Group Project Brent (Matt).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from io import StringIO\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "school_data = \"INSchoolDataCopy.csv\"\n",
    "test_score_data = \"SchoolCorpDataRefined.csv\"\n",
    "income_data = \"IncomeUpdateRefined.csv\"\n",
    "\n",
    "# Read School and Student Data File and store into Pandas Data Frames\n",
    "school_data = pd.read_csv(school_data)\n",
    "test_score_data = pd.read_csv(test_score_data)\n",
    "income_data = pd.read_csv(income_data)\n",
    "# Combine the data into a single dataset\n",
    "school_complete = pd.merge(school_data, test_score_data, how=\"left\", on=[\"ID\", \"ID\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Mailing Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Overall Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5265.0</td>\n",
       "      <td>ALEXANDRIA COM SCHOOL CORP</td>\n",
       "      <td>202 E WASHINGTON ST</td>\n",
       "      <td>ALEXANDRIA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46001</td>\n",
       "      <td>73.2</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>5275.0</td>\n",
       "      <td>ANDERSON COMMUNITY SCHOOL CORP</td>\n",
       "      <td>1600 HILLCREST AVE</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>72.4</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9790.0</td>\n",
       "      <td>ANDERSON PREPARATORY ACADEMY</td>\n",
       "      <td>3205 W 25TH ST</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>403</td>\n",
       "      <td>8665.0</td>\n",
       "      <td>WHITLEY CO CONS SCHOOLS</td>\n",
       "      <td>107 N WALNUT ST</td>\n",
       "      <td>COLUMBIA CITY</td>\n",
       "      <td>IN</td>\n",
       "      <td>46725</td>\n",
       "      <td>81.7</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>404</td>\n",
       "      <td>9540.0</td>\n",
       "      <td>WILSON EDUCATION CTR CORP</td>\n",
       "      <td>PO BOX 217</td>\n",
       "      <td>CHARLESTOWN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>405</td>\n",
       "      <td>9845.0</td>\n",
       "      <td>XAVIER SCHOOL OF EXCELLENCE</td>\n",
       "      <td>850 W JACKSON BLVD STE 850</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>IN</td>\n",
       "      <td>60607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>406</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>YORKTOWN COMMUNITY SCHOOLS</td>\n",
       "      <td>2311 S BROADWAY ST</td>\n",
       "      <td>YORKTOWN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47396</td>\n",
       "      <td>86.3</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>407</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>408 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID                     District Name                Mailing Address  \\\n",
       "0    9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "1      15.0   ADAMS CENTRAL COMMUNITY SCHOOLS            222 W WASHINGTON ST   \n",
       "2    5265.0        ALEXANDRIA COM SCHOOL CORP            202 E WASHINGTON ST   \n",
       "3    5275.0    ANDERSON COMMUNITY SCHOOL CORP             1600 HILLCREST AVE   \n",
       "4    9790.0      ANDERSON PREPARATORY ACADEMY                 3205 W 25TH ST   \n",
       "..      ...                               ...                            ...   \n",
       "403  8665.0           WHITLEY CO CONS SCHOOLS                107 N WALNUT ST   \n",
       "404  9540.0         WILSON EDUCATION CTR CORP                     PO BOX 217   \n",
       "405  9845.0       XAVIER SCHOOL OF EXCELLENCE     850 W JACKSON BLVD STE 850   \n",
       "406  1910.0        YORKTOWN COMMUNITY SCHOOLS             2311 S BROADWAY ST   \n",
       "407   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "\n",
       "              City State    ZIP  Overall Points Overall Grade  \n",
       "0     INDIANAPOLIS    IN  46204             NaN           NaN  \n",
       "1           MONROE    IN  46772            87.2             B  \n",
       "2       ALEXANDRIA    IN  46001            73.2             C  \n",
       "3         ANDERSON    IN  46011            72.4             C  \n",
       "4         ANDERSON    IN  46011             NaN           NaN  \n",
       "..             ...   ...    ...             ...           ...  \n",
       "403  COLUMBIA CITY    IN  46725            81.7             B  \n",
       "404    CHARLESTOWN    IN  47111             NaN           NaN  \n",
       "405        CHICAGO    IN  60607             NaN           NaN  \n",
       "406       YORKTOWN    IN  47396            86.3             B  \n",
       "407     ZIONSVILLE    IN  46077            94.7             A  \n",
       "\n",
       "[408 rows x 8 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_complete\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Mailing Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>Number of returns</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551,529</td>\n",
       "      <td>4,150</td>\n",
       "      <td>559,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13284</td>\n",
       "      <td>1020</td>\n",
       "      <td>13635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37721</td>\n",
       "      <td>990</td>\n",
       "      <td>38523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50494</td>\n",
       "      <td>830</td>\n",
       "      <td>51224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33246</td>\n",
       "      <td>390</td>\n",
       "      <td>33575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2755</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>57939</td>\n",
       "      <td>1560</td>\n",
       "      <td>59367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2756</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>81710</td>\n",
       "      <td>1320</td>\n",
       "      <td>83411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2757</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>99923</td>\n",
       "      <td>1140</td>\n",
       "      <td>101389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2758</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>513353</td>\n",
       "      <td>3560</td>\n",
       "      <td>520606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2759</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>1784854</td>\n",
       "      <td>3390</td>\n",
       "      <td>1817916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2760 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                     District Name                Mailing Address  \\\n",
       "0     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "1     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "2     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "3     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "4     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "...      ...                               ...                            ...   \n",
       "2755   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2756   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2757   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2758   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2759   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "\n",
       "              City State    ZIP  Overall Points Overall Grade  \\\n",
       "0     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "1     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "2     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "3     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "4     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "...            ...   ...    ...             ...           ...   \n",
       "2755    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2756    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2757    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2758    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2759    ZIONSVILLE    IN  46077            94.7             A   \n",
       "\n",
       "     Adjust Gross Income Number of returns   Amount  \n",
       "0                551,529             4,150  559,279  \n",
       "1                  13284              1020    13635  \n",
       "2                  37721               990    38523  \n",
       "3                  50494               830    51224  \n",
       "4                  33246               390    33575  \n",
       "...                  ...               ...      ...  \n",
       "2755               57939              1560    59367  \n",
       "2756               81710              1320    83411  \n",
       "2757               99923              1140   101389  \n",
       "2758              513353              3560   520606  \n",
       "2759             1784854              3390  1817916  \n",
       "\n",
       "[2760 rows x 11 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_income_complete = pd.merge(school_complete, income_data, how=\"left\", on=[\"ZIP\", \"ZIP\"])\n",
    "school_income_complete\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Mailing Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>Number of returns</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551,529</td>\n",
       "      <td>4,150</td>\n",
       "      <td>559,279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13284</td>\n",
       "      <td>1020</td>\n",
       "      <td>13635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37721</td>\n",
       "      <td>990</td>\n",
       "      <td>38523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50494</td>\n",
       "      <td>830</td>\n",
       "      <td>51224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9545.0</td>\n",
       "      <td>21ST CENTURY CHARTER SCH OF GARY</td>\n",
       "      <td>333 N PENNSYLVANIA SUITE 1000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33246</td>\n",
       "      <td>390</td>\n",
       "      <td>33575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2755</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>57939</td>\n",
       "      <td>1560</td>\n",
       "      <td>59367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2756</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>81710</td>\n",
       "      <td>1320</td>\n",
       "      <td>83411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2757</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>99923</td>\n",
       "      <td>1140</td>\n",
       "      <td>101389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2758</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>513353</td>\n",
       "      <td>3560</td>\n",
       "      <td>520606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2759</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>1784854</td>\n",
       "      <td>3390</td>\n",
       "      <td>1817916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2760 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                     District Name                Mailing Address  \\\n",
       "0     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "1     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "2     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "3     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "4     9545.0  21ST CENTURY CHARTER SCH OF GARY  333 N PENNSYLVANIA SUITE 1000   \n",
       "...      ...                               ...                            ...   \n",
       "2755   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2756   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2757   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2758   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "2759   630.0      ZIONSVILLE COMMUNITY SCHOOLS                900 MULBERRY ST   \n",
       "\n",
       "              City State    ZIP  Overall Points Overall Grade  \\\n",
       "0     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "1     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "2     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "3     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "4     INDIANAPOLIS    IN  46204             NaN           NaN   \n",
       "...            ...   ...    ...             ...           ...   \n",
       "2755    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2756    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2757    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2758    ZIONSVILLE    IN  46077            94.7             A   \n",
       "2759    ZIONSVILLE    IN  46077            94.7             A   \n",
       "\n",
       "     Adjust Gross Income Number of returns   Amount  \n",
       "0                551,529             4,150  559,279  \n",
       "1                  13284              1020    13635  \n",
       "2                  37721               990    38523  \n",
       "3                  50494               830    51224  \n",
       "4                  33246               390    33575  \n",
       "...                  ...               ...      ...  \n",
       "2755               57939              1560    59367  \n",
       "2756               81710              1320    83411  \n",
       "2757               99923              1140   101389  \n",
       "2758              513353              3560   520606  \n",
       "2759             1784854              3390  1817916  \n",
       "\n",
       "[2760 rows x 11 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_income_complete\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "school_district = school_income_complete.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     float64\n",
       "District Name           object\n",
       "Mailing Address         object\n",
       "City                    object\n",
       "State                   object\n",
       "ZIP                      int64\n",
       "Overall Points         float64\n",
       "Overall Grade           object\n",
       "Adjust Gross Income     object\n",
       "Number of returns       object\n",
       "Amount                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matthouser/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "school_district['Amount'] = pd.to_numeric(school_district['Amount'], errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district['Amount'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matthouser/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "school_district['Adjust Gross Income'] = pd.to_numeric(school_district['Adjust Gross Income'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district['Adjust Gross Income'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/matthouser/Applications/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "school_district['Number of returns'] = pd.to_numeric(school_district['Number of returns'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     float64\n",
       "District Name           object\n",
       "Mailing Address         object\n",
       "City                    object\n",
       "State                   object\n",
       "ZIP                      int64\n",
       "Overall Points         float64\n",
       "Overall Grade           object\n",
       "Adjust Gross Income    float64\n",
       "Number of returns      float64\n",
       "Amount                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Mailing Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>Number of returns</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "      <td>5552.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>5574.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "      <td>14219.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>14334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "      <td>10530.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>10631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>15.0</td>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>222 W WASHINGTON ST</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>87.2</td>\n",
       "      <td>B</td>\n",
       "      <td>9735.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>9830.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2755</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>57939.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>59367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2756</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>81710.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>83411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2757</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>99923.0</td>\n",
       "      <td>1140.0</td>\n",
       "      <td>101389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2758</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>513353.0</td>\n",
       "      <td>3560.0</td>\n",
       "      <td>520606.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2759</td>\n",
       "      <td>630.0</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>900 MULBERRY ST</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>94.7</td>\n",
       "      <td>A</td>\n",
       "      <td>1784854.0</td>\n",
       "      <td>3390.0</td>\n",
       "      <td>1817916.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1813 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID                    District Name      Mailing Address        City  \\\n",
       "7      15.0  ADAMS CENTRAL COMMUNITY SCHOOLS  222 W WASHINGTON ST      MONROE   \n",
       "8      15.0  ADAMS CENTRAL COMMUNITY SCHOOLS  222 W WASHINGTON ST      MONROE   \n",
       "9      15.0  ADAMS CENTRAL COMMUNITY SCHOOLS  222 W WASHINGTON ST      MONROE   \n",
       "10     15.0  ADAMS CENTRAL COMMUNITY SCHOOLS  222 W WASHINGTON ST      MONROE   \n",
       "11     15.0  ADAMS CENTRAL COMMUNITY SCHOOLS  222 W WASHINGTON ST      MONROE   \n",
       "...     ...                              ...                  ...         ...   \n",
       "2755  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "2756  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "2757  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "2758  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "2759  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "\n",
       "     State    ZIP  Overall Points Overall Grade  Adjust Gross Income  \\\n",
       "7       IN  46772            87.2             B                  NaN   \n",
       "8       IN  46772            87.2             B               5552.0   \n",
       "9       IN  46772            87.2             B              14219.0   \n",
       "10      IN  46772            87.2             B              10530.0   \n",
       "11      IN  46772            87.2             B               9735.0   \n",
       "...    ...    ...             ...           ...                  ...   \n",
       "2755    IN  46077            94.7             A              57939.0   \n",
       "2756    IN  46077            94.7             A              81710.0   \n",
       "2757    IN  46077            94.7             A              99923.0   \n",
       "2758    IN  46077            94.7             A             513353.0   \n",
       "2759    IN  46077            94.7             A            1784854.0   \n",
       "\n",
       "      Number of returns     Amount  \n",
       "7                   NaN        NaN  \n",
       "8                 460.0     5574.0  \n",
       "9                 400.0    14334.0  \n",
       "10                170.0    10631.0  \n",
       "11                110.0     9830.0  \n",
       "...                 ...        ...  \n",
       "2755             1560.0    59367.0  \n",
       "2756             1320.0    83411.0  \n",
       "2757             1140.0   101389.0  \n",
       "2758             3560.0   520606.0  \n",
       "2759             3390.0  1817916.0  \n",
       "\n",
       "[1813 rows x 11 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>15.0</td>\n",
       "      <td>87.2</td>\n",
       "      <td>11756.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25.0</td>\n",
       "      <td>86.4</td>\n",
       "      <td>78662.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35.0</td>\n",
       "      <td>90.3</td>\n",
       "      <td>27757.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>205356.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>235.0</td>\n",
       "      <td>77.3</td>\n",
       "      <td>28598.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8525.0</td>\n",
       "      <td>90.1</td>\n",
       "      <td>4251.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8535.0</td>\n",
       "      <td>88.9</td>\n",
       "      <td>8219.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8565.0</td>\n",
       "      <td>83.9</td>\n",
       "      <td>63622.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8625.0</td>\n",
       "      <td>82.8</td>\n",
       "      <td>36448.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8665.0</td>\n",
       "      <td>81.7</td>\n",
       "      <td>112163.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Overall Points  Adjust Gross Income\n",
       "ID                                         \n",
       "15.0              87.2         11756.400000\n",
       "25.0              86.4         78662.500000\n",
       "35.0              90.3         27757.833333\n",
       "125.0             93.3        205356.500000\n",
       "235.0             77.3         28598.666667\n",
       "...                ...                  ...\n",
       "8525.0            90.1          4251.400000\n",
       "8535.0            88.9          8219.400000\n",
       "8565.0            83.9         63622.500000\n",
       "8625.0            82.8         36448.166667\n",
       "8665.0            81.7        112163.333333\n",
       "\n",
       "[259 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district.groupby('ID').mean()[['Overall Points', 'Adjust Gross Income']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adjust Gross Income</th>\n",
       "      <th>Number of returns</th>\n",
       "      <th>Overall Points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>median</th>\n",
       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ADAMS CENTRAL COMMUNITY SCHOOLS</td>\n",
       "      <td>MONROE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46772</td>\n",
       "      <td>B</td>\n",
       "      <td>10530.0</td>\n",
       "      <td>58782.0</td>\n",
       "      <td>1240.0</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ALEXANDRIA COM SCHOOL CORP</td>\n",
       "      <td>ALEXANDRIA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46001</td>\n",
       "      <td>C</td>\n",
       "      <td>43256.0</td>\n",
       "      <td>232301.0</td>\n",
       "      <td>4910.0</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ANDERSON COMMUNITY SCHOOL CORP</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>C</td>\n",
       "      <td>72958.5</td>\n",
       "      <td>453901.0</td>\n",
       "      <td>8270.0</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ARGOS COMMUNITY SCHOOLS</td>\n",
       "      <td>ARGOS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46501</td>\n",
       "      <td>B</td>\n",
       "      <td>16112.0</td>\n",
       "      <td>75693.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>80.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ATTICA CONSOLIDATED SCH CORP</td>\n",
       "      <td>ATTICA</td>\n",
       "      <td>IN</td>\n",
       "      <td>47918</td>\n",
       "      <td>C</td>\n",
       "      <td>25679.0</td>\n",
       "      <td>137011.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WHITING SCHOOL CITY</td>\n",
       "      <td>WHITING</td>\n",
       "      <td>IN</td>\n",
       "      <td>46394</td>\n",
       "      <td>B</td>\n",
       "      <td>49138.0</td>\n",
       "      <td>263552.0</td>\n",
       "      <td>5590.0</td>\n",
       "      <td>80.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WHITKO COMMUNITY SCHOOL CORP</td>\n",
       "      <td>PIERCETON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46562</td>\n",
       "      <td>C</td>\n",
       "      <td>20914.0</td>\n",
       "      <td>119581.0</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>WHITLEY CO CONS SCHOOLS</td>\n",
       "      <td>COLUMBIA CITY</td>\n",
       "      <td>IN</td>\n",
       "      <td>46725</td>\n",
       "      <td>B</td>\n",
       "      <td>108188.5</td>\n",
       "      <td>672980.0</td>\n",
       "      <td>11730.0</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>YORKTOWN COMMUNITY SCHOOLS</td>\n",
       "      <td>YORKTOWN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47396</td>\n",
       "      <td>B</td>\n",
       "      <td>30624.0</td>\n",
       "      <td>266084.0</td>\n",
       "      <td>3510.0</td>\n",
       "      <td>86.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>A</td>\n",
       "      <td>90816.5</td>\n",
       "      <td>2568102.0</td>\n",
       "      <td>14420.0</td>\n",
       "      <td>94.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                        Adjust Gross Income  \\\n",
       "                                                                                     median   \n",
       "District Name                   City          State ZIP   Overall Grade                       \n",
       "ADAMS CENTRAL COMMUNITY SCHOOLS MONROE        IN    46772 B                         10530.0   \n",
       "ALEXANDRIA COM SCHOOL CORP      ALEXANDRIA    IN    46001 C                         43256.0   \n",
       "ANDERSON COMMUNITY SCHOOL CORP  ANDERSON      IN    46011 C                         72958.5   \n",
       "ARGOS COMMUNITY SCHOOLS         ARGOS         IN    46501 B                         16112.0   \n",
       "ATTICA CONSOLIDATED SCH CORP    ATTICA        IN    47918 C                         25679.0   \n",
       "...                                                                                     ...   \n",
       "WHITING SCHOOL CITY             WHITING       IN    46394 B                         49138.0   \n",
       "WHITKO COMMUNITY SCHOOL CORP    PIERCETON     IN    46562 C                         20914.0   \n",
       "WHITLEY CO CONS SCHOOLS         COLUMBIA CITY IN    46725 B                        108188.5   \n",
       "YORKTOWN COMMUNITY SCHOOLS      YORKTOWN      IN    47396 B                         30624.0   \n",
       "ZIONSVILLE COMMUNITY SCHOOLS    ZIONSVILLE    IN    46077 A                         90816.5   \n",
       "\n",
       "                                                                                    \\\n",
       "                                                                               sum   \n",
       "District Name                   City          State ZIP   Overall Grade              \n",
       "ADAMS CENTRAL COMMUNITY SCHOOLS MONROE        IN    46772 B                58782.0   \n",
       "ALEXANDRIA COM SCHOOL CORP      ALEXANDRIA    IN    46001 C               232301.0   \n",
       "ANDERSON COMMUNITY SCHOOL CORP  ANDERSON      IN    46011 C               453901.0   \n",
       "ARGOS COMMUNITY SCHOOLS         ARGOS         IN    46501 B                75693.0   \n",
       "ATTICA CONSOLIDATED SCH CORP    ATTICA        IN    47918 C               137011.0   \n",
       "...                                                                            ...   \n",
       "WHITING SCHOOL CITY             WHITING       IN    46394 B               263552.0   \n",
       "WHITKO COMMUNITY SCHOOL CORP    PIERCETON     IN    46562 C               119581.0   \n",
       "WHITLEY CO CONS SCHOOLS         COLUMBIA CITY IN    46725 B               672980.0   \n",
       "YORKTOWN COMMUNITY SCHOOLS      YORKTOWN      IN    47396 B               266084.0   \n",
       "ZIONSVILLE COMMUNITY SCHOOLS    ZIONSVILLE    IN    46077 A              2568102.0   \n",
       "\n",
       "                                                                        Number of returns  \\\n",
       "                                                                                      sum   \n",
       "District Name                   City          State ZIP   Overall Grade                     \n",
       "ADAMS CENTRAL COMMUNITY SCHOOLS MONROE        IN    46772 B                        1240.0   \n",
       "ALEXANDRIA COM SCHOOL CORP      ALEXANDRIA    IN    46001 C                        4910.0   \n",
       "ANDERSON COMMUNITY SCHOOL CORP  ANDERSON      IN    46011 C                        8270.0   \n",
       "ARGOS COMMUNITY SCHOOLS         ARGOS         IN    46501 B                        1700.0   \n",
       "ATTICA CONSOLIDATED SCH CORP    ATTICA        IN    47918 C                        2810.0   \n",
       "...                                                                                   ...   \n",
       "WHITING SCHOOL CITY             WHITING       IN    46394 B                        5590.0   \n",
       "WHITKO COMMUNITY SCHOOL CORP    PIERCETON     IN    46562 C                        2330.0   \n",
       "WHITLEY CO CONS SCHOOLS         COLUMBIA CITY IN    46725 B                       11730.0   \n",
       "YORKTOWN COMMUNITY SCHOOLS      YORKTOWN      IN    47396 B                        3510.0   \n",
       "ZIONSVILLE COMMUNITY SCHOOLS    ZIONSVILLE    IN    46077 A                       14420.0   \n",
       "\n",
       "                                                                        Overall Points  \n",
       "                                                                                  mean  \n",
       "District Name                   City          State ZIP   Overall Grade                 \n",
       "ADAMS CENTRAL COMMUNITY SCHOOLS MONROE        IN    46772 B                       87.2  \n",
       "ALEXANDRIA COM SCHOOL CORP      ALEXANDRIA    IN    46001 C                       73.2  \n",
       "ANDERSON COMMUNITY SCHOOL CORP  ANDERSON      IN    46011 C                       72.4  \n",
       "ARGOS COMMUNITY SCHOOLS         ARGOS         IN    46501 B                       80.4  \n",
       "ATTICA CONSOLIDATED SCH CORP    ATTICA        IN    47918 C                       79.4  \n",
       "...                                                                                ...  \n",
       "WHITING SCHOOL CITY             WHITING       IN    46394 B                       80.1  \n",
       "WHITKO COMMUNITY SCHOOL CORP    PIERCETON     IN    46562 C                       79.4  \n",
       "WHITLEY CO CONS SCHOOLS         COLUMBIA CITY IN    46725 B                       81.7  \n",
       "YORKTOWN COMMUNITY SCHOOLS      YORKTOWN      IN    47396 B                       86.3  \n",
       "ZIONSVILLE COMMUNITY SCHOOLS    ZIONSVILLE    IN    46077 A                       94.7  \n",
       "\n",
       "[259 rows x 4 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.pivot_table(school_district, values=['Overall Points', 'Number of returns', 'Adjust Gross Income'], index=['District Name', 'City', 'State', 'ZIP', 'Overall Grade'],\n",
    "                      aggfunc={'Overall Points': np.mean,\n",
    "                               'Number of returns': np.sum, \n",
    "                               'Adjust Gross Income': [np.median, np.sum]})  \n",
    "                               \n",
    "                              \n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>District Name</th>\n",
       "      <th>Overall Grade</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>Overall Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>AKRON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46910</td>\n",
       "      <td>TIPPECANOE VALLEY SCHOOL CORP</td>\n",
       "      <td>B</td>\n",
       "      <td>14869.400000</td>\n",
       "      <td>83.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>ALBION</td>\n",
       "      <td>IN</td>\n",
       "      <td>46701</td>\n",
       "      <td>CENTRAL NOBLE COM SCHOOL CORP</td>\n",
       "      <td>C</td>\n",
       "      <td>32280.000000</td>\n",
       "      <td>76.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ALEXANDRIA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46001</td>\n",
       "      <td>ALEXANDRIA COM SCHOOL CORP</td>\n",
       "      <td>C</td>\n",
       "      <td>38716.833333</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>ANDERSON COMMUNITY SCHOOL CORP</td>\n",
       "      <td>C</td>\n",
       "      <td>75650.166667</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>FRANKTON-LAPEL COMMUNITY SCHS</td>\n",
       "      <td>B</td>\n",
       "      <td>75650.166667</td>\n",
       "      <td>82.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>254</td>\n",
       "      <td>WINAMAC</td>\n",
       "      <td>IN</td>\n",
       "      <td>46996</td>\n",
       "      <td>EASTERN PULASKI COM SCH CORP</td>\n",
       "      <td>B</td>\n",
       "      <td>26645.333333</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>WINCHESTER</td>\n",
       "      <td>IN</td>\n",
       "      <td>47394</td>\n",
       "      <td>RANDOLPH CENTRAL SCHOOL CORP</td>\n",
       "      <td>B</td>\n",
       "      <td>27576.833333</td>\n",
       "      <td>82.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>256</td>\n",
       "      <td>WOLCOTT</td>\n",
       "      <td>IN</td>\n",
       "      <td>47995</td>\n",
       "      <td>TRI-COUNTY SCHOOL CORP</td>\n",
       "      <td>B</td>\n",
       "      <td>8219.400000</td>\n",
       "      <td>88.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>257</td>\n",
       "      <td>YORKTOWN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47396</td>\n",
       "      <td>YORKTOWN COMMUNITY SCHOOLS</td>\n",
       "      <td>B</td>\n",
       "      <td>44347.333333</td>\n",
       "      <td>86.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "      <td>A</td>\n",
       "      <td>428017.000000</td>\n",
       "      <td>94.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City State    ZIP                   District Name Overall Grade  \\\n",
       "0         AKRON    IN  46910   TIPPECANOE VALLEY SCHOOL CORP             B   \n",
       "1        ALBION    IN  46701   CENTRAL NOBLE COM SCHOOL CORP             C   \n",
       "2    ALEXANDRIA    IN  46001      ALEXANDRIA COM SCHOOL CORP             C   \n",
       "3      ANDERSON    IN  46011  ANDERSON COMMUNITY SCHOOL CORP             C   \n",
       "4      ANDERSON    IN  46011   FRANKTON-LAPEL COMMUNITY SCHS             B   \n",
       "..          ...   ...    ...                             ...           ...   \n",
       "254     WINAMAC    IN  46996    EASTERN PULASKI COM SCH CORP             B   \n",
       "255  WINCHESTER    IN  47394    RANDOLPH CENTRAL SCHOOL CORP             B   \n",
       "256     WOLCOTT    IN  47995          TRI-COUNTY SCHOOL CORP             B   \n",
       "257    YORKTOWN    IN  47396      YORKTOWN COMMUNITY SCHOOLS             B   \n",
       "258  ZIONSVILLE    IN  46077    ZIONSVILLE COMMUNITY SCHOOLS             A   \n",
       "\n",
       "     Adjust Gross Income  Overall Points  \n",
       "0           14869.400000            83.6  \n",
       "1           32280.000000            76.3  \n",
       "2           38716.833333            73.2  \n",
       "3           75650.166667            72.4  \n",
       "4           75650.166667            82.7  \n",
       "..                   ...             ...  \n",
       "254         26645.333333            83.0  \n",
       "255         27576.833333            82.9  \n",
       "256          8219.400000            88.9  \n",
       "257         44347.333333            86.3  \n",
       "258        428017.000000            94.7  \n",
       "\n",
       "[259 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Narrowed data to test scores in each zip\n",
    "test_score_table = pd.pivot_table(school_district, values=['Overall Points','Adjust Gross Income'], index=['City','State','ZIP','District Name','Overall Grade'])  \n",
    "test_score_table = test_score_table.reset_index()\n",
    "test_score_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>ZIP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overall Points</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>District Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>59.3</td>\n",
       "      <td>12441.600000</td>\n",
       "      <td>CANNELTON CITY SCHOOLS</td>\n",
       "      <td>47520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>64.7</td>\n",
       "      <td>91921.500000</td>\n",
       "      <td>INDIANAPOLIS PUBLIC SCHOOLS</td>\n",
       "      <td>46204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65.1</td>\n",
       "      <td>3859.200000</td>\n",
       "      <td>UNION SCHOOL CORPORATION</td>\n",
       "      <td>47358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67.7</td>\n",
       "      <td>6978.000000</td>\n",
       "      <td>MEDORA COMMUNITY SCHOOL CORP</td>\n",
       "      <td>47260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68.3</td>\n",
       "      <td>36774.666667</td>\n",
       "      <td>ELWOOD COMMUNITY SCHOOL CORP</td>\n",
       "      <td>46036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96.6</td>\n",
       "      <td>3457.400000</td>\n",
       "      <td>EASTERN HANCOCK CO COM SCH CORP</td>\n",
       "      <td>46117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96.8</td>\n",
       "      <td>178516.000000</td>\n",
       "      <td>PLAINFIELD COMMUNITY SCH CORP</td>\n",
       "      <td>46168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98.3</td>\n",
       "      <td>452893.500000</td>\n",
       "      <td>CARMEL CLAY SCHOOLS</td>\n",
       "      <td>46033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99.0</td>\n",
       "      <td>239086.166667</td>\n",
       "      <td>BROWNSBURG COMMUNITY SCH CORP</td>\n",
       "      <td>46112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100.2</td>\n",
       "      <td>17894.000000</td>\n",
       "      <td>BARR-REEVE COM SCHOOLS INC</td>\n",
       "      <td>47558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>259 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                      ZIP\n",
       "Overall Points Adjust Gross Income District Name                         \n",
       "59.3           12441.600000        CANNELTON CITY SCHOOLS           47520\n",
       "64.7           91921.500000        INDIANAPOLIS PUBLIC SCHOOLS      46204\n",
       "65.1           3859.200000         UNION SCHOOL CORPORATION         47358\n",
       "67.7           6978.000000         MEDORA COMMUNITY SCHOOL CORP     47260\n",
       "68.3           36774.666667        ELWOOD COMMUNITY SCHOOL CORP     46036\n",
       "...                                                                   ...\n",
       "96.6           3457.400000         EASTERN HANCOCK CO COM SCH CORP  46117\n",
       "96.8           178516.000000       PLAINFIELD COMMUNITY SCH CORP    46168\n",
       "98.3           452893.500000       CARMEL CLAY SCHOOLS              46033\n",
       "99.0           239086.166667       BROWNSBURG COMMUNITY SCH CORP    46112\n",
       "100.2          17894.000000        BARR-REEVE COM SCHOOLS INC       47558\n",
       "\n",
       "[259 rows x 1 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average Test Scores by Zip\n",
    "average_points = test_score_table.groupby([\"Overall Points\",'Adjust Gross Income','District Name'])\n",
    "average_comparison = average_points.mean()\n",
    "#average_comparison = average_comparison.reset_index()\n",
    "average_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0.0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0.0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-11137b31bc48>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Worst Average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mworst_average_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage_comparison\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m      3\u001b[0m \u001b[0mworst_average_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\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   1417\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    803\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\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    804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;31m# we may have a nested tuples indexer here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_nested_tuple_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\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[0;32m--> 910\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_nested_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;31m# we maybe be using a tuple to represent multiple dimensions here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_nested_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m             \u001b[0mcurrent_ndim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 993\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    994\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1848\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1849\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\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[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3733\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3734\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\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[0;32m-> 3735\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3736\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3737\u001b[0m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc_level\u001b[0;34m(self, key, level, drop_level)\u001b[0m\n\u001b[1;32m   2854\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0milevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2855\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2856\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2857\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   2937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2939\u001b[0;31m             \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2941\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexsort_depth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    477\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\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    478\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0.0"
     ]
    }
   ],
   "source": [
    "#Worst Average\n",
    "worst_average_score = average_comparison.loc[0, :]\n",
    "worst_average_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "120.0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 120.0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-54-450463733bf4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#Best Average\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mbest_average_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maverage_comparison\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m      3\u001b[0m \u001b[0mbest_average_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\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   1417\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    803\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\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    804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;31m# we may have a nested tuples indexer here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_nested_tuple_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\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[0;32m--> 910\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_nested_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;31m# we maybe be using a tuple to represent multiple dimensions here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_nested_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m             \u001b[0mcurrent_ndim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 993\u001b[0;31m             \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    994\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    995\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1848\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1849\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1850\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1851\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no slices here, handle elsewhere\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\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[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mxs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   3733\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3734\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\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[0;32m-> 3735\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3736\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3737\u001b[0m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc_level\u001b[0;34m(self, key, level, drop_level)\u001b[0m\n\u001b[1;32m   2854\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0milevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2855\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2856\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2857\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   2937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2939\u001b[0;31m             \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2941\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexsort_depth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    477\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\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    478\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\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[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 120.0"
     ]
    }
   ],
   "source": [
    "#Best Average\n",
    "best_average_score = average_comparison.loc[120,:]\n",
    "best_average_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Overall Points</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>District Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>83.6</td>\n",
       "      <td>14869.400000</td>\n",
       "      <td>AKRON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46910</td>\n",
       "      <td>TIPPECANOE VALLEY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>76.3</td>\n",
       "      <td>32280.000000</td>\n",
       "      <td>ALBION</td>\n",
       "      <td>IN</td>\n",
       "      <td>46701</td>\n",
       "      <td>CENTRAL NOBLE COM SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>82.7</td>\n",
       "      <td>75650.166667</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>FRANKTON-LAPEL COMMUNITY SCHS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>84.7</td>\n",
       "      <td>81631.333333</td>\n",
       "      <td>ANGOLA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46703</td>\n",
       "      <td>M S D STEUBEN COUNTY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>86.0</td>\n",
       "      <td>15056.000000</td>\n",
       "      <td>ARCADIA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46030</td>\n",
       "      <td>HAMILTON HEIGHTS SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>254</td>\n",
       "      <td>83.0</td>\n",
       "      <td>26645.333333</td>\n",
       "      <td>WINAMAC</td>\n",
       "      <td>IN</td>\n",
       "      <td>46996</td>\n",
       "      <td>EASTERN PULASKI COM SCH CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>82.9</td>\n",
       "      <td>27576.833333</td>\n",
       "      <td>WINCHESTER</td>\n",
       "      <td>IN</td>\n",
       "      <td>47394</td>\n",
       "      <td>RANDOLPH CENTRAL SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>256</td>\n",
       "      <td>88.9</td>\n",
       "      <td>8219.400000</td>\n",
       "      <td>WOLCOTT</td>\n",
       "      <td>IN</td>\n",
       "      <td>47995</td>\n",
       "      <td>TRI-COUNTY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>257</td>\n",
       "      <td>86.3</td>\n",
       "      <td>44347.333333</td>\n",
       "      <td>YORKTOWN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47396</td>\n",
       "      <td>YORKTOWN COMMUNITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>94.7</td>\n",
       "      <td>428017.000000</td>\n",
       "      <td>ZIONSVILLE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46077</td>\n",
       "      <td>ZIONSVILLE COMMUNITY SCHOOLS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall Points  Adjust Gross Income        City State    ZIP  \\\n",
       "0              83.6         14869.400000       AKRON    IN  46910   \n",
       "1              76.3         32280.000000      ALBION    IN  46701   \n",
       "4              82.7         75650.166667    ANDERSON    IN  46011   \n",
       "5              84.7         81631.333333      ANGOLA    IN  46703   \n",
       "6              86.0         15056.000000     ARCADIA    IN  46030   \n",
       "..              ...                  ...         ...   ...    ...   \n",
       "254            83.0         26645.333333     WINAMAC    IN  46996   \n",
       "255            82.9         27576.833333  WINCHESTER    IN  47394   \n",
       "256            88.9          8219.400000     WOLCOTT    IN  47995   \n",
       "257            86.3         44347.333333    YORKTOWN    IN  47396   \n",
       "258            94.7        428017.000000  ZIONSVILLE    IN  46077   \n",
       "\n",
       "                     District Name  \n",
       "0    TIPPECANOE VALLEY SCHOOL CORP  \n",
       "1    CENTRAL NOBLE COM SCHOOL CORP  \n",
       "4    FRANKTON-LAPEL COMMUNITY SCHS  \n",
       "5             M S D STEUBEN COUNTY  \n",
       "6     HAMILTON HEIGHTS SCHOOL CORP  \n",
       "..                             ...  \n",
       "254   EASTERN PULASKI COM SCH CORP  \n",
       "255   RANDOLPH CENTRAL SCHOOL CORP  \n",
       "256         TRI-COUNTY SCHOOL CORP  \n",
       "257     YORKTOWN COMMUNITY SCHOOLS  \n",
       "258   ZIONSVILLE COMMUNITY SCHOOLS  \n",
       "\n",
       "[234 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All school with test scores equal to or over 76\n",
    "over_seventy_six = test_score_table.loc[test_score_table[\"Overall Points\"] >=76, [\n",
    "    \"Overall Points\",'Adjust Gross Income',\"City\", \"State\", \"ZIP\", \"District Name\"]]\n",
    "over_seventy_six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must be the same size",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-dcd9b9856e0c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mover_seventy_six_scatter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0munder_seventy_six_scatter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munder_seventy_six\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Overall Points'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mover_seventy_six_scatter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munder_seventy_six_scatter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m   2845\u001b[0m         \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0medgecolors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2846\u001b[0m         plotnonfinite=plotnonfinite, **({\"data\": data} if data is not\n\u001b[0;32m-> 2847\u001b[0;31m         None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2848\u001b[0m     \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2849\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1600\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Applications/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m   4442\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4443\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4444\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4446\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
     ]
    },
    {
     "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": [
    "#Scatter Plot\n",
    "over_seventy_six_scatter = over_seventy_six.index\n",
    "over_seventy_six_scatter\n",
    "under_seventy_six_scatter = under_seventy_six['Overall Points']\n",
    "plt.scatter(over_seventy_six_scatter, under_seventy_six_scatter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Overall Points</th>\n",
       "      <th>Adjust Gross Income</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>District Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>73.2</td>\n",
       "      <td>38716.833333</td>\n",
       "      <td>ALEXANDRIA</td>\n",
       "      <td>IN</td>\n",
       "      <td>46001</td>\n",
       "      <td>ALEXANDRIA COM SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>72.4</td>\n",
       "      <td>75650.166667</td>\n",
       "      <td>ANDERSON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46011</td>\n",
       "      <td>ANDERSON COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>72.4</td>\n",
       "      <td>44824.833333</td>\n",
       "      <td>BEECH GROVE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46107</td>\n",
       "      <td>BEECH GROVE CITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>59.3</td>\n",
       "      <td>12441.600000</td>\n",
       "      <td>CANNELTON</td>\n",
       "      <td>IN</td>\n",
       "      <td>47520</td>\n",
       "      <td>CANNELTON CITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>68.3</td>\n",
       "      <td>36774.666667</td>\n",
       "      <td>ELWOOD</td>\n",
       "      <td>IN</td>\n",
       "      <td>46036</td>\n",
       "      <td>ELWOOD COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>79</td>\n",
       "      <td>74.4</td>\n",
       "      <td>82692.166667</td>\n",
       "      <td>FRANKFORT</td>\n",
       "      <td>IN</td>\n",
       "      <td>46041</td>\n",
       "      <td>COMMUNITY SCHOOLS OF FRANKFORT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16553.166667</td>\n",
       "      <td>HAMILTON</td>\n",
       "      <td>IN</td>\n",
       "      <td>46742</td>\n",
       "      <td>HAMILTON COMMUNITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>73.2</td>\n",
       "      <td>7407.600000</td>\n",
       "      <td>HAMLET</td>\n",
       "      <td>IN</td>\n",
       "      <td>46532</td>\n",
       "      <td>OREGON-DAVIS SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>101</td>\n",
       "      <td>71.8</td>\n",
       "      <td>19071.000000</td>\n",
       "      <td>HANOVER</td>\n",
       "      <td>IN</td>\n",
       "      <td>47243</td>\n",
       "      <td>SOUTHWESTERN-JEFFERSON CO CON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>73.3</td>\n",
       "      <td>135653.500000</td>\n",
       "      <td>HOBART</td>\n",
       "      <td>IN</td>\n",
       "      <td>46342</td>\n",
       "      <td>RIVER FOREST COMMUNITY SCH CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>64.7</td>\n",
       "      <td>91921.500000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>INDIANAPOLIS PUBLIC SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>69.0</td>\n",
       "      <td>120854.000000</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46219</td>\n",
       "      <td>M S D WARREN TOWNSHIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>112</td>\n",
       "      <td>73.8</td>\n",
       "      <td>84288.166667</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46221</td>\n",
       "      <td>M S D DECATUR TOWNSHIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76972.833333</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46241</td>\n",
       "      <td>M S D WAYNE TOWNSHIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>75.4</td>\n",
       "      <td>42988.500000</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>IN</td>\n",
       "      <td>47904</td>\n",
       "      <td>LAFAYETTE SCHOOL CORPORATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>73.1</td>\n",
       "      <td>10601.000000</td>\n",
       "      <td>LYNN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47355</td>\n",
       "      <td>RANDOLPH SOUTHERN SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>75.9</td>\n",
       "      <td>90091.333333</td>\n",
       "      <td>MADISON</td>\n",
       "      <td>IN</td>\n",
       "      <td>47250</td>\n",
       "      <td>MADISON CONSOLIDATED SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>70.2</td>\n",
       "      <td>77197.333333</td>\n",
       "      <td>MARION</td>\n",
       "      <td>IN</td>\n",
       "      <td>46952</td>\n",
       "      <td>MARION COMMUNITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>67.7</td>\n",
       "      <td>6978.000000</td>\n",
       "      <td>MEDORA</td>\n",
       "      <td>IN</td>\n",
       "      <td>47260</td>\n",
       "      <td>MEDORA COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>74.6</td>\n",
       "      <td>31950.500000</td>\n",
       "      <td>MITCHELL</td>\n",
       "      <td>IN</td>\n",
       "      <td>47446</td>\n",
       "      <td>MITCHELL COMMUNITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>65.1</td>\n",
       "      <td>3859.200000</td>\n",
       "      <td>MODOC</td>\n",
       "      <td>IN</td>\n",
       "      <td>47358</td>\n",
       "      <td>UNION SCHOOL CORPORATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>74.3</td>\n",
       "      <td>3036.400000</td>\n",
       "      <td>STRAUGHN</td>\n",
       "      <td>IN</td>\n",
       "      <td>47387</td>\n",
       "      <td>SOUTH HENRY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>227</td>\n",
       "      <td>75.2</td>\n",
       "      <td>63552.166667</td>\n",
       "      <td>SYRACUSE</td>\n",
       "      <td>IN</td>\n",
       "      <td>46567</td>\n",
       "      <td>WAWASEE COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>233</td>\n",
       "      <td>75.8</td>\n",
       "      <td>14841.333333</td>\n",
       "      <td>UNION CITY</td>\n",
       "      <td>IN</td>\n",
       "      <td>47390</td>\n",
       "      <td>RANDOLPH EASTERN SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>240</td>\n",
       "      <td>75.5</td>\n",
       "      <td>19838.400000</td>\n",
       "      <td>VEVAY</td>\n",
       "      <td>IN</td>\n",
       "      <td>47043</td>\n",
       "      <td>SWITZERLAND COUNTY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall Points  Adjust Gross Income          City State    ZIP  \\\n",
       "2              73.2         38716.833333    ALEXANDRIA    IN  46001   \n",
       "3              72.4         75650.166667      ANDERSON    IN  46011   \n",
       "15             72.4         44824.833333   BEECH GROVE    IN  46107   \n",
       "31             59.3         12441.600000     CANNELTON    IN  47520   \n",
       "66             68.3         36774.666667        ELWOOD    IN  46036   \n",
       "79             74.4         82692.166667     FRANKFORT    IN  46041   \n",
       "99             72.0         16553.166667      HAMILTON    IN  46742   \n",
       "100            73.2          7407.600000        HAMLET    IN  46532   \n",
       "101            71.8         19071.000000       HANOVER    IN  47243   \n",
       "105            73.3        135653.500000        HOBART    IN  46342   \n",
       "110            64.7         91921.500000  INDIANAPOLIS    IN  46204   \n",
       "111            69.0        120854.000000  INDIANAPOLIS    IN  46219   \n",
       "112            73.8         84288.166667  INDIANAPOLIS    IN  46221   \n",
       "116            75.9         76972.833333  INDIANAPOLIS    IN  46241   \n",
       "128            75.4         42988.500000     LAFAYETTE    IN  47904   \n",
       "146            73.1         10601.000000          LYNN    IN  47355   \n",
       "147            75.9         90091.333333       MADISON    IN  47250   \n",
       "149            70.2         77197.333333        MARION    IN  46952   \n",
       "151            67.7          6978.000000        MEDORA    IN  47260   \n",
       "158            74.6         31950.500000      MITCHELL    IN  47446   \n",
       "159            65.1          3859.200000         MODOC    IN  47358   \n",
       "225            74.3          3036.400000      STRAUGHN    IN  47387   \n",
       "227            75.2         63552.166667      SYRACUSE    IN  46567   \n",
       "233            75.8         14841.333333    UNION CITY    IN  47390   \n",
       "240            75.5         19838.400000         VEVAY    IN  47043   \n",
       "\n",
       "                       District Name  \n",
       "2         ALEXANDRIA COM SCHOOL CORP  \n",
       "3     ANDERSON COMMUNITY SCHOOL CORP  \n",
       "15          BEECH GROVE CITY SCHOOLS  \n",
       "31            CANNELTON CITY SCHOOLS  \n",
       "66      ELWOOD COMMUNITY SCHOOL CORP  \n",
       "79    COMMUNITY SCHOOLS OF FRANKFORT  \n",
       "99        HAMILTON COMMUNITY SCHOOLS  \n",
       "100         OREGON-DAVIS SCHOOL CORP  \n",
       "101    SOUTHWESTERN-JEFFERSON CO CON  \n",
       "105  RIVER FOREST COMMUNITY SCH CORP  \n",
       "110      INDIANAPOLIS PUBLIC SCHOOLS  \n",
       "111            M S D WARREN TOWNSHIP  \n",
       "112           M S D DECATUR TOWNSHIP  \n",
       "116             M S D WAYNE TOWNSHIP  \n",
       "128     LAFAYETTE SCHOOL CORPORATION  \n",
       "146    RANDOLPH SOUTHERN SCHOOL CORP  \n",
       "147     MADISON CONSOLIDATED SCHOOLS  \n",
       "149         MARION COMMUNITY SCHOOLS  \n",
       "151     MEDORA COMMUNITY SCHOOL CORP  \n",
       "158       MITCHELL COMMUNITY SCHOOLS  \n",
       "159         UNION SCHOOL CORPORATION  \n",
       "225          SOUTH HENRY SCHOOL CORP  \n",
       "227    WAWASEE COMMUNITY SCHOOL CORP  \n",
       "233     RANDOLPH EASTERN SCHOOL CORP  \n",
       "240   SWITZERLAND COUNTY SCHOOL CORP  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All school with test scores under 76\n",
    "under_seventy_six = test_score_table.loc[test_score_table[\"Overall Points\"] < 76, [\n",
    "    \"Overall Points\",'Adjust Gross Income',\"City\", \"State\", \"ZIP\", \"District Name\"]]\n",
    "under_seventy_six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall Points</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>District Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>59.3</td>\n",
       "      <td>CANNELTON</td>\n",
       "      <td>IN</td>\n",
       "      <td>47520</td>\n",
       "      <td>CANNELTON CITY SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>66</td>\n",
       "      <td>68.3</td>\n",
       "      <td>ELWOOD</td>\n",
       "      <td>IN</td>\n",
       "      <td>46036</td>\n",
       "      <td>ELWOOD COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>110</td>\n",
       "      <td>64.7</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46204</td>\n",
       "      <td>INDIANAPOLIS PUBLIC SCHOOLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>111</td>\n",
       "      <td>69.0</td>\n",
       "      <td>INDIANAPOLIS</td>\n",
       "      <td>IN</td>\n",
       "      <td>46219</td>\n",
       "      <td>M S D WARREN TOWNSHIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>67.7</td>\n",
       "      <td>MEDORA</td>\n",
       "      <td>IN</td>\n",
       "      <td>47260</td>\n",
       "      <td>MEDORA COMMUNITY SCHOOL CORP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>65.1</td>\n",
       "      <td>MODOC</td>\n",
       "      <td>IN</td>\n",
       "      <td>47358</td>\n",
       "      <td>UNION SCHOOL CORPORATION</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Overall Points          City State    ZIP                 District Name\n",
       "31             59.3     CANNELTON    IN  47520        CANNELTON CITY SCHOOLS\n",
       "66             68.3        ELWOOD    IN  46036  ELWOOD COMMUNITY SCHOOL CORP\n",
       "110            64.7  INDIANAPOLIS    IN  46204   INDIANAPOLIS PUBLIC SCHOOLS\n",
       "111            69.0  INDIANAPOLIS    IN  46219         M S D WARREN TOWNSHIP\n",
       "151            67.7        MEDORA    IN  47260  MEDORA COMMUNITY SCHOOL CORP\n",
       "159            65.1         MODOC    IN  47358      UNION SCHOOL CORPORATION"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All scores under 70\n",
    "under_seventy = test_score_table.loc[test_score_table[\"Overall Points\"] < 70, [\n",
    "    \"Overall Points\",\"City\", \"State\", \"ZIP\", \"District Name\"]]\n",
    "under_seventy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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": {
      "text/plain": [
       "<Figure size 576000x21600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "under_seventy.plot(kind='bar')\n",
    "plt.figure(figsize=(8000.0,300.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Highest Test Score</th>\n",
       "      <th>Lowest Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>100.2</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Highest Test Score  Lowest Test Score\n",
       "0               100.2               59.3"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lowest and Highest Overall Score\n",
    "lowest_score = test_score_table[\"Overall Points\"].min()\n",
    "lowest_score\n",
    "highest_score = test_score_table[\"Overall Points\"].max()\n",
    "highest_score\n",
    "\n",
    "summary_table = pd.DataFrame({\"Highest Test Score\": [highest_score],\n",
    "                              \"Lowest Test Score\": [lowest_score]})\n",
    "summary_table\n",
    "#Need to get the city/state/zip included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Overall Points</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZIP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>46033</td>\n",
       "      <td>1.0</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>98.300000</td>\n",
       "      <td>98.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46060</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.100000</td>\n",
       "      <td>91.100000</td>\n",
       "      <td>91.100000</td>\n",
       "      <td>91.100000</td>\n",
       "      <td>91.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46065</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.200000</td>\n",
       "      <td>91.200000</td>\n",
       "      <td>91.200000</td>\n",
       "      <td>91.200000</td>\n",
       "      <td>91.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>91.700000</td>\n",
       "      <td>91.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46077</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>94.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46112</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46117</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>96.600000</td>\n",
       "      <td>96.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46122</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>93.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46143</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>92.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46149</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46168</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>96.800000</td>\n",
       "      <td>96.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46304</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46321</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>95.700000</td>\n",
       "      <td>95.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46347</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>90.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46385</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.566667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.566667</td>\n",
       "      <td>90.566667</td>\n",
       "      <td>90.566667</td>\n",
       "      <td>90.566667</td>\n",
       "      <td>90.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46504</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "      <td>96.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.800000</td>\n",
       "      <td>91.800000</td>\n",
       "      <td>91.800000</td>\n",
       "      <td>91.800000</td>\n",
       "      <td>91.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>90.300000</td>\n",
       "      <td>90.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46721</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>90.400000</td>\n",
       "      <td>90.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46814</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>93.300000</td>\n",
       "      <td>93.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46936</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>92.300000</td>\n",
       "      <td>92.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47006</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>90.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "      <td>90.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47112</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.800000</td>\n",
       "      <td>93.800000</td>\n",
       "      <td>93.800000</td>\n",
       "      <td>93.800000</td>\n",
       "      <td>93.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47401</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "      <td>90.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>94.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>92.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.400000</td>\n",
       "      <td>91.400000</td>\n",
       "      <td>91.400000</td>\n",
       "      <td>91.400000</td>\n",
       "      <td>91.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47551</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47558</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>100.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47586</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>90.200000</td>\n",
       "      <td>90.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47620</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>92.200000</td>\n",
       "      <td>92.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47633</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47906</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.800000</td>\n",
       "      <td>95.800000</td>\n",
       "      <td>95.800000</td>\n",
       "      <td>95.800000</td>\n",
       "      <td>95.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47929</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.100000</td>\n",
       "      <td>90.100000</td>\n",
       "      <td>90.100000</td>\n",
       "      <td>90.100000</td>\n",
       "      <td>90.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Overall Points                                                      \\\n",
       "               count        mean std         min         25%         50%   \n",
       "ZIP                                                                        \n",
       "46033            1.0   98.300000 NaN   98.300000   98.300000   98.300000   \n",
       "46040            1.0   90.500000 NaN   90.500000   90.500000   90.500000   \n",
       "46060            1.0   91.100000 NaN   91.100000   91.100000   91.100000   \n",
       "46065            1.0   91.200000 NaN   91.200000   91.200000   91.200000   \n",
       "46069            1.0   91.700000 NaN   91.700000   91.700000   91.700000   \n",
       "46077            1.0   94.700000 NaN   94.700000   94.700000   94.700000   \n",
       "46112            1.0   99.000000 NaN   99.000000   99.000000   99.000000   \n",
       "46117            1.0   96.600000 NaN   96.600000   96.600000   96.600000   \n",
       "46122            1.0   93.100000 NaN   93.100000   93.100000   93.100000   \n",
       "46143            1.0   92.900000 NaN   92.900000   92.900000   92.900000   \n",
       "46149            1.0   90.500000 NaN   90.500000   90.500000   90.500000   \n",
       "46168            1.0   96.800000 NaN   96.800000   96.800000   96.800000   \n",
       "46224            1.0   96.400000 NaN   96.400000   96.400000   96.400000   \n",
       "46304            1.0   90.600000 NaN   90.600000   90.600000   90.600000   \n",
       "46321            1.0   95.700000 NaN   95.700000   95.700000   95.700000   \n",
       "46347            1.0   90.900000 NaN   90.900000   90.900000   90.900000   \n",
       "46356            1.0   90.600000 NaN   90.600000   90.600000   90.600000   \n",
       "46385            1.0   90.566667 NaN   90.566667   90.566667   90.566667   \n",
       "46504            1.0   96.400000 NaN   96.400000   96.400000   96.400000   \n",
       "46545            1.0   91.800000 NaN   91.800000   91.800000   91.800000   \n",
       "46550            1.0   90.800000 NaN   90.800000   90.800000   90.800000   \n",
       "46571            1.0   94.200000 NaN   94.200000   94.200000   94.200000   \n",
       "46711            1.0   90.300000 NaN   90.300000   90.300000   90.300000   \n",
       "46721            1.0   90.400000 NaN   90.400000   90.400000   90.400000   \n",
       "46814            1.0   93.300000 NaN   93.300000   93.300000   93.300000   \n",
       "46936            1.0   90.600000 NaN   90.600000   90.600000   90.600000   \n",
       "46979            1.0   92.300000 NaN   92.300000   92.300000   92.300000   \n",
       "47006            1.0   90.800000 NaN   90.800000   90.800000   90.800000   \n",
       "47025            1.0   90.500000 NaN   90.500000   90.500000   90.500000   \n",
       "47112            1.0   93.800000 NaN   93.800000   93.800000   93.800000   \n",
       "47401            1.0   90.600000 NaN   90.600000   90.600000   90.600000   \n",
       "47429            1.0   94.200000 NaN   94.200000   94.200000   94.200000   \n",
       "47527            1.0   92.700000 NaN   92.700000   92.700000   92.700000   \n",
       "47529            1.0   91.400000 NaN   91.400000   91.400000   91.400000   \n",
       "47551            1.0   92.000000 NaN   92.000000   92.000000   92.000000   \n",
       "47558            1.0  100.200000 NaN  100.200000  100.200000  100.200000   \n",
       "47586            1.0   90.200000 NaN   90.200000   90.200000   90.200000   \n",
       "47620            1.0   92.200000 NaN   92.200000   92.200000   92.200000   \n",
       "47633            1.0   92.000000 NaN   92.000000   92.000000   92.000000   \n",
       "47906            1.0   95.800000 NaN   95.800000   95.800000   95.800000   \n",
       "47929            1.0   90.100000 NaN   90.100000   90.100000   90.100000   \n",
       "\n",
       "                               \n",
       "              75%         max  \n",
       "ZIP                            \n",
       "46033   98.300000   98.300000  \n",
       "46040   90.500000   90.500000  \n",
       "46060   91.100000   91.100000  \n",
       "46065   91.200000   91.200000  \n",
       "46069   91.700000   91.700000  \n",
       "46077   94.700000   94.700000  \n",
       "46112   99.000000   99.000000  \n",
       "46117   96.600000   96.600000  \n",
       "46122   93.100000   93.100000  \n",
       "46143   92.900000   92.900000  \n",
       "46149   90.500000   90.500000  \n",
       "46168   96.800000   96.800000  \n",
       "46224   96.400000   96.400000  \n",
       "46304   90.600000   90.600000  \n",
       "46321   95.700000   95.700000  \n",
       "46347   90.900000   90.900000  \n",
       "46356   90.600000   90.600000  \n",
       "46385   90.566667   90.566667  \n",
       "46504   96.400000   96.400000  \n",
       "46545   91.800000   91.800000  \n",
       "46550   90.800000   90.800000  \n",
       "46571   94.200000   94.200000  \n",
       "46711   90.300000   90.300000  \n",
       "46721   90.400000   90.400000  \n",
       "46814   93.300000   93.300000  \n",
       "46936   90.600000   90.600000  \n",
       "46979   92.300000   92.300000  \n",
       "47006   90.800000   90.800000  \n",
       "47025   90.500000   90.500000  \n",
       "47112   93.800000   93.800000  \n",
       "47401   90.600000   90.600000  \n",
       "47429   94.200000   94.200000  \n",
       "47527   92.700000   92.700000  \n",
       "47529   91.400000   91.400000  \n",
       "47551   92.000000   92.000000  \n",
       "47558  100.200000  100.200000  \n",
       "47586   90.200000   90.200000  \n",
       "47620   92.200000   92.200000  \n",
       "47633   92.000000   92.000000  \n",
       "47906   95.800000   95.800000  \n",
       "47929   90.100000   90.100000  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Chart 0-75, <85, <95, >95\n",
    "zip_code_group = zip_code_group[zip_code_group['Overall Points']> 90].groupby('ZIP')\n",
    "zip_code_group.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": {
      "text/plain": [
       "<Figure size 576000x21600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar Chart\n",
    "import matplotlib.pyplot as plt\n",
    "zip_code_group.plot(kind='bar')\n",
    "plt.figure(figsize=(8000.0,300.0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d148c10>"
      ]
     },
     "execution_count": 108,
     "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": [
    "#Scatter Plot\n",
    "zip_code = zip_code_group.index\n",
    "zip_code\n",
    "grades_by_zip = zip_code_group['Overall Points']\n",
    "plt.scatter(zip_code,grades_by_zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Map based on zip coordinates\n",
    "#create poppa for each map\n",
    "#http://geopandas.org/geocoding.html\n",
    "#https://matplotlib.org/gallery/index.html (charts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}