From be7d031ea08b7a1363f2371a78e0c0b16bb87c3e Mon Sep 17 00:00:00 2001
From: gsingh58 <gurmail-singh@wisc.edu>
Date: Fri, 1 Dec 2023 00:17:28 -0600
Subject: [PATCH] Lec35 updated

---
 .../35N_Plotting-2/bus.db                     |  Bin 0 -> 237568 bytes
 .../35N_Plotting-2/iris-flowers.db            |  Bin 0 -> 16384 bytes
 .../35N_Plotting-2/iris.csv                   |  151 +
 .../lec_35N_plotting2_scatter_plots.ipynb     | 3027 +++++++++++++++++
 ..._scatter_plots_template_Gurmail_lec1.ipynb |  857 +++++
 ..._scatter_plots_template_Gurmail_lec2.ipynb |  857 +++++
 6 files changed, 4892 insertions(+)
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/bus.db
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/iris-flowers.db
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/iris.csv
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots.ipynb
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec1.ipynb
 create mode 100644 f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec2.ipynb

diff --git a/f23/Gurmail_Lecture_Notes/35N_Plotting-2/bus.db b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/bus.db
new file mode 100644
index 0000000000000000000000000000000000000000..3303edbe663ab1bcff61a5c3977edc23fbd1f0c7
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diff --git a/f23/Gurmail_Lecture_Notes/35N_Plotting-2/iris.csv b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/iris.csv
new file mode 100644
index 0000000..5c4316c
--- /dev/null
+++ b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/iris.csv
@@ -0,0 +1,151 @@
+5.1,3.5,1.4,0.2,Iris-setosa
+4.9,3.0,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+
diff --git a/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots.ipynb b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots.ipynb
new file mode 100644
index 0000000..c1c91ad
--- /dev/null
+++ b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots.ipynb
@@ -0,0 +1,3027 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Announcements - Friday, December 1\n",
+    "\n",
+    "* Download ALL files for today's lecture\n",
+    "* <b>If you have any problem with P7 or P8 grades, please send me (Gurmail.Singh@wisc.edu) an email by December 7.</b>\n",
+    "* Quiz 9 is due tonight at 11:59 pm\n",
+    "* Late days may not be used on P13\n",
+    "* If you have questions, it is almost always faster to \n",
+    "  * Post on Piazza\n",
+    "  * Go to [office hours](https://sites.google.com/wisc.edu/cs220-oh-f23/home?pli=1) \n",
+    "### Conflict Form\n",
+    "  * [Final - December 19, 7:45 am](https://cs220.cs.wisc.edu/f23/surveys.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting 2 - Scatter Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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",
+    "# new import statement\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import requests\n",
+    "matplotlib.rcParams[\"font.size\"] = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Wrapping up bus dataset example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What are the top routes, and how many people ride them daily?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"bus.db\"\n",
+    "# assert existence of path\n",
+    "assert os.path.exists(path)\n",
+    "\n",
+    "# establish connection to bus.db\n",
+    "conn = sqlite3.connect(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>Route</th>\n",
+       "      <th>daily</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>80</td>\n",
+       "      <td>10211.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4808.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4537.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4425.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2708.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2656.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2179.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>38</td>\n",
+       "      <td>1955.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1868.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1634.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1373.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>16</td>\n",
+       "      <td>1258.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1039.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>22</td>\n",
+       "      <td>995.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>19</td>\n",
+       "      <td>827.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>50</td>\n",
+       "      <td>748.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>67</td>\n",
+       "      <td>729.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>70</td>\n",
+       "      <td>710.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>30</td>\n",
+       "      <td>687.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>72</td>\n",
+       "      <td>636.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13</td>\n",
+       "      <td>615.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>40</td>\n",
+       "      <td>602.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>21</td>\n",
+       "      <td>590.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>20</td>\n",
+       "      <td>545.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>71</td>\n",
+       "      <td>497.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>56</td>\n",
+       "      <td>477.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>57</td>\n",
+       "      <td>464.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>73</td>\n",
+       "      <td>448.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>75</td>\n",
+       "      <td>435.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>44</td>\n",
+       "      <td>416.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>11</td>\n",
+       "      <td>392.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>47</td>\n",
+       "      <td>379.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>81</td>\n",
+       "      <td>371.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>58</td>\n",
+       "      <td>362.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>12</td>\n",
+       "      <td>329.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>37</td>\n",
+       "      <td>319.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>27</td>\n",
+       "      <td>298.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>17</td>\n",
+       "      <td>294.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>82</td>\n",
+       "      <td>219.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>33</td>\n",
+       "      <td>206.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>52</td>\n",
+       "      <td>176.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>39</td>\n",
+       "      <td>140.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>35</td>\n",
+       "      <td>140.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>31</td>\n",
+       "      <td>139.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>51</td>\n",
+       "      <td>137.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>55</td>\n",
+       "      <td>129.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>84</td>\n",
+       "      <td>114.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>29</td>\n",
+       "      <td>111.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>26</td>\n",
+       "      <td>107.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>32</td>\n",
+       "      <td>86.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>34</td>\n",
+       "      <td>81.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>49</td>\n",
+       "      <td>61.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>36</td>\n",
+       "      <td>59.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>48</td>\n",
+       "      <td>30.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>25</td>\n",
+       "      <td>24.19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Route     daily\n",
+       "0      80  10211.79\n",
+       "1       2   4808.03\n",
+       "2       6   4537.02\n",
+       "3      10   4425.23\n",
+       "4       3   2708.55\n",
+       "5       4   2656.99\n",
+       "6      15   2179.98\n",
+       "7      38   1955.85\n",
+       "8      28   1868.31\n",
+       "9       5   1634.69\n",
+       "10     14   1373.81\n",
+       "11     16   1258.93\n",
+       "12     18   1039.57\n",
+       "13     22    995.21\n",
+       "14     19    827.53\n",
+       "15     50    748.75\n",
+       "16     67    729.54\n",
+       "17     70    710.80\n",
+       "18     30    687.13\n",
+       "19     72    636.95\n",
+       "20     13    615.20\n",
+       "21     40    602.92\n",
+       "22     21    590.86\n",
+       "23     20    545.91\n",
+       "24     71    497.09\n",
+       "25     56    477.44\n",
+       "26     57    464.86\n",
+       "27     73    448.87\n",
+       "28     75    435.35\n",
+       "29     44    416.90\n",
+       "30     11    392.43\n",
+       "31     47    379.89\n",
+       "32     81    371.76\n",
+       "33     58    362.59\n",
+       "34     12    329.51\n",
+       "35     37    319.82\n",
+       "36     27    298.07\n",
+       "37     17    294.55\n",
+       "38     82    219.48\n",
+       "39     33    206.53\n",
+       "40      1    181.44\n",
+       "41     52    176.24\n",
+       "42     39    140.89\n",
+       "43     35    140.42\n",
+       "44     31    139.87\n",
+       "45     51    137.57\n",
+       "46     55    129.23\n",
+       "47     84    114.21\n",
+       "48     29    111.28\n",
+       "49     26    107.10\n",
+       "50     32     86.47\n",
+       "51     34     81.97\n",
+       "52     49     61.83\n",
+       "53     36     59.13\n",
+       "54     48     30.65\n",
+       "55     25     24.19"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_sql(\"\"\"\n",
+    "SELECT Route, SUM(DailyBoardings) AS daily\n",
+    "FROM boarding\n",
+    "GROUP BY Route\n",
+    "ORDER BY daily DESC\n",
+    "\"\"\", conn)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     10211.79\n",
+       "1      4808.03\n",
+       "2      4537.02\n",
+       "3      4425.23\n",
+       "4      2708.55\n",
+       "5      2656.99\n",
+       "6      2179.98\n",
+       "7      1955.85\n",
+       "8      1868.31\n",
+       "9      1634.69\n",
+       "10     1373.81\n",
+       "11     1258.93\n",
+       "12     1039.57\n",
+       "13      995.21\n",
+       "14      827.53\n",
+       "15      748.75\n",
+       "16      729.54\n",
+       "17      710.80\n",
+       "18      687.13\n",
+       "19      636.95\n",
+       "20      615.20\n",
+       "21      602.92\n",
+       "22      590.86\n",
+       "23      545.91\n",
+       "24      497.09\n",
+       "25      477.44\n",
+       "26      464.86\n",
+       "27      448.87\n",
+       "28      435.35\n",
+       "29      416.90\n",
+       "30      392.43\n",
+       "31      379.89\n",
+       "32      371.76\n",
+       "33      362.59\n",
+       "34      329.51\n",
+       "35      319.82\n",
+       "36      298.07\n",
+       "37      294.55\n",
+       "38      219.48\n",
+       "39      206.53\n",
+       "40      181.44\n",
+       "41      176.24\n",
+       "42      140.89\n",
+       "43      140.42\n",
+       "44      139.87\n",
+       "45      137.57\n",
+       "46      129.23\n",
+       "47      114.21\n",
+       "48      111.28\n",
+       "49      107.10\n",
+       "50       86.47\n",
+       "51       81.97\n",
+       "52       61.83\n",
+       "53       59.13\n",
+       "54       30.65\n",
+       "55       24.19\n",
+       "Name: daily, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# let's extract daily column from df\n",
+    "df[\"daily\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's create a bar plot from daily column Series\n",
+    "df[\"daily\"].plot.bar()\n",
+    "\n",
+    "# Oops wrong x-axis labels!"
+   ]
+  },
+  {
+   "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>Route</th>\n",
+       "      <th>daily</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>80</td>\n",
+       "      <td>10211.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4808.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>4537.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4425.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2708.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2656.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>15</td>\n",
+       "      <td>2179.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>38</td>\n",
+       "      <td>1955.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1868.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1634.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14</td>\n",
+       "      <td>1373.81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>16</td>\n",
+       "      <td>1258.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>18</td>\n",
+       "      <td>1039.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>22</td>\n",
+       "      <td>995.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>19</td>\n",
+       "      <td>827.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>50</td>\n",
+       "      <td>748.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>67</td>\n",
+       "      <td>729.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>70</td>\n",
+       "      <td>710.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>30</td>\n",
+       "      <td>687.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>72</td>\n",
+       "      <td>636.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13</td>\n",
+       "      <td>615.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>40</td>\n",
+       "      <td>602.92</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>21</td>\n",
+       "      <td>590.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>20</td>\n",
+       "      <td>545.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>71</td>\n",
+       "      <td>497.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>56</td>\n",
+       "      <td>477.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>57</td>\n",
+       "      <td>464.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>73</td>\n",
+       "      <td>448.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>75</td>\n",
+       "      <td>435.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>44</td>\n",
+       "      <td>416.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>11</td>\n",
+       "      <td>392.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>47</td>\n",
+       "      <td>379.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>81</td>\n",
+       "      <td>371.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>58</td>\n",
+       "      <td>362.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>12</td>\n",
+       "      <td>329.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>37</td>\n",
+       "      <td>319.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>27</td>\n",
+       "      <td>298.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>17</td>\n",
+       "      <td>294.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>82</td>\n",
+       "      <td>219.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>33</td>\n",
+       "      <td>206.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>1</td>\n",
+       "      <td>181.44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>52</td>\n",
+       "      <td>176.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>39</td>\n",
+       "      <td>140.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>35</td>\n",
+       "      <td>140.42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>31</td>\n",
+       "      <td>139.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>51</td>\n",
+       "      <td>137.57</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>55</td>\n",
+       "      <td>129.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>84</td>\n",
+       "      <td>114.21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>29</td>\n",
+       "      <td>111.28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>26</td>\n",
+       "      <td>107.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>32</td>\n",
+       "      <td>86.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>34</td>\n",
+       "      <td>81.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>49</td>\n",
+       "      <td>61.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>36</td>\n",
+       "      <td>59.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>48</td>\n",
+       "      <td>30.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>25</td>\n",
+       "      <td>24.19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Route     daily\n",
+       "0      80  10211.79\n",
+       "1       2   4808.03\n",
+       "2       6   4537.02\n",
+       "3      10   4425.23\n",
+       "4       3   2708.55\n",
+       "5       4   2656.99\n",
+       "6      15   2179.98\n",
+       "7      38   1955.85\n",
+       "8      28   1868.31\n",
+       "9       5   1634.69\n",
+       "10     14   1373.81\n",
+       "11     16   1258.93\n",
+       "12     18   1039.57\n",
+       "13     22    995.21\n",
+       "14     19    827.53\n",
+       "15     50    748.75\n",
+       "16     67    729.54\n",
+       "17     70    710.80\n",
+       "18     30    687.13\n",
+       "19     72    636.95\n",
+       "20     13    615.20\n",
+       "21     40    602.92\n",
+       "22     21    590.86\n",
+       "23     20    545.91\n",
+       "24     71    497.09\n",
+       "25     56    477.44\n",
+       "26     57    464.86\n",
+       "27     73    448.87\n",
+       "28     75    435.35\n",
+       "29     44    416.90\n",
+       "30     11    392.43\n",
+       "31     47    379.89\n",
+       "32     81    371.76\n",
+       "33     58    362.59\n",
+       "34     12    329.51\n",
+       "35     37    319.82\n",
+       "36     27    298.07\n",
+       "37     17    294.55\n",
+       "38     82    219.48\n",
+       "39     33    206.53\n",
+       "40      1    181.44\n",
+       "41     52    176.24\n",
+       "42     39    140.89\n",
+       "43     35    140.42\n",
+       "44     31    139.87\n",
+       "45     51    137.57\n",
+       "46     55    129.23\n",
+       "47     84    114.21\n",
+       "48     29    111.28\n",
+       "49     26    107.10\n",
+       "50     32     86.47\n",
+       "51     34     81.97\n",
+       "52     49     61.83\n",
+       "53     36     59.13\n",
+       "54     48     30.65\n",
+       "55     25     24.19"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Route'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df.set_index(\"Route\")\n",
+    "\n",
+    "# let's plot for top 5 routes alone\n",
+    "df.head(5)[\"daily\"].plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Route\n",
+       "80       10211.79\n",
+       "2         4808.03\n",
+       "6         4537.02\n",
+       "10        4425.23\n",
+       "3         2708.55\n",
+       "other    29296.56\n",
+       "Name: daily, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# let's use slicing to aggregate the rest of the data\n",
+    "s = df[\"daily\"].iloc[:5]\n",
+    "s[\"other\"] = df[\"daily\"].iloc[5:].sum()\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's plot the bars\n",
+    "ax = (s / 1000).plot.bar(color = \"k\")\n",
+    "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 1:  Downloading IRIS dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use requests to get this URL\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+    "response = requests.get(url)\n",
+    "\n",
+    "# check that the request was successful\n",
+    "response.raise_for_status()\n",
+    "\n",
+    "# open a file called \"iris.csv\" for writing the data locally\n",
+    "file_obj = open(\"iris.csv\", \"w\")\n",
+    "\n",
+    "# write the text of response to the file object\n",
+    "file_obj.write(response.text)\n",
+    "\n",
+    "# close the file object\n",
+    "file_obj.close()\n",
+    "\n",
+    "# Look at the file you downloaded. What's wrong with it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 2: Making a DataFrame"
+   ]
+  },
+  {
+   "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>5.1</th>\n",
+       "      <th>3.5</th>\n",
+       "      <th>1.4</th>\n",
+       "      <th>0.2</th>\n",
+       "      <th>Iris-setosa</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   5.1  3.5  1.4  0.2  Iris-setosa\n",
+       "0  4.9  3.0  1.4  0.2  Iris-setosa\n",
+       "1  4.7  3.2  1.3  0.2  Iris-setosa\n",
+       "2  4.6  3.1  1.5  0.2  Iris-setosa\n",
+       "3  5.0  3.6  1.4  0.2  Iris-setosa\n",
+       "4  5.4  3.9  1.7  0.4  Iris-setosa"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read the \"iris.csv\" file into a Pandas dataframe\n",
+    "iris_df = pd.read_csv(\"iris.csv\")\n",
+    "\n",
+    "# display the head of the data frame\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 3: Our CSV file has no header. Let's add column names.\n",
+    "- Refer to the documentation: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep-length</th>\n",
+       "      <th>sep-width</th>\n",
+       "      <th>pet-length</th>\n",
+       "      <th>pet-width</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sep-length  sep-width  pet-length  pet-width        class\n",
+       "0         5.1        3.5         1.4        0.2  Iris-setosa\n",
+       "1         4.9        3.0         1.4        0.2  Iris-setosa\n",
+       "2         4.7        3.2         1.3        0.2  Iris-setosa\n",
+       "3         4.6        3.1         1.5        0.2  Iris-setosa\n",
+       "4         5.0        3.6         1.4        0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Attribute Information:\n",
+    "# 1. sepal length in cm\n",
+    "# 2. sepal width in cm\n",
+    "# 3. petal length in cm\n",
+    "# 4. petal width in cm\n",
+    "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+    "\n",
+    "# These should be our headers \n",
+    "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
+    "\n",
+    "iris_df = pd.read_csv(\"iris.csv\",\n",
+    "                 names = [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"])\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 4: Connect to our database version of this data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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>iris</td>\n",
+       "      <td>iris</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n  \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    type  name tbl_name  rootpage  \\\n",
+       "0  table  iris     iris         2   \n",
+       "\n",
+       "                                                 sql  \n",
+       "0  CREATE TABLE \"iris\" (\\n\"sep-length\" REAL,\\n  \"...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
+    "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
+    "Break any ties by ordering by the shortest sepal width."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep-length</th>\n",
+       "      <th>sep-width</th>\n",
+       "      <th>pet-length</th>\n",
+       "      <th>pet-width</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.4</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sep-length  sep-width  pet-length  pet-width        class\n",
+       "0         5.8        4.0         1.2        0.2  Iris-setosa\n",
+       "1         5.7        3.8         1.7        0.3  Iris-setosa\n",
+       "2         5.7        4.4         1.5        0.4  Iris-setosa\n",
+       "3         5.5        3.5         1.3        0.2  Iris-setosa\n",
+       "4         5.5        4.2         1.4        0.2  Iris-setosa\n",
+       "5         5.4        3.4         1.7        0.2  Iris-setosa\n",
+       "6         5.4        3.4         1.5        0.4  Iris-setosa\n",
+       "7         5.4        3.7         1.5        0.2  Iris-setosa\n",
+       "8         5.4        3.9         1.7        0.4  Iris-setosa\n",
+       "9         5.4        3.9         1.3        0.4  Iris-setosa"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_sql(\"\"\"\n",
+    "    SELECT *\n",
+    "    FROM iris\n",
+    "    WHERE class = 'Iris-setosa'\n",
+    "    ORDER BY `sep-length` DESC, `sep-width` ASC\n",
+    "    LIMIT 10\n",
+    "\"\"\", iris_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": 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>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": 18,
+     "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\", \\\n",
+    "                     color = \"red\", marker = \"*\", s = 50)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the trees data comparing a tree's age to its height...\n",
+    " - What is `df_name`?\n",
+    " - What is `x_col_name`?\n",
+    " - What is `y_col_name`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age', ylabel='height'>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"g\")  \n",
+    "# TODO: change y to diameter"
+   ]
+  },
+  {
+   "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": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age', ylabel='height'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot with some more beautification options.\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\",  marker = \"D\", s = 50) # D for diamond"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Tree Age vs Height')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ppoj4fqsrjIgdwA8lvQ/4CPCluuX31szeKel8soC4sNV1mJnZrmt2imlF4rkL6uYDOHAn131QC+2CxJf0zMxsak0aEBHxsk6sRNIgcDSwmuzy1WPJTh39SaLt8cBPIuLh/GqnpcA/d6IOMzNrXVF3cw2y00m/Ah4DPg98PCKukzQkaYukai/zMcAdkraSdWJfy/OPWszMbIq1fBVTzS036m0ne+O/Frg0dbVRRIwDR6ZeNyIeoOZ7FRFxJnBmq3WZmdnUaOcy1y+R3XbjS8B6YAj4r2Snf34DfBJ4KfCXHa7RzMy6oJ2AWAy8OSJ+XX1C0r8B34mIwySNAv8bB4SZ2bTQTh/Ei4Atdc9tBX4/f3wPsE8HajIzsxJoJyC+RfY9iGMlzZd0LPA/8+cB/hC4v8P1mZlZl7QTEB8Gfgx8jew2GcuBW4A/y5ffC7yto9WZWXmMjsLgYPbT+kLLfRARsQ04K59Syzd0qigzK5nRUTjhBJiYyH6uXg0jI92uyqZYs1ttvCkibsofH92oXTu32jCzHlMbDuCQ6CPNjiC+QnbjPEjfdgN2/lYbZlZ29eFQ5ZDoC81utXF4zeOO3HbDzHpEo3CockhMe23dakPS7pLeKGlRPr+npD2npjTra+4Q7b5FixqHQ9XERNbOpqWWA0LSq8i+6/B1nj3ddCRw2RTUZf2s+sl1fDz76ZDojlWrYGBg8jYDA1k7m5baOYK4FPhsRMwHns6f+wHgsaOtcxp1iDokijcykp0+ahQSAwM+vTTNtRMQhwFX5Y8DICK2kg0farbrmnWIOiSK1ygkHA59oZ2AuB94be0Tkv4A+EUnC7I+1WqHqEOiePUh4XDoG+0ExFLgeknnAbMkfQb4F+DsKanM+os7RMutGhKVisOhj7QcEBGxGngLUAHWkN3u+8SI+M7UlGZ9xR2iZqXTzlVMLwBeRzY+9G+APYGPS1o5RbVZP3GHaLn5yrK+1M4ppiuAjwNPkPU7/LJmakrSVZIekvSEpHskfXCStmdI2iBpk6TLJM1qo07rVSMjsGxZetmyZQ6HbvGVZX1LEalRRBMNpceAl0XE4zu1Iukw4BcRsV3SfLLTVG+LiFvr2h0HrASOBn4NfBP494hI3iSwanh4OMbGxnamNCuLyTqqfQTRHd4n056kWyNiOLWsnSOIB4Cd/iQfEWsjYnt1Np8OSjT9ALAib/8YsIxsNDubznwVU/l4n/S9SY8g6u7gegTwHuCLwMO17Vq9m6ukr5C92e9BNqbEmyJiS12b24ELImJVPr8/MA7sHxGP1rVdAiwBGBoaeu26detaKcPKaHAwO7/dTKUCGzdOfT3mfdInJjuCaHY319QdXC+om2/5bq4R8VFJf042+txRwPZEs9nAppr56uO9gOcEREQsJxu4iOHh4dbOlVk5rVo1+adV8FVMRfM+6XuTnmKKiJe1MLV1q++I2BERPwReAnwk0WQLMKdmvvp4czvrsR5TvYppVoOzmLNm+Xx30XxlWd9r626uHTaTdB/EWmBBzfwC4OH600tmVgDfaqOvFRIQkgYlnSxptqQZ+ZVKpwCpvouVwGmSDpW0L9k3tS8vok7romqH6PbUWUey590h2h2+1UbfKuoIIshOJ/0KeAz4PPDxiLhO0pCkLZKGACLiRuAiYBRYl0/nFFSndYtvtVFuvtVGX2r5exBl5+9B9Lhml1SCP7maTYFOfQ/CbOq4Q9SsdBwQVh7uEDUrFQeElYs7RM1KwwFh5eMOUbNSaPZNarPuGBnx7RvMusxHEGZmluSAMDOzJAeEmZklOSDMzCzJAWFmZkkOCDMzS3JAmJlZkgPCzMySHBBmZpbkgLByGh2FwUEPEGTWzBT+rTggrHyqY0OMj3sUObPJTPHfSlFDjs6StELSOkmbJd0m6fgGbRdL2pGPMledjiqiTiuB+oGDJiYcEmYpBfytFHUEMRNYDxwJ7A0sBa6RNK9B+5sjYnbNtKaYMq2rGo0q55Awe66C/lYKCYiI2BoR50bE/RHx24hYDdwHvLaI9VsPaDbkqEPCLFPg30pX+iAkzQVeAaxt0OQISY9IukfSUknJ25JLWiJpTNLY+Pj4lNVrBVi0aPLxqCFbvmhRMfWYlVWBfyuFB4Sk3YGrgSsi4u5Ek5uAw4FB4CTgFOBTqdeKiOURMRwRw5VKZapKtiKsWtV4POqqgYGsnXWHrywrhwL/VgoNCEm7AVcCTwGnp9pExL0RcV9+KupO4Hzg3QWWad3QaDzqKg892l2+sqw8CvxbKSwgJAlYAcwFToqIp1v81QA0ZYVZeTT6j+9w6C5fWVY+Bf2tFHkEcSlwCPD2iHiyUSNJx+d9FEiaT3bF03XFlGhdV/8f3+HQXb6yrLwK+Fsp6nsQBwAfBhYCG2q+3/BeSUP546G8+THAHZK2AjcA1wIXFFGnlUT1P36l4nDoJl9ZVn5T/LeiiOjoC3bL8PBwjI2NdbsMs+ljcDDrc2imUoGNG6e+HpsSkm6NiOHUMt9qw8zSfGVZ33NAmFmaryzrew4IM2vMV5b1NQeEmU3OV5b1LQeEmTXnK8v6UvIeR2ZmzzMy4quV+oyPIMzMLMkBYWZmSQ4IMzNLckCYmVmSA8LMzJIcEGZmluSAMDOzJAeEmZklOSDMzCzJAWFmZkkOCDMzSypqyNFZklZIWidps6TbJB0/SfszJG2QtEnSZZJmFVGnmZk9q6gjiJnAeuBIYG9gKXCNpHn1DSUdB5xFNjb1POBA4LyC6jQzs1whARERWyPi3Ii4PyJ+GxGrgfuA1yaafwBYERFrI+IxYBmwuIg6zczsWV3pg5A0F3gFsDax+DDg9pr524G5kvZLvM4SSWOSxsZbGVzdzMxaVnhASNoduBq4IiLuTjSZDWyqma8+3qu+YUQsj4jhiBiuVCo7V9DoKAwOZj/NzOx3Cg0ISbsBVwJPAac3aLYFmFMzX328ueMFjY7CCSfA+Hj20yFhZvY7hQWEJAErgLnASRHxdIOma4EFNfMLgIcj4tGOFlQNh4mJbH5iwiFhZlajyCOIS4FDgLdHxJOTtFsJnCbpUEn7AmcDl3e0kvpwqHJImJn9TlHfgzgA+DCwENggaUs+vVfSUP54CCAibgQuAkaBdfl0TseKaRQOVQ4JMzMAFBHdrqEjhoeHY2xsrHnDwcGsz6GZSsUDtHfT6CgsWgSrVsHISLerMZu2JN0aEcOpZf13q41Vq2BgYPI2AwNZO+sOXzxgVgr9FxAjI7B6NcxqcPeOWbOy5f7U2h2+eMCsNPovIKy8fPGAWan0X0BU34S2b08v377db0bd4IsHzErHndSNuJO6WN4vZl3hTupa7qQuJ+8Xs9Lpv4CodlI3ejMaGHAndTd4v5iVTv8FBDR+M/KbUHd5v5iVSn8GBDz/zchvQuXg/WJWGv0bEPDsm1Gl4jehMvF+MSuFmd0uoOtGRnxVTBl5v5h1XX8fQZiZWUMOCDMzS3JAmJlZkgPCzMySHBCjo9ltHnyPHzOz5+jvgPC4A2ZmDRUWEJJOlzQmabukyydpt1jSjpphSbdIOqrjBXncATOzSRV5BPFr4HPAZS20vTkiZtdMazpaiccdMDNrqrCAiIhrI+JfgUeLWmeSxx0wM2tJWfsgjpD0iKR7JC2VlPzGt6Ql+WmrsfFWxhIAWLSocThUTUxk7czM+lgZA+Im4HBgEDgJOAX4VKphRCyPiOGIGK5UKq29uscdMDNrSekCIiLujYj7IuK3EXEncD7w7o6twOMOmJm1pHQBkRCAOvqKHnfAzKypIi9znSnphcAMYIakF6b6FiQdL2lu/ng+sBS4ruMFedwBM7NJFXkEcTbwJHAW8L788dmShvLvOgzl7Y4B7pC0FbgBuBa4YEoq8rgDZmYNKSK6XUNHDA8Px9jYWLfLMDPrKZJujYjh1LJe6IMwM7MucECYmVmSA8LMzJKmTR+EpHFg3U7++v7AIx0sp5u8LeU0XbZlumwHeFuqDoiI5DeNp01A7ApJY406aXqNt6Wcpsu2TJftAG9LK3yKyczMkhwQZmaW5IDILO92AR3kbSmn6bIt02U7wNvSlPsgzMwsyUcQZmaW5IAwM7MkB4SZmSX1RUBIOj0fmnS7pMubtD1D0gZJmyRdJmlWQWW2pNVtkbRY0o78TrnV6ajCCm1C0ixJKyStk7RZ0m2Sjp+kfWn3SzvbUvb9AiDpKkkPSXoiH/b3g5O0LfN+aWk7emGfVEk6WNI2SVdN0qZj+6QvAgL4NfA54LLJGkk6jux25McA84ADgfOmurg2tbQtuZsjYnbNtGZqS2vLTGA9cCSwN9m4H9dImlffsAf2S8vbkivzfgG4EJgXEXOAdwCfk/Ta+kY9sF9a2o5c2fdJ1ZeBWxot7PQ+6YuAiIhrI+JfgUebNP0AsCIi1kbEY8AyYPEUl9eWNral1CJia0ScGxH358PLrgbuA1J/wKXeL21uS+nl/87bq7P5dFCiadn3S6vb0RMknQw8DnxvkmYd3Sd9ERBtOAy4vWb+dmCupP26VM+uOkLSI/nh9dLUCH5lkY8i+ApgbWJxT+2XJtsCPbBfJH1F0gRwN/AQ2eBd9Uq/X1rcDij5PpE0Bzgf+GSTph3dJw6I55oNbKqZrz7eqwu17KqbgMOBQeAk4BTgU12tqAFJuwNXA1dExN2JJj2zX1rYlp7YLxHxUbJ/3zeSjeq4PdGs9Pulxe3ohX2yjOzIYH2Tdh3dJw6I59oCzKmZrz7e3IVadklE3BsR9+WnPO4k+/Tx7m7XVU/SbsCVwFPA6Q2a9cR+aWVbemW/AETEjoj4IfAS4COJJj2xX5ptR9n3iaSFwLHAJS007+g+cUA811pgQc38AuDhiOjp8/25ANTtImpJErACmAucFBFPN2ha+v3SxrbUK91+SZhJ+tx96fdLnUbbUa9s++Qosg7nByRtAM4ETpL0k0Tbju6TvggISTMlvRCYAcyQ9MIG5xhXAqdJOlTSvsDZwOUFltpUq9si6fj8XDiS5pNdWXNdsdU2dSlwCPD2iHhyknal3y+0uC1l3y+SBiWdLGm2pBn5VTGnAN9PNC/tfmlnO8q+T8jus3QQsDCfvgpcDxyXaNvZfRIR034CzuXZqxiq07nAENkh2VBN208ADwNPAN8AZnW7/p3ZFuDz+XZsBe4lO2zevdv112zHAXnt2/K6q9N7e22/tLMtPbBfKsAPyK6WeQK4E/hQvqxn9ks721H2fZLYtnOBq4rYJ75Zn5mZJfXFKSYzM2ufA8LMzJIcEGZmluSAMDOzJAeEmZklOSDMzCzJAWFmZkkOCDMzS3JAmJlZkgPCbBdIOkvSL/OhRu+S9Mf58zMkfSEfY+A+ZUPFRvW+WZL2VjZM6UOSHpT0OUkzurs1Zs9VqkExzHrQL8nGGtgAvAe4StLLgXcCx5PdXG0r8M91v3cF2f1yXg7sCawmG7b0a4VUbdYC34vJrIMk/RQ4B/gYsCoivpY/fyzwXWB3YD/gAWCfyO/8KukUYElEjHSjbrMUH0GY7QJJp5LdPXNe/tRsYH/g98mOCKpqHx9AFhQPZcNIANnp3majhZkVygFhtpMkHQB8HTgGuDkiduRHECIb//glNc1fWvN4PdnQl/tHxDMFlWvWNndSm+28PcnGgRgHkPSnZGMbA1wDfEzSiyXtA3y6+ksR8RDwHeALkuZI2k3SQZKOLLR6syYcEGY7KSLuAr4A3EzW4fwq4Ef54q+ThcAdwG3ADcAzwI58+anAC4C7gMeAfwFeVFTtZq1wJ7VZASQdD3w1Ig7odi1mrfIRhNkUkLSHpLfmY4i/mOzKpm92uy6zdvgIwmwKSBogGxN5PvAk2SDzH4uIJ7pamFkbHBBmZpbkU0xmZpbkgDAzsyQHhJmZJTkgzMwsyQFhZmZJ/x9ejDKqMfS+QAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Add a title to your plot.\n",
+    "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) # D for diamond\n",
+    "ax.set_title(\"Tree Age vs Height\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "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": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What is the correlation between our DataFrame columns?\n",
+    "corr_df = trees_df.corr()\n",
+    "corr_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7974683544303798"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# What is the correlation between age and height (don't use .iloc)\n",
+    "# Using index in this case isn't considered as hardcoding\n",
+    "corr_df['age']['height']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variating Stylistic Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age', ylabel='height'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Option 1:\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\",  marker = \"H\", s = \"diameter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age', ylabel='height'>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Option 2:\n",
+    "# this way allows you to make it bigger\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use subplots to group scatterplot data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Re-visit the Iris Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>sep-length</th>\n",
+       "      <th>sep-width</th>\n",
+       "      <th>pet-length</th>\n",
+       "      <th>pet-width</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sep-length  sep-width  pet-length  pet-width           class\n",
+       "0           5.1        3.5         1.4        0.2     Iris-setosa\n",
+       "1           4.9        3.0         1.4        0.2     Iris-setosa\n",
+       "2           4.7        3.2         1.3        0.2     Iris-setosa\n",
+       "3           4.6        3.1         1.5        0.2     Iris-setosa\n",
+       "4           5.0        3.6         1.4        0.2     Iris-setosa\n",
+       "..          ...        ...         ...        ...             ...\n",
+       "145         6.7        3.0         5.2        2.3  Iris-virginica\n",
+       "146         6.3        2.5         5.0        1.9  Iris-virginica\n",
+       "147         6.5        3.0         5.2        2.0  Iris-virginica\n",
+       "148         6.2        3.4         5.4        2.3  Iris-virginica\n",
+       "149         5.9        3.0         5.1        1.8  Iris-virginica\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_df"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# In Pandas\n",
+    "varietes = list(set(iris_df[\"class\"]))\n",
+    "varietes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# In SQL\n",
+    "varietes = list(pd.read_sql(\"\"\"\n",
+    "    SELECT DISTINCT class\n",
+    "    FROM iris\n",
+    "\"\"\", iris_conn)[\"class\"])\n",
+    "varietes"
+   ]
+  },
+  {
+   "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": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If you want to continue using SQL instead, don't close the connection!\n",
+    "iris_conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEMCAYAAADeYiHoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcgklEQVR4nO3dfXRc9X3n8fd37EESyIAie0ViWTFEQGOK8KZqAnUgPCRADqkhVdImwTEkS2j2hD6QpjZhHSBZ8qSloScPJSUpS4BsQk9E7YaUR/MQKKRE5thiMQW8GJAMVowiGYtIYsx89487hpG4kubKM3Nn5n5e58zxzG/uzHx/urY/+t3fnfszd0dERGSqVNwFiIhIZVJAiIhIKAWEiIiEUkCIiEgoBYSIiISaH3cBxbJw4UJfunRp3GWIiFSVTZs2veTui8Keq5mAWLp0Kb29vXGXISJSVczsueme0yEmEREJpYAQEZFQCggREQmlgBARkVAKCBERCaWASKCh0Qm29I8wNDoRdylSQtrPsr9q5jRXKcyGzTtY29NHOpUik83S3dXByuWL4y5Likz7WYpBI4gEGRqdYG1PH+OZLHsm9jKeybKmp0+/YdYY7WcpFgVEggwMj5FOTd7l6VSKgeGxmCqSUtB+lmJRQCRIa1MDmWx2Ulsmm6W1qSGmiqQUtJ+lWBQQCdLcWEd3Vwf16RQL6uZTn07R3dVBc2Nd3KVJEWk/S7FYrSw52tnZ6boWU2GGRicYGB6jtalB/2nUMO1nKYSZbXL3zrDndBZTAjU31uk/jATQfpb9pUNMIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhyhYQZnaRmfWa2YSZXT/DdmZmV5rZDjPbbWb3mdkx5apTREQC5RxBvABcCVw3y3YfBT4NnAi8BXgYuLG0pYmIyFRlCwh3v8Xd1wNDs2x6OPCguz/j7q8BNwHLSl2fiIhMVolzED8F2s3sKDNLA+cBt4dtaGYX5g5b9e7atausRYqI1LpKXHL0ReAB4EngNaAfODVsQ3e/FrgWgjWpy1WgiEgSVOII4nLgD4ElQD3wZeAeMzsw1qpERBKmEgPiOOBmdx9w973ufj3QhOYhRETKqpynuc43s3pgHjDPzOrNLOwQ16+Bj5pZi5mlzOyTQBrYVq5aRUSkvCOIdcAYcAmwKnd/nZm1mdmombXltvsmsAXYDIwAFwNd7j5SxlpFRBLP3Gtjbrezs9N7e3vjLkNEpKqY2SZ37wx7rhLnIEREpAIoIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAiKBhkYn2NI/wtDoRNyllI36nAxJ7HMpVeKSo1JCGzbvYG1PH+lUikw2S3dXByuXL467rJJSn9VnmRuNIBJkaHSCtT19jGey7JnYy3gmy5qevpr+bUt9Vp9l7hQQCTIwPEY6NXmXp1MpBobHYqqo9NTngPosc6GASJDWpgYy2eyktkw2S2tTQ0wVlZ76HFCfZS4UEAnS3FhHd1cH9ekUC+rmU59O0d3VQXNjXdyllYz6rD7L3GnJ0QQaGp1gYHiM1qaGxPwDUp/VZwk305KjOospgZob6xL3j0d9ToYk9rmUdIhJRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCRU2QLCzC4ys14zmzCz62fZ9ggzu9XM9pjZS2bWXaYyE2H9o/1c8KNfs/7R/rhLkRIaGp1gS/+I1mWucaXcz+VcD+IF4ErgDGDadQDN7ADgLuB7wJ8BrwFHlaPAJDj+a3ex8+VXAbj7id/wzdv/k4cv/UDMVUmxbdi8g7U9faRTKTLZLN1dHaxcvjjusqTISr2fyzaCcPdb3H09MDTLpucDL7j7t9z9FXcfd/e+kheYAOsf7X89HPZ58eVXNZKoMUOjE6zt6WM8k2XPxF7GM1nW9PRpJFFjyrGfK3EO4njgWTO7LXd46T4zOzZsQzO7MHfYqnfXrl1lLrP63PrYzkjtUp0GhsdIpyb/006nUgwMj8VUkZRCOfZzJQZEK/Ax4NvA24BfABtyh54mcfdr3b3T3TsXLVpU5jKrz4eOPSxSu1Sn1qYGMtnspLZMNktr07RHdqUKlWM/V2JAjAEPuvtt7v4qcBXQDLwz3rKq3znvWsJbD56cs289+ADOedeSmCqSUmhurKO7q4P6dIoFdfOpT6fo7urQWs01phz7uZyT1IXqA1bEXUStevjSD7D+0X5ufWwnHzr2MIVDjVq5fDEr2hcyMDxGa1ODwqFGlXo/ly0gzGx+7vPmAfPMrB7Y6+57p2x6E/A3ZvZ+4F7gL4GXgCfKVWutO+ddSxQMCdDcWKdgSIBS7udyHmJaR3D46BJgVe7+OjNrM7NRM2sDcPcnc89/HxgGzgZW5g43iYhImZi7F76x2enAcqAxv93dLytuWdF1dnZ6b29v3GWIiFQVM9vk7p1hzxV8iMnMvgv8KcFhn9/lPVV4woiISNWIMgfxcWC5u+tbVSIiCRBlDmIIGClRHSIiUmFmHEGY2RF5D/8O+LGZfR0YzN/O3Z8pQW0iIhKj2Q4xbSOYY7C8tg9N2cYJTl0VEZEaMmNAuHslftNaRETKoOAAMLNvT9P+90WrRkREKkaUEcL507R/sgh1iIhIhZn1NFcz+/S+bfPu73MEwWUwRESkxhTyPYh9I4QDmDxacIKzmc4rdlEiIhK/WQPC3U8BMLMr3X1d6UsSEZFKEOWb1JeZ2ZvmLNw9G7axiIhUtyiT1HuBzNSbmU2Y2XYz+zsza5zxHSrQxq07WfuzLWzcmpxlN296aDsf/f5D3PTQ9rhLKZuh0Qm29I8kal3m3u1DfOvOJ+ndPtsy8CLhCr6aq5l9DjgH+AbQD7QBawiWBH0SuBx43N0vKEmls5jL1VxPv/o+nhp85fXHR7ccxB0Xn1zcwirMcVfczu7x115/fEj9PLZccWaMFZXehs07WNvTRzqVIpPN0t3Vwcrli+Muq6RW/fBXPLjtjWA4sb2ZGy84PsaKpFLNdDXXKCOIzwMfcfeN7v6Uu99NcHXXv3T324GP8OZvWVesjVt3TgoHgCcHX6npkcRND22fFA4Au8dfq+mRxNDoBGt7+hjPZNkzsZfxTJY1PX01PZLo3T40KRwAHtg2pJGERBYlIA4GDpzSdiBwSO7+TqBqVkW/c+tgpPZasKHvxUjttWBgeIx0avJf83QqxcDwWEwVld4vnw4/83y6dpHpRAmIG4C7zOwzZnammV0A3AH8KPf86QSHmqrC6ctaIrXXgrM73hqpvRa0NjWQyU4+jyKTzdLaVDW/y0R20pELI7WLTCdKQPwt8F3gY8DVwCeA7xHMQ0CwkND7ilpdCZ227DCObjloUtvRLQdx2rLDYqqo9Fb90eEcUj/5uoqH1M9j1R8dHlNFpdfcWEd3Vwf16RQL6uZTn07R3dVR02s1dx7ezIntzZPaTmxvpvPw5mleIRIu0pKjlWyuS45u3LqTO7cOcvqylpoOh3w3PbSdDX0vcnbHW2s6HPINjU4wMDxGa1NDTYdDvt7tQ/zy6Zc46ciFCgeZ1kyT1FqTWkQkwbQmtYiIRKY1qUVEJJTWpBYRkVBRRhBak1pEJEGiBMQ1uT+1JrWISAIUHBBan1pEJFki/6dvZkvMTFf9EhGpcQUHhJm1mdm/A/8J3J1r+4iZ/bBUxYmISHyijCD+keDS3gsI1oIAuAv4QLGLEhGR+EWZpH43cJa7Z83MAdx9t5kdMsvrRESkCkUZQQwC7fkNZrYMeL6oFYmISEWIEhBXAbea2aeA+Wb2ceBm4JslqUxERGIV5TTX68zst8CFBEuOngd8yd3Xl6g2ERGJUZQ5CHJhsL4klYiISEWZMSDM7NOFvIm7XzfbNmZ2EXA+cCzwE3c/v4DX3AOcAqTdfW8htUSVxHUCrr7jCTb07eTsjsO4+Ix3xl1OWWhtBJHoZlwPwszuLeA93N1PnfWDzP4EyAJnAA2zBYSZnQv8OXAiBQTEXNaD2LB5B2t7+kinUmSyWbq7Oli5fHGk96g2R37xF2Tydnna4OmvnxVfQWWw6oe/4sFtQ68/PrG9mRsv0Hc9RWA/1oNw91OKVYS735IrphNonWnb3KmzlwOrgYeLVUO+odEJ1vb0MZ7JMk6wZvGanj5WtC+s2ZHE1Xc8MSkcADIetNfqSKJ3+9CkcAB4YNsQvduHNJIQmcWcrq9kZpcUu5ApvkZwccCds9RxoZn1mlnvrl27In3AwPAY6dTk7qdTKQaGx6LWWjU29IX/OKdrrwW/fPqlSO0i8oa5XoDv0qJWkSc3wlgBfGe2bd39WnfvdPfORYsWRfqc1qYGMtnspLZMNktrU0Ok96kmZ3eEr7k9XXstOOnIhZHaReQNcw0IK2oV+97ULAX8A/BXpZqU3qe5sY7urg7q0ykW1M2nPp2iu6ujZg8vAVx8xjtJT9lzaaNmDy8BdB7ezIntkw8lndjerMNLIgWIdJprnpuKWsUbDgY6gZvNDN5YZ2LAzD7q7g8U88NWLl/MivaFiTqL6emvn5W4s5huvOB4ncUkMgcznsU0aUOzL7j7VSHtn3f3bxXw+vkEgXQ5wST1Z4C9+SMFC1KhJe9lS4BHctvvcvdXp3v/uZzFJCKSdDOdxRTlENNl07SvK/D164Ax4BJgVe7+utxlxEfNrM0DO/fdgH0zz4MzhYOIiBTfrIeYzGzfdxzmmdkpTJ5/OALYU8gHufsVwBXTPN04zWuepUTzHSIiMrNC5iD+KfdnPZD/jWknuMLrXxS7KBERid+sAeHuhwOY2Q3uvrr0JYmISCUoeA7C3VebWdrMTjSzPwMws4PM7KDSlSciInGJsib1scBTwA9447DT+5h82ElERGpElLOYrgEuc/ff4401qe8H3lv0qkREJHZRAuIY3viC3L41qV8BavfaFCIiCRYlIJ4F/iC/wczeDWwrZkEiIlIZolxq40vAL8zs+0CdmX0R+O/ABSWpTEREYhXlLKZbgTOBRcC9QBvwYXe/s0S1iYhIjAoeQZjZAcCHgdOBtwE7gJfM7HF3Hy9RfSIiEpMoh5iuAY4m+Ob0cwQjiEuBxUBBa1eLiEj1iBIQ5wDvcPeR3OOtZvYIwSS1AkJEpMZEOYtpJ3DglLYG4MXilSPlsHHrTtb+bAsbt9buUqNTbRvcw896+9k2WNC1JWvC0OgEW/pHGBqdiLsUqVJRRhA3Areb2XeAAYK1Gj4H3JB3xVfc/Z7ilijFdPrV9/HU4CsA3Nw7wNEtB3HHxSfHWlOpXbb+MW741fOvP159QhtfOfvYGCsqvQ2bd7C2p490KkUmm6W7q4OVyxfHXZZUmSgB8ee5P6euR/3Z3A2CL9Adsb9FSWls3Lrz9XDY58nBV9i4dSenLavNdam3De6ZFA4ANzz8PKuPX0p7y4KYqiqtodEJ1vb0MZ7JMk6w7vqanj5WtC9MxKqJUjwFB8S+q7pK9bpz6+C07bUaEJv7R6Ztr9WAGBgeI51KvR4OAOlUioHhMQWERBJlDkKq3OnLWiK114LlSw6N1F4LWpsayGSzk9oy2SytTboqjkSjgEiQ05YdxtEtk6/OfnTLQTU7egBob1nA6hPaJrWtPqGtZkcPAM2NdXR3dVCfTrGgbj716RTdXR0aPUhk5u5x11AUnZ2d3tvbG3cZVWHj1p3cuXWQ05e11HQ45Ns2uIfN/SMsX3JoTYdDvqHRCQaGx2htalA4yLTMbJO7d4Y+p4AQEUmumQJCh5hERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREKVLSDM7CIz6zWzCTO7fobtzjOzTWb2spkNmFm3mUVZO1tmsW1wDz/r7Wfb4J64SxGRClbO/3hfAK4EzgBmWvvwQOCvgf8AFgH/CnwB+EaJ60uEy9Y/xg2/ev71x6tPaOMrZx8bY0UiUqnKNoJw91vcfT0wNMt217j7A+7+qrvvAH4MrChHjbVu2+CeSeEAcMPDz2skISKhqmEO4iTg8bAnzOzC3GGr3l27dpW5rOqzuX8kUruIJFtFB4SZfQroBK4Ke97dr3X3TnfvXLRoUXmLq0LLlxwaqV1Ekq1iA8LMziGYd/igu78Uczk1ob1lAatPaJvUtvqENtpbFsRUkYhUsoo8O8jMzgR+AJzl7o/FXU8t+crZx7L6+KVs7h9h+ZJDFQ4iMq2yBUTuVNX5wDxgnpnVA3vdfe+U7U4lmJj+sLs/Uq76kqS9ZYGCQURmVc5DTOuAMeASYFXu/jozazOzUTPbd+zjS8AhwL/l2kfN7LYy1ikiIpRxBOHuVwBXTPN0Y952p5SjHhERmVnFTlKLiEi8FBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBISIioRQQIiISKvEBMTQ6wZb+EYZGJ+IuRUSkolTkgkHlsmHzDtb29JFOpchks3R3dbBy+eK4yxIRqQiJHUEMjU6wtqeP8UyWPRN7Gc9kWdPTp5GEiEhOYgNiYHiMdGpy99OpFAPDYzFVJCJSWRIbEK1NDWSy2UltmWyW1qaGmCoSEaksiQ2I5sY6urs6qE+nWFA3n/p0iu6uDpob6+IuTUSkIiR6knrl8sWsaF/IwPAYrU0NCgcRkTyJDggIRhIKBhGRN0vsISYREZmZAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREIpIEREJJQCQkREQikgREQklAJCRERCKSBERCSUAkJEREKVLSDM7CIz6zWzCTO7fpZtLzaznWa228yuMzNdj7uIhkYn2NI/ovW3RWRG5VwP4gXgSuAMYNp1Pc3sDOAS4NTca/4F+HKuTfbThs07WNvTRzqVIpPN0t3Vwcrli+MuS0QqUNlGEO5+i7uvB4Zm2fQ84J/c/XF3Hwb+J3B+ictLhKHRCdb29DGeybJnYi/jmSxrevo0khCRUJU4B3EMsCXv8Ragxcyap25oZhfmDlv17tq1q2wFVquB4THSqcm7PJ1KMTA8FlNFIlLJKjEgGoHdeY/33V8wdUN3v9bdO929c9GiRWUprpq1NjWQyWYntWWyWVqbpj3iJyIJVokBMQocnPd43/09MdRSU5ob6+ju6qA+nWJB3Xzq0ym6uzq0JreIhCrnJHWhHgeOA/459/g4YNDdZ5u7kAKsXL6YFe0LGRgeo7WpQeEgItMqW0CY2fzc580D5plZPbDX3fdO2fQG4Hoz+zHwIrAOuL5cdSZBc2OdgkFEZlXOQ0zrgDGC01VX5e6vM7M2Mxs1szYAd78d6AbuBZ7L3S4vY50iIgKYu8ddQ1F0dnZ6b29v3GWIiFQVM9vk7p1hz1XiJLWIiFQABYSIiIRSQIiISKiamYMws10EE9pzsRB4qYjlVAP1ORnU52TYnz6/3d1Dv2lcMwGxP8ysd7pJmlqlPieD+pwMpeqzDjGJiEgoBYSIiIRSQASujbuAGKjPyaA+J0NJ+qw5CBERCaURhIiIhFJAiIhIKAWEiIiESkxAmNlbzOxfzOwVM3vOzD4xzXa/b2Z3mNlLZlbVEzQR+nyemW0ys5fNbMDMunOXZ68qEfr7MTN70sx2m9lvzOxHZnZw2LaVrtA+T3nNPWbm1biPIdJ+Pt/MXstdLXrf7eTyVlscUfazmR1hZrea2Z7c/2Pdc/3cxAQE8D3gVaAFOBe4xsyOCdkuQ7BY0X8rY22lUmifDwT+muDbmO8BTgO+UKYai6nQ/v47sMLdDwGOIFin5MqyVVlchfYZADM7l8pcKCyKKH1+2N0b8273lavIIiuoz2Z2AHAXcA9wGNAK3DTnT3X3mr8BB+V+uEfltd0IfGOG17QHP5746y9Xn/O2+zzw87j7UI7+EqyBfgPwb3H3odR9Bg4BngKOBxyYH3cfStln4HzgwbhrLnOfLwQeKNZnJ2UEcRTwmrs/lde2BZj2N60asD99Polg6ddqEqm/ZvZeM9tNsNZ5F/D3Ja+w+KLu468B1wA7S11YCUXt83/NHWZ5ysy+VKWH1aL0+XjgWTO7Ldfv+8zs2Ll+cFICohHYPaVtN7AghlrKZU59NrNPAZ3AVSWqq1Qi9dfdH/TgEFMr8L+AZ0taXWkU3Gcz6wRWAN8pQ12lFGU//xL4feC/EPwS8HHgb0taXWlE6XMr8DHg28DbgF8AG3KHniJLSkCMAlMnIQ8m+O2xVkXus5mdA3wD+KC7V9vVMOe0j919B3A78NMS1VVKBfXZzFLAPwB/5W9eA77aFLyf3f0Zd9/u7ll3fwz4CvCRMtRYbFH+bo8RHFa7zd1fJfhFrxl451w+OCkB8RQw38yOzGs7juo7jBJFpD6b2ZnAD4A/zv1jqjb7s4/nA+8oSVWlVWifDyYYFd5sZjuBX+faB8zsxNKXWVT7s58dsJJUVVpR+txH0M/iiHsCpowTPT8FfkIw4bOCYIh2TMh2BtQDy3I/6HqgLu76S9znU4Eh4KS4ay5Tf88F2nL7+u3A/cAtcddfqj7n+nlY3u0Pc3+3FwMHxN2HEu7nDwItufu/B/xf4PK46y9xn48Gfge8H5gHXAz8v7nu59g7XsYf8FuA9cArwPPAJ3LtbQRDuLbc46W5fzz5t2fjrr/Efb4X2Jtr23e7Le76S9jfrwIDue0GCC501hx3/aXs85TX7Ps7XnVnMUXcz1cBg7ntniE4xJSOu/5S72fgT4BtwMvAfWFBUuhNF+sTEZFQSZmDEBGRiBQQIiISSgEhIiKhFBAiIhJKASEiIqEUECIiEkoBIRITM3t8uvUJzOxkMxuY4bVLq3lNB6kOCgiRCMzsWTN7fzHey92P8QLXJyjm54oUSgEhIiKhFBCSSLnfyL9oZlvNbNjM/reZ1eee+5CZbTazETN7yMw6cu03Elza4Oe55SvXhLzvKWb2WN7ju83skbzHD+aumjtpVGBmDWZ2fa6WrQTXS9r3mpk+91wzez537f//UcQfkUjVLz0osj/OBc4guL7Nz4F1ZnYLcB3wx0AvsAr4VzM72t0/mbv66QXufvc07/kw0G5mC4ERgvUIsma2gOB6V38APBDyussJrij7DoILst2274mwzzWzpbmn30twgbajgEfM7BZ3f2IuPwyRqTSCkCT7rrv3u/tvCS7g93HgM8A/uvt/uPtr7v4jYIJgpa5Zufs4QbCcRHCJ7T7gQYIrcB4PPO3uQyEv/VPgq+7+W3fvJ1jwpRBfdvcxd99CsMrYcQW+TmRWCghJsv68+88RrMD1duBvcoeXRsxsBFiSe+5NzOz7ucM+o2Z2aa75fuBkgpC4n+CKmu/L3e6fppa3hdRTiPzlQ39HsPqYSFEoICTJluTdbwNeIPhP+qvufmje7UB3/0luu0mXP3b3z7p7Y+72tVzz1IC4n9kD4sWQeiZ9VLSuiew/BYQk2efMrNXM3gJcCtxMsKreZ83sPRY4yMzOys0hQLC+wBGzvO9DBPMC7wYecffHCUYm7yFYJznMPwNfNLMmM2sF/mLK84V8rkhRKSAkyf4PcCfBYjLPAFe6ey/BPMR3gWGChVfOz3vN1wkms0fM7Athb+rurwCPAo97sC4wBJPXz7n7b6ap5csEh5W252q6ccrzs36uSLFpwSBJJDN7lpnPRhJJPI0gREQklAJCRERC6RCTiIiE0ghCRERCKSBERCSUAkJEREIpIEREJJQCQkREQv1/QIc+HEFCbUIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n",
+    "iris_df[iris_df[\"class\"] == 'Iris-setosa'].plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Write a for loop that iterates through each variety in classes\n",
+    "# and makes a plot for only that class\n",
+    "\n",
+    "# For each class add a color and a marker style\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "\n",
+    "for i in range(len(varietes)):\n",
+    "    variety = varietes[i]\n",
+    "    \n",
+    "    # make a df just of just the data for this variety\n",
+    "    variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+    "    \n",
+    "    #make a scatter plot for this variety\n",
+    "    variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
+    "                            label = variety, color = colors[i], marker = markers[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Did you notice that it made 3 plots?!?! What's decieving about this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We can make Subplots in plots, called an AxesSubplot, keyword ax\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# complete this code to make 3 plots in one\n",
+    "\n",
+    "plot_area = None   # don't change this...look at this variable in line 12\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "for i in range(len(varietes)):\n",
+    "    variety = varietes[i]\n",
+    "    \n",
+    "    # make a df just of just the data for this variety\n",
+    "    variety_df = iris_df[iris_df[\"class\"] == variety] \n",
+    "    \n",
+    "    #make a scatter plot for this variety\n",
+    "    plot_area = variety_df.plot.scatter(x = \"pet-width\", y = \"pet-length\", \\\n",
+    "                                        label = variety, color = colors[i], marker = markers[i], \\\n",
+    "                                        ax = plot_area)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's focus on \"Iris-virginica\" data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sep-length</th>\n",
+       "      <th>sep-width</th>\n",
+       "      <th>pet-length</th>\n",
+       "      <th>pet-width</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>7.1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>Iris-virginica</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sep-length  sep-width  pet-length  pet-width           class\n",
+       "100         6.3        3.3         6.0        2.5  Iris-virginica\n",
+       "101         5.8        2.7         5.1        1.9  Iris-virginica\n",
+       "102         7.1        3.0         5.9        2.1  Iris-virginica\n",
+       "103         6.3        2.9         5.6        1.8  Iris-virginica\n",
+       "104         6.5        3.0         5.8        2.2  Iris-virginica"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_virginica = iris_df[iris_df[\"class\"] == \"Iris-virginica\"]\n",
+    "assert(len(iris_virginica) == 50)\n",
+    "iris_virginica.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's learn about *xlim* and *ylim*\n",
+    "- Allows us to set x-axis and y-axis limits\n",
+    "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
+    "- You need to be careful about setting the UPPER-BOUND"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='pet-width', ylabel='pet-length'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = 0, ylim = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                    xlim = (0, 6), ylim = (0, 6),\n",
+    "                    figsize = (3, 3))\n",
+    "\n",
+    "# What is wrong with this plot?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the maximum pet-len?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.9"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_virginica[\"pet-length\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 6.0)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ax.get_ylim()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's include assert statements to make sure we don't crop the plot!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/k6/kcy8b4f57hx9f1wh4sbs8mn40000gn/T/ipykernel_26032/2630924870.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m                      \u001b[0mxlim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m                      figsize = (3, 3))\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0miris_virginica\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pet-length\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 6), ylim = (0, 6),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's try all 4 assert statements\n",
+    "\n",
+    "```\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 7), ylim = (0, 7),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time-Permitting\n",
+    "Plot this data in an interesting/meaningful way & identify any correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>grade</th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>attendance</th>\n",
+       "      <th>height</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Cole</td>\n",
+       "      <td>C</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cynthia</td>\n",
+       "      <td>AB</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alice</td>\n",
+       "      <td>B</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Seth</td>\n",
+       "      <td>BC</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      name grade  gpa  attendance  height\n",
+       "0     Cole     C  2.0           4      68\n",
+       "1  Cynthia    AB  3.5          11      66\n",
+       "2    Alice     B  3.0          10      60\n",
+       "3     Seth    BC  2.5           6      72"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "students = pd.DataFrame({\n",
+    "    \"name\": [\n",
+    "        \"Cole\",\n",
+    "        \"Cynthia\",\n",
+    "        \"Alice\",\n",
+    "        \"Seth\"\n",
+    "    ],\n",
+    "    \"grade\": [\n",
+    "        \"C\",\n",
+    "        \"AB\",\n",
+    "        \"B\",\n",
+    "        \"BC\"\n",
+    "    ],\n",
+    "    \"gpa\": [\n",
+    "        2.0,\n",
+    "        3.5,\n",
+    "        3.0,\n",
+    "        2.5\n",
+    "    ],\n",
+    "    \"attendance\": [\n",
+    "        4,\n",
+    "        11,\n",
+    "        10,\n",
+    "        6\n",
+    "    ],\n",
+    "    \"height\": [\n",
+    "        68,\n",
+    "        66,\n",
+    "        60,\n",
+    "        72\n",
+    "    ]\n",
+    "})\n",
+    "students"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0.3333333333333333\n",
+       "1                  0.25\n",
+       "2                   0.0\n",
+       "3                   0.5\n",
+       "Name: height, dtype: string"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Min, Max, and Overall Difference in Student Height\n",
+    "min_height = students[\"height\"].min()\n",
+    "max_height = students[\"height\"].max()\n",
+    "diff_height = max_height - min_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+    "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+    "height_colors = height_colors / 2 \n",
+    "\n",
+    "# Color must be a string (e.g. c='0.34')\n",
+    "height_colors = height_colors.astype(\"string\")\n",
+    "\n",
+    "height_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='attendance', ylabel='gpa'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>gpa</th>\n",
+       "      <th>attendance</th>\n",
+       "      <th>height</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>gpa</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.976831</td>\n",
+       "      <td>-0.464758</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>attendance</th>\n",
+       "      <td>0.976831</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.635586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>height</th>\n",
+       "      <td>-0.464758</td>\n",
+       "      <td>-0.635586</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 gpa  attendance    height\n",
+       "gpa         1.000000    0.976831 -0.464758\n",
+       "attendance  0.976831    1.000000 -0.635586\n",
+       "height     -0.464758   -0.635586  1.000000"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "students.corr()"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec1.ipynb b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec1.ipynb
new file mode 100644
index 0000000..a02b77b
--- /dev/null
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@@ -0,0 +1,857 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Announcements - Friday, December 1\n",
+    "\n",
+    "* Download ALL files for today's lecture\n",
+    "* <b>If you have any problem with P7 or P8 grades, please send me (Gurmail.Singh@wisc.edu) an email by December 7.</b>\n",
+    "* Quiz 9 is due tonight at 11:59 pm\n",
+    "* Late days may not be used on P13\n",
+    "* If you have questions, it is almost always faster to \n",
+    "  * Post on Piazza\n",
+    "  * Go to [office hours](https://sites.google.com/wisc.edu/cs220-oh-f23/home?pli=1) \n",
+    "### Conflict Form\n",
+    "  * [Final - December 19, 7:45 am](https://cs220.cs.wisc.edu/f23/surveys.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting 2 - Scatter Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "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",
+    "# new import statement\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import requests\n",
+    "matplotlib.rcParams[\"font.size\"] = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Wrapping up bus dataset example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What are the top routes, and how many people ride them daily?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"bus.db\"\n",
+    "# assert existence of path\n",
+    "assert os.path.exists(path)\n",
+    "\n",
+    "# establish connection to bus.db\n",
+    "conn = sqlite3.connect(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_sql(\"\"\"\n",
+    "SELECT Route, SUM(DailyBoardings) AS daily\n",
+    "FROM boarding\n",
+    "GROUP BY Route\n",
+    "ORDER BY daily DESC\n",
+    "\"\"\", conn)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's extract daily column from df\n",
+    "df[\"daily\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a bar plot from daily column Series\n",
+    "df[\"daily\"].plot.bar()\n",
+    "\n",
+    "# Oops wrong x-axis labels!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = ???\n",
+    "\n",
+    "# let's plot for top 5 routes alone\n",
+    "???"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's use slicing to aggregate the rest of the data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's plot the bars\n",
+    "ax = (s / 1000).plot.bar(color = \"k\")\n",
+    "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 1:  Downloading IRIS dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use requests to get this URL\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+    "response = ???\n",
+    "\n",
+    "# check that the request was successful\n",
+    "???\n",
+    "\n",
+    "# open a file called \"iris.csv\" for writing the data locally\n",
+    "file_obj = open(\"iris.csv\", ???)\n",
+    "\n",
+    "# write the text of response to the file object\n",
+    "file_obj.write(???)\n",
+    "\n",
+    "# close the file object\n",
+    "file_obj.close()\n",
+    "\n",
+    "# Look at the file you downloaded. What's wrong with it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 2: Making a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read the \"iris.csv\" file into a Pandas dataframe\n",
+    "iris_df = ???\n",
+    "\n",
+    "# display the head of the data frame\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 3: Our CSV file has no header. Let's add column names.\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": [
+    "# Attribute Information:\n",
+    "# 1. sepal length in cm\n",
+    "# 2. sepal width in cm\n",
+    "# 3. petal length in cm\n",
+    "# 4. petal width in cm\n",
+    "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+    "\n",
+    "# These should be our headers \n",
+    "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
+    "\n",
+    "iris_df = pd.read_csv(\"iris.csv\",\n",
+    "                 ???)\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 4: Connect to our database version of this data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
+    "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
+    "Break any ties by ordering by the shortest sepal width."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_sql(\"\"\"\n",
+    "    SELECT\n",
+    "    FROM\n",
+    "    WHERE \n",
+    "    ORDER BY \n",
+    "    LIMIT 10\n",
+    "\"\"\", iris_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": 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\", \\\n",
+    "                     color = \"red\", marker = \"*\", s = 50)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the trees data comparing a tree's age to its height...\n",
+    " - What is `df_name`?\n",
+    " - What is `x_col_name`?\n",
+    " - What is `y_col_name`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: change y to diameter"
+   ]
+  },
+  {
+   "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": [
+    "# Plot with some more beautification options.\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\",  marker = \"D\", s = 50) \n",
+    "# D for diamond"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a title to your plot.\n",
+    "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) \n",
+    "# D for diamond\n",
+    "ax.set_title(\"Tree Age vs Height\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the correlation between our DataFrame columns?\n",
+    "corr_df = trees_df.corr()\n",
+    "corr_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the correlation between age and height (don't use .iloc)\n",
+    "# Using index in this case isn't considered as hardcoding\n",
+    "corr_df['age']['height']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variating Stylistic Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1:\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\",  marker = \"H\", s = \"diameter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 2:\n",
+    "# this way allows you to make it bigger\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use subplots to group scatterplot data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Re-visit the Iris Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_df"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In Pandas\n",
+    "varietes = list(set(iris_df[\"class\"]))\n",
+    "varietes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In SQL\n",
+    "varietes = list(pd.read_sql(\"\"\"\n",
+    "    SELECT DISTINCT class\n",
+    "    FROM iris\n",
+    "\"\"\", iris_conn)[\"class\"])\n",
+    "varietes"
+   ]
+  },
+  {
+   "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",
+    "iris_conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a for loop that iterates through each variety in classes\n",
+    "# and makes a plot for only that class\n",
+    "\n",
+    "# For each class add a color and a marker style\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "\n",
+    "for i in range(len(varietes)):\n",
+    "    ???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Did you notice that it made 3 plots?!?! What's decieving about this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We can make Subplots in plots, called an AxesSubplot, keyword ax\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# complete this code to make 3 plots in one\n",
+    "\n",
+    "plot_area = None   # don't change this...look at this variable in line 12\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "for i in range(len(varietes)):\n",
+    "    ???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's focus on \"Iris-virginica\" data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica = ???\n",
+    "assert(len(iris_virginica) == 50)\n",
+    "iris_virginica.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's learn about *xlim* and *ylim*\n",
+    "- Allows us to set x-axis and y-axis limits\n",
+    "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
+    "- You need to be careful about setting the UPPER-BOUND"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = ???, ylim = ???)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                    xlim = (0, 6), ylim = (0, 6),\n",
+    "                    figsize = (3, 3))\n",
+    "\n",
+    "# What is wrong with this plot?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the maximum pet-len?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.get_ylim()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's include assert statements to make sure we don't crop the plot!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 6), ylim = (0, 6),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's try all 4 assert statements\n",
+    "\n",
+    "```\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 7), ylim = (0, 7),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time-Permitting\n",
+    "Plot this data in an interesting/meaningful way & identify any correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students = pd.DataFrame({\n",
+    "    \"name\": [\n",
+    "        \"Cole\",\n",
+    "        \"Cynthia\",\n",
+    "        \"Alice\",\n",
+    "        \"Seth\"\n",
+    "    ],\n",
+    "    \"grade\": [\n",
+    "        \"C\",\n",
+    "        \"AB\",\n",
+    "        \"B\",\n",
+    "        \"BC\"\n",
+    "    ],\n",
+    "    \"gpa\": [\n",
+    "        2.0,\n",
+    "        3.5,\n",
+    "        3.0,\n",
+    "        2.5\n",
+    "    ],\n",
+    "    \"attendance\": [\n",
+    "        4,\n",
+    "        11,\n",
+    "        10,\n",
+    "        6\n",
+    "    ],\n",
+    "    \"height\": [\n",
+    "        68,\n",
+    "        66,\n",
+    "        60,\n",
+    "        72\n",
+    "    ]\n",
+    "})\n",
+    "students"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Min, Max, and Overall Difference in Student Height\n",
+    "min_height = students[\"height\"].min()\n",
+    "max_height = students[\"height\"].max()\n",
+    "diff_height = max_height - min_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+    "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+    "height_colors = height_colors / 2 \n",
+    "\n",
+    "# Color must be a string (e.g. c='0.34')\n",
+    "height_colors = height_colors.astype(\"string\")\n",
+    "\n",
+    "height_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students.corr()"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec2.ipynb b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec2.ipynb
new file mode 100644
index 0000000..a02b77b
--- /dev/null
+++ b/f23/Gurmail_Lecture_Notes/35N_Plotting-2/lec_35N_plotting2_scatter_plots_template_Gurmail_lec2.ipynb
@@ -0,0 +1,857 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Announcements - Friday, December 1\n",
+    "\n",
+    "* Download ALL files for today's lecture\n",
+    "* <b>If you have any problem with P7 or P8 grades, please send me (Gurmail.Singh@wisc.edu) an email by December 7.</b>\n",
+    "* Quiz 9 is due tonight at 11:59 pm\n",
+    "* Late days may not be used on P13\n",
+    "* If you have questions, it is almost always faster to \n",
+    "  * Post on Piazza\n",
+    "  * Go to [office hours](https://sites.google.com/wisc.edu/cs220-oh-f23/home?pli=1) \n",
+    "### Conflict Form\n",
+    "  * [Final - December 19, 7:45 am](https://cs220.cs.wisc.edu/f23/surveys.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting 2 - Scatter Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ignore this cell (it's just to make certain text red later, but you don't need to understand it).\n",
+    "from IPython.core.display import display, HTML\n",
+    "display(HTML('<style>em { color: red; }</style> <style>.container { width:100% !important; }</style>'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "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",
+    "# new import statement\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import requests\n",
+    "matplotlib.rcParams[\"font.size\"] = 12"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Wrapping up bus dataset example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What are the top routes, and how many people ride them daily?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = \"bus.db\"\n",
+    "# assert existence of path\n",
+    "assert os.path.exists(path)\n",
+    "\n",
+    "# establish connection to bus.db\n",
+    "conn = sqlite3.connect(path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_sql(\"\"\"\n",
+    "SELECT Route, SUM(DailyBoardings) AS daily\n",
+    "FROM boarding\n",
+    "GROUP BY Route\n",
+    "ORDER BY daily DESC\n",
+    "\"\"\", conn)\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's extract daily column from df\n",
+    "df[\"daily\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's create a bar plot from daily column Series\n",
+    "df[\"daily\"].plot.bar()\n",
+    "\n",
+    "# Oops wrong x-axis labels!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = ???\n",
+    "\n",
+    "# let's plot for top 5 routes alone\n",
+    "???"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's use slicing to aggregate the rest of the data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's plot the bars\n",
+    "ax = (s / 1000).plot.bar(color = \"k\")\n",
+    "ax.set_ylabel(\"Rides / Day (Thousands)\")\n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IRIS dataset: http://archive.ics.uci.edu/ml/datasets/iris\n",
+    "- This set of data is used in beginning Machine Learning Courses\n",
+    "- You can train a ML algorithm to use the values to predict the class of iris\n",
+    "- Dataset link: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 1:  Downloading IRIS dataset (https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use requests to get this URL\n",
+    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\n",
+    "response = ???\n",
+    "\n",
+    "# check that the request was successful\n",
+    "???\n",
+    "\n",
+    "# open a file called \"iris.csv\" for writing the data locally\n",
+    "file_obj = open(\"iris.csv\", ???)\n",
+    "\n",
+    "# write the text of response to the file object\n",
+    "file_obj.write(???)\n",
+    "\n",
+    "# close the file object\n",
+    "file_obj.close()\n",
+    "\n",
+    "# Look at the file you downloaded. What's wrong with it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 2: Making a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read the \"iris.csv\" file into a Pandas dataframe\n",
+    "iris_df = ???\n",
+    "\n",
+    "# display the head of the data frame\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 3: Our CSV file has no header. Let's add column names.\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": [
+    "# Attribute Information:\n",
+    "# 1. sepal length in cm\n",
+    "# 2. sepal width in cm\n",
+    "# 3. petal length in cm\n",
+    "# 4. petal width in cm\n",
+    "# 5. class: Iris Setosa, Iris Versicolour, Iris Virginica\n",
+    "\n",
+    "# These should be our headers \n",
+    "# [\"sep-length\", \"sep-width\", \"pet-length\", \"pet-width\", \"class\"]\n",
+    "\n",
+    "iris_df = pd.read_csv(\"iris.csv\",\n",
+    "                 ???)\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 4: Connect to our database version of this data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_conn = sqlite3.connect(\"iris-flowers.db\")\n",
+    "pd.read_sql(\"SELECT * FROM sqlite_master WHERE type='table'\", iris_conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Warmup 5: Using SQL, get the 10 'Iris-setosa' flowers with the longest sepal length.\n",
+    "Break any ties by ordering by the shortest sepal width."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.read_sql(\"\"\"\n",
+    "    SELECT\n",
+    "    FROM\n",
+    "    WHERE \n",
+    "    ORDER BY \n",
+    "    LIMIT 10\n",
+    "\"\"\", iris_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": 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\", \\\n",
+    "                     color = \"red\", marker = \"*\", s = 50)`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the trees data comparing a tree's age to its height...\n",
+    " - What is `df_name`?\n",
+    " - What is `x_col_name`?\n",
+    " - What is `y_col_name`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: change y to diameter"
+   ]
+  },
+  {
+   "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": [
+    "# Plot with some more beautification options.\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\",  marker = \"D\", s = 50) \n",
+    "# D for diamond"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a title to your plot.\n",
+    "ax = trees_df.plot.scatter(x = \"age\", y = \"height\", color = \"r\", marker = \"D\", s = 50) \n",
+    "# D for diamond\n",
+    "ax.set_title(\"Tree Age vs Height\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Correlation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the correlation between our DataFrame columns?\n",
+    "corr_df = trees_df.corr()\n",
+    "corr_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the correlation between age and height (don't use .iloc)\n",
+    "# Using index in this case isn't considered as hardcoding\n",
+    "corr_df['age']['height']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Variating Stylistic Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1:\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\",  marker = \"H\", s = \"diameter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 2:\n",
+    "# this way allows you to make it bigger\n",
+    "trees_df.plot.scatter(x = \"age\", y = \"height\", marker = \"H\", s = trees_df[\"diameter\"] * 50) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use subplots to group scatterplot data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Re-visit the Iris Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_df"
+   ]
+  },
+  {
+   "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": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In Pandas\n",
+    "varietes = list(set(iris_df[\"class\"]))\n",
+    "varietes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In SQL\n",
+    "varietes = list(pd.read_sql(\"\"\"\n",
+    "    SELECT DISTINCT class\n",
+    "    FROM iris\n",
+    "\"\"\", iris_conn)[\"class\"])\n",
+    "varietes"
+   ]
+  },
+  {
+   "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",
+    "iris_conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Change this scatter plot so that the data is only for class ='Iris-setosa'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write a for loop that iterates through each variety in classes\n",
+    "# and makes a plot for only that class\n",
+    "\n",
+    "# For each class add a color and a marker style\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "\n",
+    "for i in range(len(varietes)):\n",
+    "    ???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Did you notice that it made 3 plots?!?! What's decieving about this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We can make Subplots in plots, called an AxesSubplot, keyword ax\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# complete this code to make 3 plots in one\n",
+    "\n",
+    "plot_area = None   # don't change this...look at this variable in line 12\n",
+    "colors = [\"blue\", \"green\", \"red\"]\n",
+    "markers = [\"o\", \"^\", \"v\"]\n",
+    "for i in range(len(varietes)):\n",
+    "    ???"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's focus on \"Iris-virginica\" data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica = ???\n",
+    "assert(len(iris_virginica) == 50)\n",
+    "iris_virginica.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's learn about *xlim* and *ylim*\n",
+    "- Allows us to set x-axis and y-axis limits\n",
+    "- Takes either a single value (LOWER-BOUND) or a tuple containing two values (LOWER-BOUND, UPPER-BOUND)\n",
+    "- You need to be careful about setting the UPPER-BOUND"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\", xlim = ???, ylim = ???)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                    xlim = (0, 6), ylim = (0, 6),\n",
+    "                    figsize = (3, 3))\n",
+    "\n",
+    "# What is wrong with this plot?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What is the maximum pet-len?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.get_ylim()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's include assert statements to make sure we don't crop the plot!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 6), ylim = (0, 6),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[\"pet-length\"].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now let's try all 4 assert statements\n",
+    "\n",
+    "```\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = iris_virginica.plot.scatter(x = \"pet-width\", y = \"pet-length\",\n",
+    "                     xlim = (0, 7), ylim = (0, 7),\n",
+    "                     figsize = (3, 3))\n",
+    "assert iris_virginica[ax.get_xlabel()].min() >= ax.get_xlim()[0]\n",
+    "assert iris_virginica[ax.get_xlabel()].max() <= ax.get_xlim()[1]\n",
+    "assert iris_virginica[ax.get_ylabel()].min() >= ax.get_ylim()[0]\n",
+    "assert iris_virginica[ax.get_ylabel()].max() <= ax.get_ylim()[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time-Permitting\n",
+    "Plot this data in an interesting/meaningful way & identify any correlations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students = pd.DataFrame({\n",
+    "    \"name\": [\n",
+    "        \"Cole\",\n",
+    "        \"Cynthia\",\n",
+    "        \"Alice\",\n",
+    "        \"Seth\"\n",
+    "    ],\n",
+    "    \"grade\": [\n",
+    "        \"C\",\n",
+    "        \"AB\",\n",
+    "        \"B\",\n",
+    "        \"BC\"\n",
+    "    ],\n",
+    "    \"gpa\": [\n",
+    "        2.0,\n",
+    "        3.5,\n",
+    "        3.0,\n",
+    "        2.5\n",
+    "    ],\n",
+    "    \"attendance\": [\n",
+    "        4,\n",
+    "        11,\n",
+    "        10,\n",
+    "        6\n",
+    "    ],\n",
+    "    \"height\": [\n",
+    "        68,\n",
+    "        66,\n",
+    "        60,\n",
+    "        72\n",
+    "    ]\n",
+    "})\n",
+    "students"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Min, Max, and Overall Difference in Student Height\n",
+    "min_height = students[\"height\"].min()\n",
+    "max_height = students[\"height\"].max()\n",
+    "diff_height = max_height - min_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 1] (black to white)\n",
+    "height_colors = (students[\"height\"] - min_height) / diff_height\n",
+    "\n",
+    "# Normalize students heights on a scale of [0, 0.5] (black to gray)\n",
+    "height_colors = height_colors / 2 \n",
+    "\n",
+    "# Color must be a string (e.g. c='0.34')\n",
+    "height_colors = height_colors.astype(\"string\")\n",
+    "\n",
+    "height_colors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students.plot.scatter(x=\"attendance\", y=\"gpa\", c=height_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "students.corr()"
+   ]
+  }
+ ],
+ "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
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