{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Title :\n",
    "Exercise: Visualization Improvisation\n",
    "\n",
    "## Description :\n",
    "\n",
    "For this exercise we would like you to get creative and experiment!  \n",
    "\n",
    "**You have the freedom to plot anything that you'd like from this data**. You're expected to produce **two plots**, both of which should adhere to the principles learned in lecture (e.g., make it clear to understand/digest, effective, simple, not misleading, etc). \n",
    "\n",
    "Please feel inspired to challenge yourself by making a type of plot you've never made before -- perhaps never even seen before! Give a brief explanation of the reason and usefulness of the plot.\n",
    "\n",
    "Your data is the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston\" target=\"_blank\">Boston housing prices dataset</a>. We will load it directly from sklearn  \n",
    "\n",
    "**Resource**: for tons of great coding examples, visit the <a href=\"https://matplotlib.org/gallery/index.html\" target=\"_blank\">matplotlib</a> website."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <img style=\"float: left; padding-right: 10px; width: 45px\" src=\"https://raw.githubusercontent.com/Harvard-IACS/2018-CS109A/master/content/styles/iacs.png\"> CS109A Introduction to Data Science \n",
    "\n",
    "## Lecture 14, Exercise: Visualization\n",
    "\n",
    "\n",
    "**Harvard University**<br/>\n",
    "**Fall 2020**<br/>\n",
    "**Instructors**: Pavlos Protopapas, Kevin Rader, and Chris Tanner\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  506.000000  \n",
       "mean    12.653063  \n",
       "std      7.141062  \n",
       "min      1.730000  \n",
       "25%      6.950000  \n",
       "50%     11.360000  \n",
       "75%     16.955000  \n",
       "max     37.970000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the boston housing dataset\n",
    "boston = load_boston()\n",
    "boston_pd = pd.DataFrame(boston.data)\n",
    "boston_pd.columns = boston.feature_names\n",
    "boston_pd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# our canonical example\n",
    "plt.figure(figsize=(5, 4))\n",
    "plt.hist(boston.target)\n",
    "plt.title('Boston Housing Prices')\n",
    "plt.xlabel('Price ($1000)')\n",
    "plt.ylabel('# of Houses')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR FIRST PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR SECOND PLOT"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}