl88CzYcMGxcbGOrXl5uYqMTFRL730kkuBJyUlRWPGjPFUiYBfqa6v1vRd01VsLW5py0nIUeHUQkX2jvRhZQDgzJ/+Xvn8ktYPw44khYWFadSoUTp37pwPKgL82/Rd01VyusSpreR0iabtmuajigCgbf7098rngacttbW1+uijjzR69GiXtp80aZKCg4MVFRWlBx54QMePH79hn4aGBtntdqcF8HcVtgoVW4vlMBxO7Q7DoWJrsSptlT6qDACc+dvfK78MPPn5+aqrq9PixYs73G7QoEFavHixNm3apLKyMq1cuVJHjx5VRkaGPv300w77rl69WhERES1LfHx8d+4C4BHWamuH609Vn/JSJQDQMX/7e2UxDMPw6ifewNKlS7Vq1Sq9+uqrmjt3bqf7nz17VqmpqZowYYL27NnT7nYNDQ1qaGhoeW232xUfH6/a2lqFh4e7VTvgaRW2CiX/Jrn99XMruIEZgF/w1t8ru92uiIiIG35/+9UZnhUrVmjVqlV68cUX3Qo7kjRs2DCNHz9ehw8f7nC70NBQhYeHOy2AvxsRPUI5CTkKtgQ7tQdbgpWTkEPYAeA3/O3vld8EnhUrVmj58uVavny5nnvuuS69l2EYCgrym10DulXh1EJlD892assenq3CqYU+qggA2uZPf6/84pLWypUr9fzzz2vJkiVauXJll97rzJkzSktLU3Z2tnbv3u1yP1dPiQH+otJWqVPVp5iHB4Df8+TfK1e/v30eeNatW6enn35aubm5WrZsWav1GRkZkqTZs2dr69atslqtGjp0qCQpOztbd955p9LS0hQeHq7y8nK9/PLLunLlig4dOqSUlBSX6yDwAIB/TBAHdIar398+n3hw7969kqSioiIVFRW1Wt+cxxwOhxwOh67PZ6mpqdq+fbvWrl2r+vp6xcbGasKECVq6dKlGjBjhnR0AABPwpwniAE/w+Rkef8EZHgA9WW5BrkpOlzjNmRJsCVb28GwV5bX+n1HAXwTkU1oAAO/ztwniAE8g8ABAD+dvE8QBnkDgAYAeLiEqocP1iVGJXqoE8BwCDwD0cP42QRzgCQQeAIBfTRAHeILPH0sHAPheZO9IFeUVMaElTIvAAwBokRSdRNCBKXFJCwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmB6BBwAAmJ7PA8/777+vWbNmaeTIkerbt68GDx6s++67T8eOHXOpf1VVlWbOnKmYmBj16dNHmZmZKi0t9XDVAAAgkPg88Lz22ms6e/as5s+frz/84Q9av369qqqqlJGRoffff7/Dvg0NDZo4caJKS0u1fv167dmzRwMHDlRubq4OHDjgpT0AAAD+zmIYhuHLAqqqqhQbG+vUdvXqVSUmJiolJUUlJSXt9t24caPy8/N16NAhZWZmSpIaGxuVnp6usLAwHTlyxOU67Ha7IiIiVFtbq/DwcPd2BgAAeJWr398+P8Pzw7AjSWFhYRo1apTOnTvXYd/du3crOTm5JexIUkhIiPLy8vThhx/q/Pnz3V4vAAAIPD4PPG2pra3VRx99pNGjR3e43fHjx5WWltaqvbntxIkTHqkPAAAElhBfF9CW/Px81dXVafHixR1uZ7PZFBUV1aq9uc1ms7Xbt6GhQQ0NDS2v7Xa7m9UCAAB/53dneJYuXapt27bpX//1X3X77bffcHuLxeLWutWrVysiIqJliY+Pd6teAADg//wq8KxYsUKrVq3Siy++qLlz595w++jo6DbP4lRXV0tSm2d/mi1atEi1tbUty43uFwIAAIHLby5prVixQsuXL9fy5cv13HPPudQnNTVV5eXlrdqb21JSUtrtGxoaqtDQUPeKBQAAAcUvzvCsXLlSy5cv15IlS7Rs2TKX+02ZMkWff/650+PnjY2NKigo0Lhx4xQXF+eJcgEAQIDxeeBZt26dnn/+eeXm5uree+/V4cOHnZZms2fPVkhIiP7617+2tM2aNUujR4/WQw89pLffflslJSX6x3/8R508eVK//OUvfbE7AADAD/n8ktbevXslSUVFRSoqKmq1vnleRIfDIYfDoevnSQwNDVVpaakWLlyoefPm6ZtvvtGtt96qffv2KSsryzs7cAMVtgpZq61KjEpUUnSSr8sBAKBH8vlMy/6iu2darq6v1vRd01VsLW5py0nIUeHUQkX2juzy+wMAgACaadmspu+arpLTzj+LUXK6RNN2TfNRRQAA9FwEHg+osFWo2Fosh+FwancYDhVbi1Vpq/RRZQAA9EwEHg+wVls7XH+q+pSXKgEAABKBxyMSohI6XJ8YleilSgAAgETg8YgR0SOUk5CjYEuwU3uwJVg5CTk8rQUAgJcReDykcGqhsodnO7VlD89W4dRCH1UEAEDP5fN5eMwqsnekivKKVGmr1KnqU8zDAwCADxF4PMwQ0xzBM5jUEgBcR+DxECYehKcwtgCg87iHx0OYeBCewtgCgM4j8HgAEw/CUxhbAOAeAo8HMPEgPIWxBQDuIfB4ABMPwlMYWwDgHgKPBzDxIDyFsQUA7iHweAgTD8JTGFsA0HkWwzCYKEaS3W5XRESEamtrFR4e3m3vy8SD8BTGFgC4/v1N4PmepwIPAADwHFe/v7mkBQAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATI/AAwAATM/ngefKlStauHCh7r77bg0YMEAWi0XLly93qe+WLVtksVjaXC5evOjZwgEAQMAI8XUBNptNb7zxhtLT03X//fdr06ZNnX6PzZs3a+TIkU5t0dHR3VVil1TYKmSttioxKlFJ0Um+LgcAgB7J54Fn6NCh+vrrr2WxWHT58mW3Ak9KSorGjBnjgercV11frem7pqvYWtzSlpOQo8KphYrsHenDygAA6Hl8fkmr+RKU2UzfNV0lp0uc2kpOl2jarmk+qggAgJ7L54GnO0yaNEnBwcGKiorSAw88oOPHj9+wT0NDg+x2u9PSXSpsFSq2FsthOJzaHYZDxdZiVdoqu+2zAADAjQV04Bk0aJAWL16sTZs2qaysTCtXrtTRo0eVkZGhTz/9tMO+q1evVkRERMsSHx/fbXVZq60drj9VfarbPgsAANyYxTAMw9dFNLt8+bIGDBigZcuWufyk1g+dPXtWqampmjBhgvbs2dPudg0NDWpoaGh5bbfbFR8fr9raWoWHh7v12c0qbBVK/k1y++vnVnADMwAA3cButysiIuKG398BfYanLcOGDdP48eN1+PDhDrcLDQ1VeHi409JdRkSPUE5CjoItwU7twZZg5STkEHYAAPAy0wUeSTIMQ0FBvt21wqmFyh6e7dSWPTxbhVMLfVQRAAA9l88fS+9uZ86c0cGDB5WdnX3jjT0osnekivKKVGmr1KnqU8zDAwCAD/lF4Nm3b5/q6up05coVSdJnn32mnTt3SpLuuece9enTR7Nnz9bWrVtltVo1dOhQSVJ2drbuvPNOpaWlKTw8XOXl5Xr55ZdlsVi0cuVKn+3P9ZKikwg6AAD4mF8EnieeeEJ//etfW17v2LFDO3bskHTtjM2wYcPkcDjkcDh0/T3Wqamp2r59u9auXav6+nrFxsZqwoQJWrp0qUaMGOH1/QAAAP7Jr57S8iVX7/IGAAD+o8c+pQUAAPBDBB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6BB4AAGB6fjHTsj9onn/Rbrf7uBIAAOCq5u/tG82jTOD5XvPveMXHx/u4EgAA0FlXrlxRREREu+v5aYnvNTU16cKFC+rXr58sFku3va/dbld8fLzOnTvHT1bcAMeqczheruNYuY5j5TqOles8eawMw9CVK1cUFxenoKD279ThDM/3goKCdMstt3js/cPDw/kPwkUcq87heLmOY+U6jpXrOFau89Sx6ujMTjNuWgYAAKZH4AEAAKZH4PGw0NBQLVu2TKGhob4uxe9xrDqH4+U6jpXrOFau41i5zh+OFTctAwAA0+MMDwAAMD0CDwAAMD0CDwAAMD0Cj5uuXLmihQsX6u6779aAAQNksVi0fPlyl/tXVVVp5syZiomJUZ8+fZSZmanS0lLPFexDXTlWW7ZskcViaXO5ePGiZwv3gffff1+zZs3SyJEj1bdvXw0ePFj33Xefjh075lL/njSuunKsetq4+uSTT3TvvfdqyJAh6t27t6KiopSZmamCggKX+vekcdWVY9XTxlVbNm3aJIvForCwMJe29+bYYuJBN9lsNr3xxhtKT0/X/fffr02bNrnct6GhQRMnTlRNTY3Wr1+v2NhYbdiwQbm5uSopKVFWVpYHK/e+rhyrZps3b9bIkSOd2qKjo7urRL/x2muvyWazaf78+Ro1apQuXbqkdevWKSMjQ8XFxZowYUK7fXvauOrKsWrWU8ZVTU2N4uPjNW3aNA0ePFh1dXXatm2bHnnkEZ09e1ZLlixpt29PG1ddOVbNesq4+qHz58/r6aefVlxcnGpra2+4vdfHlgG3NDU1GU1NTYZhGMalS5cMScayZctc6rthwwZDknHo0KGWtv/3//6fMWrUKGPs2LGeKNenunKsNm/ebEgyjh496sEK/cdXX33Vqu3KlSvGwIEDjYkTJ3bYt6eNq64cq542rtozbtw4Iz4+vsNtetq4ao8rx6qnj6tJkyYZkydPNmbMmGH07dv3htt7e2xxSctNzacp3bF7924lJycrMzOzpS0kJER5eXn68MMPdf78+e4q0y905Vj1NLGxsa3awsLCNGrUKJ07d67Dvj1tXHXlWOGamJgYhYR0fKK/p42r9rhyrHqygoICHThwQBs3bnS5j7fHFoHHB44fP660tLRW7c1tJ06c8HZJfm/SpEkKDg5WVFSUHnjgAR0/ftzXJXlNbW2tPvroI40ePbrD7RhXrh+rZj1tXDU1NamxsVGXLl3Sxo0bVVxcrGeffbbDPj11XLlzrJr1tHFVVVWlBQsWaM2aNZ36TUpvjy3iqg/YbDZFRUW1am9us9ls3i7Jbw0aNEiLFy9WRkaGwsPDVV5erjVr1igjI0MHDx5Uenq6r0v0uPz8fNXV1Wnx4sUdbse4cv1Y9dRxNWfOHL3++uuSpF69eumVV17R448/3mGfnjqu3DlWPXlcJScn64knnuhUP2+PLQKPj3R0iYfLP/8nNzdXubm5La/vvPNO3XvvvUpNTdXzzz+vPXv2+LA6z1u6dKm2bdumV199VbfffvsNt+/J46ozx6qnjqvnnntOP//5z1VVVaW9e/dq7ty5qqur09NPP91hv544rtw5Vj1xXO3atUt79+7Vxx9/7NZY8ObYIvD4QHR0dJvJtbq6WpLaTLz4P8OGDdP48eN1+PBhX5fiUStWrNCqVav04osvau7cuTfcviePq84eq7b0hHE1ZMgQDRkyRJJ0zz33SJIWLVqkGTNmaMCAAW326anjyp1j1RYzj6urV68qPz9f8+bNU1xcnGpqaiRJ3333naRrT7zddNNN6tu3b5v9vT22uIfHB1JTU1VeXt6qvbktJSXF2yUFHMMwFBRk3uG7YsUKLV++XMuXL9dzzz3nUp+eOq7cOVbtMfu4+qGxY8eqsbFRp0+fbnebnjqufsiVY9Ues46ry5cv66uvvtK6desUGRnZshQWFqqurk6RkZF6+OGH2+3v7bFlvn8DAWDKlCn6/PPPdeTIkZa2xsZGFRQUaNy4cYqLi/Nhdf7vzJkzOnjwoDIyMnxdikesXLlSy5cv15IlS7Rs2TKX+/XEceXusWqL2cdVW8rKyhQUFKThw4e3u01PHFdtceVYtcXM42rQoEEqKytrteTk5Ojmm29WWVmZVq1a1W5/r4+tbn/QvQf5wx/+YOzYscN48803DUnGQw89ZOzYscPYsWOHUVdXZxiGYcyaNcsIDg42zp4929Lv22+/NUaPHm3Ex8cb27ZtM/74xz8aU6ZMMUJCQoz9+/f7anc8yt1jNXHiRGPFihXG7t27jdLSUuPf/u3fjLi4OKNfv35GeXm5r3bHY9auXWtIMnJzc43//u//brU0Y1x17Vj1tHH16KOPGv/yL/9ibN++3di/f7+xc+dO45/+6Z8MScYzzzzTsh3jqmvHqqeNq/a0NQ+PP4wtAk8XDB061JDU5nLmzBnDMK79i7/+dbOLFy8aP/3pT42oqCjj5ptvNjIyMow//vGP3t8JL3H3WC1YsMAYNWqU0a9fPyMkJMSIi4sz8vLyjJMnT/pmRzwsKyur3eN0/f+fMK66dqx62rh68803jR//+MdGTEyMERISYvTv39/Iysoy3nrrLaftGFddO1Y9bVy1p63A4w9jy2IYhtG954wAAAD8C/fwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAAA0yPwAAgYp06d0s9+9jMlJSWpT58+Gjx4sCZPnqzy8vJW2544cUJ33323+vTpowEDBig/P1/vvfeeLBaL9u/f77RtSUmJJk6cqPDwcPXp00d33HGHSktLvbRXALyBwAMgYFy4cEHR0dFas2aNioqKtGHDBoWEhGjcuHE6efJky3ZffvmlsrKydPLkSb322mv6j//4D125ckVz585t9Z4FBQW6++67FR4erq1bt+o///M/FRUVpZycHEIPYCL8WjqAgOVwONTU1KTRo0dr0qRJ+vWvfy1JWrhwodauXavjx49r1KhRLdvn5uaquLhYZWVluuuuu/TNN98oPj5ed9xxh955552W7ZqamvS3f/u3Cg0N1ZEjR7y+XwC6H2d4AASMxsZGvfTSSxo1apR69eqlkJAQ9erVS5WVlfqf//mflu0OHDiglJQUp7AjSdOmTXN6fejQIVVXV2vGjBlqbGxsWZqampSbm6ujR4+qrq7OK/sGwLNCfF0AALjqqaee0oYNG/Tss88qKytLkZGRCgoK0s9//nPV19e3bGez2fSjH/2oVf+BAwc6vf7qq68kSQ8++GC7n1ldXa2+fft20x4A8BUCD4CAUVBQoJ/+9Kd66aWXnNovX76s/v37t7yOjo5uCTPXu3jxotPrmJgYSdKrr76qjIyMNj/zhyEJQGAi8AAIGBaLRaGhoU5t7733ns6fP6/ExMSWtqysLK1du1afffaZ02Wt3//+905977jjDvXv31+fffZZmzc0AzAPAg+AgDFp0iRt2bJFI0eOVFpamo4dO6Zf/epXuuWWW5y2W7Bggd588039wz/8g1544QUNHDhQb7/9tj7//HNJUlDQtdsXw8LC9Oqrr2rGjBmqrq7Wgw8+qNjYWF26dEmffvqpLl26pNdee83r+wmg+3HTMoCAsX79euXl5Wn16tWaPHmy3nnnHf3Xf/2XEhISnLaLi4vTgQMHNGLECP3iF7/Qww8/rF69eumFF16QJKfLX3l5eSorK9PVq1f1+OOPKzs7W/Pnz9dHH32kiRMnenP3AHgQj6UD6DEee+wxFRYWymazqVevXr4uB4AXcUkLgCm98MILiouL0/Dhw3X16lW9++672rRpk5YsWULYAXogAg8AU7rpppv0q1/9Sv/7v/+rxsZGJSUl6de//rXmz5/v69IA+ACXtAAAgOlx0zIAADA9Ag8AADA9Ag8AADA9Ag8AADA9Ag8AADA9Ag8AADA9Ag8AADA9Ag8AADC9/w8OVslaOGMRnQAAAABJRU5ErkJggg==",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"g\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Now plot with a little more beautification...\n",
+ " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
+ " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
+ " - Change the size (any int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 12,
+ "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": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker=\"D\", s=50) # D for diamond"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### And we can add a Title to our plot..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Tree Age vs Height')"
+ ]
+ },
+ "execution_count": 13,
+ "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 = trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker=\"D\", s=50)\n",
+ "ax.set_title(\"Tree Age vs Height\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Correlation\n",
+ "\n",
+ "## Example 2: What is the correlation between our DataFrame columns?"
+ ]
+ },
+ {
+ "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>age</th>\n",
+ " <th>height</th>\n",
+ " <th>diameter</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>age</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.797468</td>\n",
+ " <td>0.854578</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>height</th>\n",
+ " <td>0.797468</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.839345</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>diameter</th>\n",
+ " <td>0.854578</td>\n",
+ " <td>0.839345</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " age height diameter\n",
+ "age 1.000000 0.797468 0.854578\n",
+ "height 0.797468 1.000000 0.839345\n",
+ "diameter 0.854578 0.839345 1.000000"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "corr_df = trees_df.corr()\n",
+ "corr_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 1: What is the correlation between age and height?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7974683544303798"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "corr_df['age']['height']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variating Stylistic Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 16,
+ "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": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=\"diameter\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We should scale up the sizes to make them more easily visible"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='age', ylabel='height'>"
+ ]
+ },
+ "execution_count": 17,
+ "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": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=trees_df[\"diameter\"] * 20) # this way allows you to make it bigger"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Titanic Data"
+ ]
+ },
+ {
+ "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>ID</th>\n",
+ " <th>Survived</th>\n",
+ " <th>Passenger Class</th>\n",
+ " <th>Name</th>\n",
+ " <th>Sex</th>\n",
+ " <th>Age</th>\n",
+ " <th>No. of Siblings/Spouses aboard</th>\n",
+ " <th>No. of Parents/Children aboard</th>\n",
+ " <th>Ticket Number</th>\n",
+ " <th>Fare</th>\n",
+ " <th>Cabin</th>\n",
+ " <th>Location Embarked</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Braund, Mr. Owen Harris</td>\n",
+ " <td>male</td>\n",
+ " <td>22.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>A/5 21171</td>\n",
+ " <td>7.2500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+ " <td>female</td>\n",
+ " <td>38.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>PC 17599</td>\n",
+ " <td>71.2833</td>\n",
+ " <td>C85</td>\n",
+ " <td>C</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>Heikkinen, Miss. Laina</td>\n",
+ " <td>female</td>\n",
+ " <td>26.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>STON/O2. 3101282</td>\n",
+ " <td>7.9250</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+ " <td>female</td>\n",
+ " <td>35.0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>113803</td>\n",
+ " <td>53.1000</td>\n",
+ " <td>C123</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>Allen, Mr. William Henry</td>\n",
+ " <td>male</td>\n",
+ " <td>35.0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>373450</td>\n",
+ " <td>8.0500</td>\n",
+ " <td>NaN</td>\n",
+ " <td>S</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " ID Survived Passenger Class \\\n",
+ "0 1 0 3 \n",
+ "1 2 1 1 \n",
+ "2 3 1 3 \n",
+ "3 4 1 1 \n",
+ "4 5 0 3 \n",
+ "\n",
+ " Name Sex Age \\\n",
+ "0 Braund, Mr. Owen Harris male 22.0 \n",
+ "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n",
+ "2 Heikkinen, Miss. Laina female 26.0 \n",
+ "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
+ "4 Allen, Mr. William Henry male 35.0 \n",
+ "\n",
+ " No. of Siblings/Spouses aboard No. of Parents/Children aboard \\\n",
+ "0 1 0 \n",
+ "1 1 0 \n",
+ "2 0 0 \n",
+ "3 1 0 \n",
+ "4 0 0 \n",
+ "\n",
+ " Ticket Number Fare Cabin Location Embarked \n",
+ "0 A/5 21171 7.2500 NaN S \n",
+ "1 PC 17599 71.2833 C85 C \n",
+ "2 STON/O2. 3101282 7.9250 NaN S \n",
+ "3 113803 53.1000 C123 S \n",
+ "4 373450 8.0500 NaN S "
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "titanic_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How do we create a *scatter plot* for various *class types*?\n",
+ "First, gather all the class types."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In Pandas..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 2, 3]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "classes = list(set(titanic_df[\"Passenger Class\"]))\n",
+ "classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In SQL..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 2, 3]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "classes = sorted(list(pd.read_sql(\"\"\"\n",
+ " SELECT DISTINCT `Passenger Class`\n",
+ " FROM titanic\n",
+ "\"\"\", titanic_conn)[\"Passenger Class\"]))\n",
+ "classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If you want to continue using SQL instead, don't close the connection!\n",
+ "titanic_conn.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 2: Change this scatter plot so that the data is only for `Passenger class = 3`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='Age', ylabel='Fare'>"
+ ]
+ },
+ "execution_count": 22,
+ "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": [
+ "titanic_df[titanic_df[\"Passenger Class\"] == 3].plot.scatter(x=\"Age\", y=\"Fare\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 3: Write a for loop that iterates through each Passenger Class and makes a plot for only that class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# write your code here\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Make the same series of plots, but this time make each plot a different color"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "\n",
+ "# write your code here\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Make the same series of plots, but this time make each plot a different color AND marker"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "\n",
+ "# write your code here\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i], marker=markers[i])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Food for thought:** Did you notice that it made 3 plots? What's deceptive about this?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "min_x = titanic_df[\"Age\"].min()\n",
+ "max_x = titanic_df[\"Age\"].max()\n",
+ "min_y = titanic_df[\"Fare\"].min()\n",
+ "max_y = titanic_df[\"Fare\"].max()\n",
+ "\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i], marker=markers[i], xlim=(min_x, max_x), ylim=(min_y, max_y))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We have to be VERY careful to not crop out data. We'll talk about this next lecture..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We can also make Subplots in plots, called an AxesSubplot, keyword `ax`\n",
+ "\n",
+ "<pre>\n",
+ "1. if AxesSuplot ax passed, then plot in that subplot\n",
+ "2. if ax is None, create a new AxesSubplot\n",
+ "3. return AxesSubplot that was used\n",
+ "</pre>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_area = None # don't change this...look at this variable in the last line\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " plot_area = pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i], marker=markers[i], ax=plot_area)"
+ ]
+ }
+ ],
+ "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/35_Plotting2/Lecture Code/Lec35_Plotting2_Template.ipynb b/s24/AmFam_Ashwin/35_Plotting2/Lecture Code/Lec35_Plotting2_Template.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..470e07d7382cbc23cd2beeec2788a16123ecfb29
--- /dev/null
+++ b/s24/AmFam_Ashwin/35_Plotting2/Lecture Code/Lec35_Plotting2_Template.ipynb
@@ -0,0 +1,583 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "from pandas import DataFrame, Series\n",
+ "\n",
+ "import sqlite3\n",
+ "import os\n",
+ "\n",
+ "import matplotlib\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "import requests\n",
+ "matplotlib.rcParams[\"font.size\"] = 12"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Titanic dataset: https://www.kaggle.com/datasets/yasserh/titanic-dataset\n",
+ "\n",
+ "A **copy** can be found at: `https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/raw/main/s24/AmFam_Ashwin/35_Plotting2/Lecture%20Code/titanic.csv`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 1: Requests and file writing\n",
+ "\n",
+ "Download this file and save it locally in the file `titanic.csv`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 2: Making a DataFrame\n",
+ "\n",
+ "Read the `\"titanic.csv\"` file into a Pandas DataFrame"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 3: Some of our column names are not very clear, let's change them.\n",
+ "These should be our headers: `\"ID\", \"Survived\", \"Passenger Class\", \"Name\", \"Sex\", \"Age\", \"No. of Siblings/Spouses aboard\", \"No. of Parents/Children aboard\", \"Ticket Number\", \"Fare\", \"Cabin\", \"Location Embarked\"`\n",
+ "\n",
+ "Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 4: Connect to our database version of this data!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This following code will create a `titanic.db` file and write the contents of `titanic_df` into this Database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "titanic_conn = sqlite3.connect(\"titanic.db\")\n",
+ "titanic_df.to_sql(\"titanic\", titanic_conn, if_exists=\"replace\", index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pd.read_sql(\"SELECT * FROM titanic LIMIT 5\", titanic_conn)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 5: Using SQL, get the 10 oldest male Titanic passengers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Warmup 6: Using SQL, get the average Fare for each Passenger Class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lecture 35: Scatter Plots\n",
+ "**Learning Objectives**\n",
+ "- Set the marker, color, and size of scatter plot data\n",
+ "- Calculate correlation between DataFrame columns\n",
+ "- Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Set the marker, color, and size of scatter plot data\n",
+ "\n",
+ "To start, let's look at some made-up data about Trees.\n",
+ "The city of Madison maintains a database of all the trees they care for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees = [\n",
+ " {\"age\": 1, \"height\": 1.5, \"diameter\": 0.8},\n",
+ " {\"age\": 1, \"height\": 1.9, \"diameter\": 1.2},\n",
+ " {\"age\": 1, \"height\": 1.8, \"diameter\": 1.4},\n",
+ " {\"age\": 2, \"height\": 1.8, \"diameter\": 0.9},\n",
+ " {\"age\": 2, \"height\": 2.5, \"diameter\": 1.5},\n",
+ " {\"age\": 2, \"height\": 3, \"diameter\": 1.8},\n",
+ " {\"age\": 2, \"height\": 2.9, \"diameter\": 1.7},\n",
+ " {\"age\": 3, \"height\": 3.2, \"diameter\": 2.1},\n",
+ " {\"age\": 3, \"height\": 3, \"diameter\": 2},\n",
+ " {\"age\": 3, \"height\": 2.4, \"diameter\": 2.2},\n",
+ " {\"age\": 2, \"height\": 3.1, \"diameter\": 2.9},\n",
+ " {\"age\": 4, \"height\": 2.5, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 3.9, \"diameter\": 3.1},\n",
+ " {\"age\": 4, \"height\": 4.9, \"diameter\": 2.8},\n",
+ " {\"age\": 4, \"height\": 5.2, \"diameter\": 3.5},\n",
+ " {\"age\": 4, \"height\": 4.8, \"diameter\": 4},\n",
+ "]\n",
+ "trees_df = DataFrame(trees)\n",
+ "trees_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Scatter Plots\n",
+ "We can make a scatter plot of a DataFrame using the following function...\n",
+ "\n",
+ "`df_name.plot.scatter(x=\"x_col_name\", y=\"y_col_name\", color=\"peachpuff\")`\n",
+ "\n",
+ "## Example 1: Plot the trees data comparing a tree's age to its height\n",
+ "<pre>\n",
+ " - What is `df_name`?\n",
+ " - What is `x_col_name`?\n",
+ " - What is `y_col_name`?\n",
+ "</pre>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"g\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Now plot with a little more beautification...\n",
+ " - Use a new [color](https://matplotlib.org/3.5.0/_images/sphx_glr_named_colors_003.png)\n",
+ " - Use a type of [marker](https://matplotlib.org/stable/api/markers_api.html)\n",
+ " - Change the size (any int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker=\"D\", s=50) # D for diamond"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### And we can add a Title to our plot..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ax = trees_df.plot.scatter(x=\"age\", y=\"height\", color=\"r\", marker=\"D\", s=50)\n",
+ "ax.set_title(\"Tree Age vs Height\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Correlation\n",
+ "\n",
+ "## Example 2: What is the correlation between our DataFrame columns?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "corr_df = trees_df.corr()\n",
+ "corr_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 1: What is the correlation between age and height?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Variating Stylistic Parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=\"diameter\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We should scale up the sizes to make them more easily visible"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "trees_df.plot.scatter(x=\"age\", y=\"height\", marker=\"H\", s=trees_df[\"diameter\"] * 20) # this way allows you to make it bigger"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use subplots to group scatterplot data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Re-visit the Titanic Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "titanic_df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How do we create a *scatter plot* for various *class types*?\n",
+ "First, gather all the class types."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In Pandas..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classes = list(set(titanic_df[\"Passenger Class\"]))\n",
+ "classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In SQL..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "classes = sorted(list(pd.read_sql(\"\"\"\n",
+ " SELECT DISTINCT `Passenger Class`\n",
+ " FROM titanic\n",
+ "\"\"\", titanic_conn)[\"Passenger Class\"]))\n",
+ "classes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### In reality, you can choose to write Pandas or SQL queries (or a mix of both!). For the rest of this lecture, we'll use Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# If you want to continue using SQL instead, don't close the connection!\n",
+ "titanic_conn.close()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 2: Change this scatter plot so that the data is only for `Passenger class = 3`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "titanic_df.plot.scatter(x=\"Age\", y=\"Fare\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exercise 3: Write a for loop that iterates through each Passenger Class and makes a plot for only that class"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Make the same series of plots, but this time make each plot a different color"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Make the same series of plots, but this time make each plot a different color AND marker"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "\n",
+ "# write your code here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Food for thought:** Did you notice that it made 3 plots? What's deceptive about this?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "min_x = titanic_df[\"Age\"].min()\n",
+ "max_x = titanic_df[\"Age\"].max()\n",
+ "min_y = titanic_df[\"Fare\"].min()\n",
+ "max_y = titanic_df[\"Fare\"].max()\n",
+ "\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i], marker=markers[i], xlim=(min_x, max_x), ylim=(min_y, max_y))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We have to be VERY careful to not crop out data. We'll talk about this next lecture..."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We can also make Subplots in plots, called an AxesSubplot, keyword `ax`\n",
+ "\n",
+ "<pre>\n",
+ "1. if AxesSuplot ax passed, then plot in that subplot\n",
+ "2. if ax is None, create a new AxesSubplot\n",
+ "3. return AxesSubplot that was used\n",
+ "</pre>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plot_area = None # don't change this...look at this variable in the last line\n",
+ "colors = [\"blue\", \"green\", \"red\"]\n",
+ "markers = [\"o\", \"^\", \"v\"]\n",
+ "for i in range(len(classes)):\n",
+ " pass_class = classes[i]\n",
+ " \n",
+ " # make a df just of just the data for this variety\n",
+ " pass_class_df = titanic_df[titanic_df[\"Passenger Class\"] == pass_class] \n",
+ " \n",
+ " # make a scatter plot for this passenger class\n",
+ " plot_area = pass_class_df.plot.scatter(x=\"Age\", y=\"Fare\", label=pass_class, color=colors[i], marker=markers[i], ax=plot_area)"
+ ]
+ }
+ ],
+ "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
+}