Group Project Brent (6).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Does Higher <font color= 'olivedrab'>Median Income </font>Mean Higher <font color= 'goldenrod'>Test Scores</font>?\n",
    "***\n",
    "## Data Analysis based on IRS Median Household Income and Indiana Department of Education Data Reports\n",
    "#### Brent Thomas, Matt Houser, Chelsea Snedden, Emmanuel Olofinkua\n",
    "#### Sources of Data for Analysis:\n",
    "#### &#8226; [IRS Individual Income Tax Statistics by Zip Code, 2017](https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2017-zip-code-data-soi)\n",
    "#### &#8226; [Indiana DOE School Corporation Data, 2017](https://www.doe.in.gov/accountability/find-school-and-corporation-data-reports)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'INSchoolDataCopy.csv' does not exist: b'INSchoolDataCopy.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-53-f8b311d96117>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# Read School and Student Data File and store into Pandas Data Frames\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m \u001b[0mschool_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mschool_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[0mtest_score_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_score_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mincome_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mincome_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\conda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    683\u001b[0m         )\n\u001b[0;32m    684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    687\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\conda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\conda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 895\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    897\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\conda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1134\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1135\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1136\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1137\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\conda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1915\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1917\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1918\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1919\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File b'INSchoolDataCopy.csv' does not exist: b'INSchoolDataCopy.csv'"
     ]
    }
   ],
   "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",
    "# 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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
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       "      <th>0</th>\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",
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       "      <th>1</th>\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",
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       "      <th>2</th>\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",
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       "      <th>3</th>\n",
       "      <td>5275.0</td>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\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",
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       "      <th>403</th>\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",
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       "    <tr>\n",
       "      <th>404</th>\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",
       "      <th>405</th>\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",
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       "      <th>406</th>\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",
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       "      <td>B</td>\n",
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       "      <th>407</th>\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",
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      "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": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "school_complete\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "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",
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       "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": 3,
     "metadata": {},
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    }
   ],
   "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": 4,
   "metadata": {},
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      "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",
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       "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",
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       "[2760 rows x 11 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_income_complete\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "school_district = school_income_complete.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bthom\\Anaconda3\\envs\\PythonData\\lib\\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district['Amount'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bthom\\Anaconda3\\envs\\PythonData\\lib\\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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "school_district['Adjust Gross Income'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bthom\\Anaconda3\\envs\\PythonData\\lib\\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"
     ]
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   ],
   "source": [
    "school_district['Number of returns'] = pd.to_numeric(school_district['Number of returns'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                     float64\n",
       "District Name           object\n",
       "Mailing Address         object\n",
       "City                    object\n",
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       "Overall Grade           object\n",
       "Adjust Gross Income    float64\n",
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "school_district.dtypes"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "         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",
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       "...     ...                              ...                  ...         ...   \n",
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       "2759  630.0     ZIONSVILLE COMMUNITY SCHOOLS      900 MULBERRY ST  ZIONSVILLE   \n",
       "\n",
       "     State    ZIP  Overall Points Overall Grade  Adjust Gross Income  \\\n",
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "school_district"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        Overall Points  Adjust Gross Income\n",
       "ID                                         \n",
       "15.0              87.2         11756.400000\n",
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     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "school_district.groupby('ID').mean()[['Overall Points', 'Adjust Gross Income']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison to Other States: <font color='crimson'> Indiana </font> vs. <font color='orange'> Tennessee </font>\n",
    "***\n",
    "## Comparison on <font color= 'olivedrab'> Median Income </font> by State and<font color='mediumslateblue'> SAT Test Score</font>.\n",
    "####  Sources of Data : \n",
    "#### &#8226; [Federal Reserve Economic Data, Indiana, 2017](https://fred.stlouisfed.org/graph/?id=MEHOINUSINA672N,)\n",
    "    \n",
    "#### &#8226; [Federal Reserve Economic Data, Tennessee, 2017](https://fred.stlouisfed.org/graph/?id=MEHOINUSTNA646N,)\n",
    "#### &#8226; [SAT Annual Report, Indiana, 2017](https://reports.collegeboard.org/pdf/2017-indiana-sat-suite-assessments-annual-report.pdf)\n",
    "#### &#8226; [SAT Annual Report, Tennessee, 2017](https://reports.collegeboard.org/pdf/2017-tennessee-sat-suite-assessments-annual-report.pdf,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Indiana vs Tennessee SAT bar chart.\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "#Data from College Board, Median SAT scores.\n",
    "means_sat=[1074,1228]\n",
    "means_sat_essay=[1098,1283]\n",
    "\n",
    "#Setting other chart needs.\n",
    "x = np.arange(1,3)\n",
    "labels_2=('Indiana','Tennessee')\n",
    "width=.35\n",
    "fig, ax = plt.subplots()\n",
    "rects_sat = ax.bar(x-width/2, means_sat, width, label='Mean SAT Score', color='rosybrown', align='center')\n",
    "rects_sat_essay = ax.bar(x+width/2, means_sat_essay, width, label='Mean SAT Score w/ Essay', color='thistle', align='center')\n",
    "\n",
    "#setting labels and tick marks.\n",
    "ax.set_ylabel('Mean Score')\n",
    "ax.set_title('Indiana and Tennessee SAT Test Scores, 2017')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels_2)\n",
    "ax.legend()                         \n",
    "                           \n",
    "#Plotting\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")\n",
    "x_vals=[\"Indiana\",\"Tennessee\"]\n",
    "y_vals=[60200,55306]\n",
    "colors='rosybrown','thistle'\n",
    "ax = sns.barplot(x=x_vals, y=y_vals, palette = colors,capsize = 0.05,             \n",
    "            saturation = 8,             \n",
    "            errcolor = 'gray', errwidth = 2,  \n",
    "            ci = 'sd' )\n",
    "ax.axes.yaxis.label.set_text(\"Median Adjusted Gross Income\")\n",
    "ax.title.set_text('Indiana and Tennessee Median Income, 2017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hnz9MrC9KYrPl1wkhFEikE1MVFUxlEEQBgiAg4AvA7/LD6/RC9slQZRWKrDT7Py5zwRZBEiDpJeiNeuhNeuiMOhgsBhjMBuiMOkgGCQDAVAos0rnRekgk4jEWbM0IogCmMAS8AficPnidXvjdfvjdfsg+WdMaRZ0IvVEPQ5QBJpsJFrsFBoshGJiCAFGi3nUS+SiQSMQ5N4AUvwJXjQuuWhc8dR4oAUXr8lpOAIwWI0w2E8wxZphjzNAb9VBVNdglKFIrikQWCiQS9iImgFpAEAWYok2Iio1CdHI0dIZgrzu1oEgkoEAiYSkUQoIAZ7UTzkon3HXuiAugS9EZdbAmWGFLtsFoNYKpDJJO0rosQlqFAomEFUVWQiHUUNYAV63rsgcZRCpRJyIqLgq2JBsssRYwxmiABAkrFEiEe4qsQBAFuOvcqCuug6uGQuiSBMBit8CeZkdUXBQA6tYj/KNAIlxqvBco4A2gtrgWjgoHVFnVuqywJOpE2JJtiMuIg6SXIEgCtZoIlyiQCFcab0h117lRnV8Nb4NX65IiislmQlxWHKJiqdVE+EOBRLigKsHWT0N5A2oKaxDwBDSuKLJJBgmx6bGwp9shQICoo2Ai2qNAIppqnH6ntqgWdcV1nW6UnNYEQYAt1YbEbokQRLoBl2iLAoloQpVVqIqKqrwqNJQ3hKbNIdoQRAH2dDviu8TTzBBEMxRIpEM1zh9XeaYS9aX1WpdDfkKQBMRlxCEuKw4AXWMiHYsCiXQIVVUBBtSV1KE6rzp0zYjwSdSJiMuKQ2x6bPDvFEykA1AgkXanKirctW5UnKpAwEuDFcKJpJcQ3zUeMSkxoVnRCWkvFEik3aiyCtkvo+xEGTz1Hq3LIVfAEGVAWp806M16ai2RdkOBRNpc43WiilMVaChv0Loc0obs6XYkXpUICMFFCAlpSxRIpE2pigpntRPlJ8rpOlGE0hl1SO2TClO0iVpLpE1RIJE2wVQGVVVRdrwMziqn1uWQDhCdFI3knsnB+5eotUTaAAUSuWKqosLr8KLkaAkUP93Y2plIeglJVyfBGm+l1hK5YhRIpNUa552rOF2B+hK6p6gzi4qPQmqfVFrJllwRCiTSKqqiIuANoPhIMc07RwAAepMe6f3ToTfSSDzSOhRI5LKpioqawhpU51fTukSkCUEUkNwzGdGJ0RRK5LJRIJEWaxy4UPJdCdy1bq3LIRyzpdiQfHUy3UxLLgsFEmkRVVGhBBQUHiykLjrSIkarERn9MyDqRBqFR1qEAolckiLLUPwq8vfl071F5LKIOhHp/dJhstE9S+TS6AghF6UEAqg4eRI6gwRLrEXrckiYUWUVhQcLUVtUS19myCVRIJELUgIBnPz6axz95BMc++xTpPZJhdFq1LosEoaqzlah4nQFhRK5KAokcl5KIIDD//kPig8dAgCUHT+OwgP7kdE/A5JB0rg6Eo7qS+pReqyUQolcEAUSaUb2+3FwwwZUnz3b5Oenvv4aDWWlyLwuk25+JK3irHKi6FARVJlCiTRHgURCGGOQ/X7sX7cOtUVF533M/nVroSo+pPZN7eDqSKTw1HtQcKAAiqyAxlSRc1EgEQAAU1XIPh/2vfceGsrKLvrY3W+/DXO0EQndEjqoOhJpfE4fCvYXQJVVCiUSQsO+CZiqIvBDGLlra1u0TVR8PAZPuQfl35fDUeFo5wo1JgA6vQ6SUYLOoIPOoIOkkwABEAQh1H3ZOLcfGKDICmS/HPzPJ0MJKDSrxXnozXpkDcyCpJOoG5hQIHV2jd10e999t8Vh1CixRw9cM2YsCg8UwuvwtlOFHUdn0sFkNcFkM8FsM0Nn1EHSSxAlsUnYhGYf+OH82TgTQeijxIJ/ZiprElqNNxfLPhmeBg+8DV54nV7IXlmjZ8wHvUmPLjd0gagTaVaHTi5iA6moqAi33347unfv3uTny5cvR2pq669/jBo1CkuXLkWfPn0AAA8//DBOnDiBTz75BADgdruRk5ODnTt3wmQynXcfv/71r/H8888jOTm51XW0Fdnvx77334ezsrJV21+VnY2sGwYjb28eZF/4nFglgwSzzQyTzQRLjCU0nJ0xBlFq/xMjYwyqooZ+j8/pg7veDW+DF54GT6dbxsMYZUTWwCyIOrqK0JnptC6gPSUlJWHDhg1tus/s7Gx8++236NOnDxRFwfHjx2G1WlFYWIjMzEwcOHAAAwYMuGAYAcCKFSvatKbWUgIBHPzww1aHEQCc2bUL1qRkZF6Xibx9eWAKv99vjFYjrAlW2JJs0Jl0YGrHhM/5CIIQ7Pb7gTkmGI6qokIQBcheGQ3lDXBWO+Fz+jq8vo7mc/lQdKQIGddm0IwOnVinfOerqqrwm9/8BpMmTcLkyZOxY8cOAIDL5cL8+fMxadIkTJgwAZs2bWq2bWMgAcDBgwfRp08f5OTk4OuvvwYA7N27Fzk5OQCAt99+G7/4xS8wbtw4TJw4EWfOnAEAjBw5EkVFRZg4cSKOHDkCAFAUBTfeeCOqq6tx6NAhTJkyBRMnTsSMGTNQWFgIAJg2bRpmz56N0aNH49ixY1f0GiiBAA5v3oy6kpIr2g8AHNq4AQGfG+n90q94X21JEARYYi1I7pmMHjk9kDUgC3FZcTBYDBBFMXjdgqMuosaQEkURBosBcV3ikDUgCz1yeiC5ZzIssRau6m1rnjoPyk6U0X1KnVhEB1JFRQUmTJgQ+u/1118HACxatAiTJ0/GunXrsGzZMixYsABOpxPLli1Dv379sG7dOqxevRrLly8PhUGj7Oxs7N+/HwCwbds2jBgxAjk5Odi2bRsAYM+ePcjJyYHT6cRnn32Gt956C5s2bcLNN9+M1atXN9nXhAkTsHnzZgDArl270Lt3b0RHR+Ppp5/Gn//8Z6xfvx73338/nnnmmdA2vXr1wieffBLqMmwNJRDAsU8/RXVeXqv38VO7V78Ng0WHxO6JbbbP1jJYDMEQGtEDaf3SEJMaE7wWFGaTfIqiCFEnQtJLiEmNQVq/NPQYEQwng8WgdXntwlHhQNXZKgqlTqpTdtnt2LEDZ86cwd/+9jcAgCzLKCwsxI4dO+D1erF27VoAwetBJ0+eRGZmZmjbuLg4WK1WlJWVYdu2bVi6dCni4+Px+OOPw+/3o7i4GL1794YgCPjzn/+MzZs3Iy8vD19//XWzELnjjjvwy1/+Eo8//jg2bdqE3Nxc5OXlobCwEA899FDocU6nM/Tn/v37X9FrogQCyNuzB+Xff39F+/kpVZax+501yJ52H3wuHxrKGtp0/5ckANYEK+Ky4mC0GJuMfosE53bxxaTEwJZsg8/tQ01BDZxVzogawVdbVAu9SY+Y1BjqvutkIjqQLkRVVaxatQp2ux1AsCUVHx8PVVWxZMkS9OvXD0Cway8mJqbZ9tnZ2di6dSvcbndogESvXr2wadMmDBw4EIIgoLS0FNOmTcPUqVNx4403IiEhoVk3W2JiIrp164ZvvvkGO3fuxIIFC5Cfn4+MjIxQkCqKgqqqqtA2F7s2dSlKIIDq/Hzk7d7d6n1cjLe+Hof+vRHX5U5AwBOAp97TLr/nXDqjDvY0O+xpdkBAk+sykUoQBQgQYI42I6VXCtATqCupQ11xHWR/+AwsuZiKUxXQGXWIiouiUOpEOuU7nZ2djTVr1gAATp06hfHjx8Pj8SA7OxvvvPMOgGBI5ebmorS0tNn2w4YNw5tvvons7OzQz3JycrBy5UqMGDECAHD48GF06dIF06dPx7XXXovPPvsMitJ85NSECRPw0ksvYejQoTCbzbjqqqtQX1+PvXv3AgDWrl2Lxx577Iqfs6oo8DQ04LuPPrrifV1MTX4+zuzcgfRr06E36dvt90gGCSm9UtBtSDfEZsRC0kudIox+StJJkPQSYjNi0W1oN6T0SomYuQZLj5bC5/JBVan7rrPolIH09NNP4+DBgxg/fjweffRR/OlPf4LVasXs2bPh9Xoxbtw4/Nd//RfmzZuHrKysZtsPHjwYeXl5ofABgoH0/fffY/jw4aG/q6qKsWPHYuLEiejWrRuKzjMdz89//nPk5eUhNzcXAGAwGLB06VK8+OKLGD9+PNavX49FixZd8XNWAgHsX7cO6nlCsa3l792LqjOnkXld24+YEnUiEq9KxFVDr0J0cnCZbPoGjdDrEJ0cjauGXoWEqxLCfgg1Y4zmvetkIvY+JPIjJRDAvg8+gKO8vEN/79Cp0yDqzCg8WHjF1zgEUYA93Y6ELgmAAAqhS1AVFWBAVX4V6orrgjfphimzzYyMdvhyQ/hDgRThlEAAJ7ZuRekPw8s7kiiKyJn5a7hrfSg/2fowtKXYkNQ9CYIo0EnpMqmKCqYyVJyu6PiBJm0orksc4rPi6f2PcPTuRjBFllGdl6dJGAHBwSO731mD6CRrcNDBZdIZdcgamIXkq5NDU/iQyyNKwWHjyVcnI2tgFnTG8BzHVJNfA6/DG9YtPXJp9AmPUIwxBLxeHP2//9O0Dp/DgYMbPkRi98TLWgI9JjUG3YZ0gzHaSEHUBkRJhDHaiG5DuiEmtfnI0XBQ8l0J3Z8U4eiTHqFUWcahjRuhBAJal4LaoiKc/Gor0vqlXfKGzsZWUVKPpOCF+jC6kZV3ohgc+JDUIyksW0tKQEHxd8UUShGMPu0RSA4EkLd7NxwVFVqXElJ08CDKTxwPXpy+wOgvahV1jHBuLXnqPKgtqqVQilD0qY8wqqrCXVODvD17tC6lmWOffgp3XQ0y+meElm4AgiPo0q9Jp1ZRBwq1lronIf2a9LCa1aLqbBXdnxSh6JMfYZii4Eg73/x6Jfb+618QJRacYQDBLrqug7rCEmuhVpEGRJ0IS6wFXQZ1CasuvJLvSiJquiQSRGeACCL7/cjbvRueujqtS7kwVcXu1W8jKs6CpKuT0HVQV+hMOgojDYmSCL1Jj66DusJsM2tdTovIPhnV+dXUdRdh6CwQIRhj8LvdyN+3T+tSLsnvdqPo4AHY0+zB4dzURac5UQwOD8+4LgO2FJvW5bRITWFNcGl4EjHoTBAhVFnGkf/8B4zzfnVBENBr5EhkDhgQ0Wv7hCtREpF8dTKSeiY1uc7HJQaUHiulVlIEoUCKAEoggJLvvuNqVN35CKKI/rm5SO3TB5K+/SZeJVdGlETEJMcg/Zp07kPJU++Bs9pJAxwiBAVSBFAVBad+WCCQV6IkYcD/+3+IzcigMAoDoiTCYrcg49oM7kfgVZysACiPIgIFUpiT/X6c/OorqDK/6+CIkoQBkyYhJjWVwiiMiJIIc4wZGf35DiUloKDyTCV13UUACqQw53e7UfqThf94IkgSBkycCFtSEoVRGBIlEaZoU7ClxPE1v7qSOgS8AdBc0eGNAimMKYEAjn/+OcDph1AQRVyXmwtbcjKFURgTJREmmwlp16ZxfU2p9HgpTb4a5iiQwpSqqmgoK0NtYaHWpVzQNWPHwp6WRmEUAURJhCXGgrS+aVqXckE+hw/uWjeFUhijQApTTFVxfMsWrcu4oG5DhyK+SxcKowgiSiKi4qIQ3yVe61IuqPJMJXXbhTEKpDCkKgoqT5+Gu7ZW61LOK6F7d3QZNIjCKAKJkoi4rDhY461al3JefrcfrhoXDQMPUxRIYYgxhrO7dmldxnlFxcfjmttvpzCKYKIkIrVvKgxRF19KRCuVZyppnrswRYEUZlRVRW1BAZetI73JhIGTJkHUhc8knaR1BFFAZv/MCy4loqWAJwBXNbWSwhF/RxO5KKYoOL1jh9ZlNCOIIq6bMAF6o5Hr4cGkbQiCAFEvNltKhBdVeVXUSgpDFEhhhDGG+rIyOKuqtC6lmZ433QRrQgK1jjoRURRhjDIiqUeS1qU043f74an30ACHMEOBFEZUWcbp7du1LqOZ2IwMpPbtS9eNOiFREhGTEgOznb9lK6rOVtEQ8DBDgRRG3LW1aCgr07qMJiS9Hv3GjKEw6sRESURanzTuphfyOrzwuXxal0Eug8CoTRsWZL8fxz79FBUnT2pdCgBg14kT2HLwIIzR0dAZjXA6nSgvL8fWrVuRkJAAAPjwww+xcuXK0DYOhyP0GLfbjd/+9rfweDx44IEHcOedd4a2ycvLw5w5czR5XqR1VEVFQ3kDyr8v1yWrM+wAACAASURBVLqUJqwJVqT0ToGkk7QuhbQABVKYkP1+fLV8OVfrHcVmZOC6CROgApg6dSomTpyIu++++7yPDQQCTR7zwgsv4Nprr8WoUaMwduxYfPbZZ3A6nZgxYwZWrVoFs5m/LiBycaqiouhwETx1Hq1L+ZEA9MjpQYEUJqjLLgyoigpR0uHGWb9BvzFjYIiK0rqkJl11K1asQFxc3AXDCECzxxgMBrjdbrjd7tCKsX//+98xY8YMCqMwxWXXHQMayhvoWlKYoBZSGFAVFWd3n4XBbEBsZiwsdgvc9fU4u2snKr7/XpOaet96K1J690a9w4HRo0dj3bp1yMzMPO9ja2pqmj2moqICjz/+OGpqavDwww+jW7duWLx4Md54442OfBqkjfHYdWeIMqDL9V0gSvT9m3cUSGHAXedG4YEfJ1GVDBLsqXbY0+1gTEXF9ydw6uuvIfv9HVKPNSEBg375S0h6PZYvX468vDy8+OKLF3x8Sx4zc+ZMPPHEEygqKsKaNWtgtVqxYMEC2O329ngKpB2pioqCbwu4GlDQdXBXGKOMWpdBLoG+MnBOkRXUFjedlUHxK6jOr8bpnadR/n0FYjO742cPzsLge+5BXJcu7V7T1TfdBEEK9sn/5z//waRJky76+Es95qOPPkL37t3Ro0cPvPDCC1i6dCluuukm/POf/2zLskkHEQQBiT0StS6jidriWiiyonUZ5BLoLsYw4Kxynv8fGOCqdsFV7YLepEdMWgz6j8+FEpBRevQIzuzc2eYrydrT0hCTkgJRFFFfX4+CggIMHDjwgo+/1GM8Hg/eeOONUPjIsgxRFCGKIrxeb5vWTjqGIAow28wwx5jhqedjgIOjwoGk7vzdwEuaohYSxxhjcFY6WzQFSsAbQNWZKpzafgpVZ6qR3LMvbnroN7jhrrsQk5raZjX1vOWW0GwM+fn5SExMhP6ce5AOHz6MCRMmhP5+vseca/ny5bj33nthtQZnj54xYwbuuOMOrFy5Evfee2+b1U06liAKSLqanwBQZRWuGhfN3MA5uobEMUVWUPJdCdy17lZtb7AYYE+3IyYlBgGvF8WHDuLsnj1AK4eOJ1x1Ffrdfjt0Bj5neSZ8URUVJUdL4Kp2aV0KAMBityD9mnQuJ4QlQRRIHFMVFae2nbrib3WCKCA6KRpxmXHQGXWoKy7Gya+2wlVdfRk7ETB8+nSYY2KuqBbSuQQ8AZz55ozWZYT0yOkBSU/3JPGKriFxzF3nbpMuBqYyNJQ1oKGsAUarEbEZsRhyz73wu90o+HYfCvfvv+Q+Unr3hp7uDyKXSdJLsCXb0FDeoHUpAABXjQvRSdE0Iz2nqIXEKUVWUHGyot0+yKIkwpZsQ1xmLESdiOr8PJz86it4G87/+4ZPnw4zDcEmreD3+HH2m7NalwEAsCZakdKLphLiFbWQOCWIApzVFxhd1wZURUVdSR3qSupgjjEjNiMZw/5rOrwOB/J2f4PSo0dDj41JTYXeYmm3Wkhk0+l1MNlM8DZoP2rSXevmayYJ0gS1kDjldXiRvy+/Q3+npJdgS7EhLiMOEBgqT5/Cqa+/Rq+RI5Fw1VWhKX4IuRyMMTirnCj5rkTrUgAAXQZ1gclq0roMch4USBxSFRVVZ6tQW6TdMuWWWAviMuNgjjFDEAXqcydXRFVUnNl1BkpA+5tT47rEIb5LPH3B4hC9I5y64M2wHcRd60bRoaLgLBH0lYW0gZhUPkZouqpdNNkqpyiQOKQEFAS8Aa3LAADEpMRQnzu5YqIkIjYjVusyAAA+Jz9z7JGmKJA45K5r3Y2wbS0qPorCiLQZQRQQFa/90ikAWn2zOWlfFEicURWVm/m/YlJiaHgsaTOSTkJMCh/ddo5KB022yiEKJM4wxrgYHgsBiIrj49ssiRxRcVEAB41uT72HBupwiAKJM6IowufWvo/bHGOmiShJm2OMwRyj/Ywfsq9tZ8EnbYMCiTM+t4+LUW3RCdF0/Yi0OUEUYE2wal0GAHC1gCAJokDiCGOMm+tH0YnRdJ8GaXOiKMKWaNO6DACAp8FDvQCcoTMOR3gZ0GCwGGiKftJuRJ0Ig0X7JUx8Dh9UpXVLsZD2QWcdjgiCAK9D+wENvHSpkMhljdf+GPM6tf+skaYokDgT8Gh/Q2x0YjREiQ4N0j5ESUR0UrTWZcDv9lO3NGfo3eAILxdZeehOIZGNi2OMgZsZUUgQBRJH/G6/1iVAb9ZrXQLpJHg41njoIic/okDiBFMZF4FkijbRyCPS7hhjMEVrvwSEp8FDAxs4QoHECVVVubhZzxRtoutHpN2JkshFIPk9fpr5myN05uEID/3ZFruFplQh7U4QBFjs2q9CLPtkLqYyIkEUSJwQIHDRQuLiYjPpFHg41mSfTCPtOELvBCcESUDAp20LiYeLzKRz0fqYU2UVjIe5uggACiRuqLKq+Rx2erOe+tNJh2Eqg96k/ZcgHpZVJ0EUSJyQ/dp31+kMOppQlXQcAdAZdVpXAcVPgcQLCiRO8DCggQKJdCRREqEzcBBI1ELiBgUSJ3iYMkhv0tMIO9JhBEHgosuOh8FEJIgCiQOMMS6+pfFwciCdCw/HXMAXoJvBOUGBxAMWvDFWazz055POhYdjTgkoNJiHExRIHGCMgSnafyAkvaR1CaST4eGYYwrTfIQrCaJA4gBjjIv5tCSd9icH0rnwcMzRfUj8oEDiBA9dBjTCjnQ0bo45Tsro7CiQOKH5RVWBgxpIp8PNMcdJGZ0dBRIntP5g0nBvohXNjz1G3Xa8oEDiAQ+fBQF81EE6FwbqLiMhFEg84CEM6MRAtEDHHDkHBRIPOOgy0LrLkHRedOyRRhRIPBCg/ZosjIO+fNLpCIKgee8ABSI/KJA4IEDg434M+mCSDkbHHDkXBRIHBFGAqNP+rVBl7W/OJZ2LIms/h6MgCtQ7wAntz4IEgihwMYUKD2sykc6Fh7WIdHodBRInKJA4IRkokEjnw8MxJxklfmaM6OQokDih02s/6zEPiwSSzoWHY05v1H4JDBJEgcQJHrrsAl5aF4Z0HMYYFwtT8rAEBgmiQOIED4MaFJ/CxTIYpHNgKuOjy46DL4MkSPuzIAHAxzT8sl/W/AZd0nlQIJGfokDihCBpf1HV7/FDoLlcSAcRBAGyV/tAEiU6DfKC3glOiKKo+UifgCdARwTpOCLgd/u1LUEn0nVTjtDphxOqqsJgMWhdhuYnCNJ58HCs6Qw6LhbHJEEUSBzhIZA89R76xkjaHWMMnjqP1mXQCDvOUCBxQpRELgLJ2+CFqtAUQqR9qYoKT4P2gWS0GjXvKic/okDihCAIMEWbtC4DXoeXBjaQdidAgM/p07oMmG1m7WfaJyH0TnDEaDFqXUKwX5+OCtLeOBjQAARbSIQfdOrhCC/92X6X9icKEtm4OMYEmjaINxRIHGGMQW/S/gPiqHJAVek6EmkfqqrCUeXQugwYzAY6zjlDgcQRxhgXAxtcVS7NV/EkEYz9cIxpjLrr+EOBxBFBFLgIJJ/LRyPtSLtRFRU+l/YDGkxWE83SwBl6NzgiiiIssRatywAAOKucdMMgaXNMZXBWObUuAwBgsploYT7OUCBxxhxj1roEAICjkq4jkbanqiocldpfPwIAYxR12fGGAokzAvjotnPXuenbI2lzgiDAXefWugxIBomLCY1JUxRIvBEAi52DbjsGLk4cJLK469xcDJix2C3UJc0hCiTOiJIIa4JV6zIAAPWl9VBkResySIRQZAX1pfValwEAiIqL4mINMtIUBRKHzDY+riM5q51cfJslEYL9cExxICouSusSyHlQIHGIgcEQpf11JDCgtriWhoCTK6YqKmqLa7n4gqMz6Wi4N6foXeGQIAh8XEcCUFdSB5prlVwx4YdjiQNRsVG0xAqnKJA4JEoirPF8XEdS/ArctW76AJNWY4zBXeuG4ufjemR0YjRdP+IUBRKneLkfCQBqCmuo2460mqqoqCms0boMAMHeB54+W6QpCiROMca4+eB46jxQZQok0jqqrHKxOiwAmO1mau1zjAKJU6IowpZs07qMkOr8agolctlUWUV1frXWZYRYE6y0IB/H6J3hlCAKiE6K1rqMkPqyeigKH9cASPhQFAX1ZXzcewQAtiQbLVnOMQokzpntfHTbgQGVpyrpRtkw9NZbb2H06NGYMGEC5s6di7q6Oni9Xjz55JMYN24c7rjjDjz55JPwer3NtvX7/ViwYAHGjh2LsWPH4sUXXwx9MdmyZQtGjRqFcePG4fDhw6FtnnrqKezYsQOKrKDyVCUXQ70BBCcupiziGgUSx0RRRExyjNZlhDgqHZB9stZlkMuwa9curFixAqtWrcKGDRtw4403YsGCBVi2bBkURcHGjRuxceNG+Hw+vPbaa822X716NWpqarBp0yZs3LgR+/fvx0cffQQAePnll/H222/j+eefx4oVKwAAhw4dgtPpxPDhwyH7ZG4mUgUAe5qd7j/iHL07HGvstuNpktOKUxU04i6MfPfddxg+fDhSUlIAAKNGjcKWLVswePBgPPTQQxBFEZIkoU+fPigpKWm2/f3334+//OUvEEURdXV1aGhoQExM8EuSwWCA2+2Gw+GAXq8HYwxLlizB/PnzoSoqKk5VdOhzvRhREhEVF8XVZ4k0R4HEOcYYouL5mebEXeuGz+mjkUph4rrrrsOuXbtQXFwMAFi3bh0CgQB69uyJbt26AQCKi4uxatUq3H777efdh16vx//8z//g5z//ORISEjBo0CAAwLx58zB37ly8+uqr+M1vfoP3338f2dnZSE1Nhc/pg7uWn8l5o5Oiuek6JBcmMDqzcM9V40LRoSKtywgxRhuRNSCLuj/CxPvvv481a9ZAEARMnjwZf/vb3/Dxxx8jNjYWR44cwezZs3H33Xdj1qxZF91PIBDAM888A8YYXnrppSb/Vl9fj5kzZ+Ltt9/GP974B7Z/uR2ZyZmYPnl6Oz6zlusyqAtMVpPWZZBLoDNKGDDbzZD0/NxZ7nP44KxyUtddGHA6nRgyZAjWr1+PdevW4bbbbgMA2O12bN68GTNmzMDvfve7C4bRvn37cPbsWQDBltLEiRNx9OjRZo/761//ilmzZqGoqAjbvt6GZ37zDBwuBw4eO9h+T66F9GY9DGYO5oYkl0SBFA4YYEvh554kACg/WU7ryYSBiooKTJs2DU5ncJbtZcuW4Y477sAXX3yB559/Hm+88QbGjx9/we137dqFF154AbIsQ1VV/Pvf/8bQoUObPOb48eMoLS3FrbfeCr/XD8UXHIUnCAJ8fl/7PbkWikmNodF1YYK67MKEHJBxesdprvrBo+KikNYvjbruOPf2229j9erVUFUVN9xwAxYsWIAJEyagvr4eycnJocddf/31WLhwId555x0cOXIEixYtgt/vx+LFi7Fnzx6Ioojrr78eTzzxBMzmH29HmD59OhYuXIguWV1Q8l0Jnv/z8zh2+hiy0rLwxINPQCfptHjaId1zukOn17YG0jIUSGFCkRVUnKxAQ3mD1qU0kdonNXj3O4VSp6YqKpxVTpQeK9W6lCYscRak9U2jyVTDBJ1FwoSkkxDfNV7rMpqhrjsCAExlKD9ZrnUZzSR2S6QwCiMUSGFEp9cF7zbniCqrKD1WSgMcOjFV+eEY4GyuQ3OMGQYLDWYIJxRIYUTUiUjomqB1Gc24alxwVDoolDohVVHhqHTAVePSupRmErol0Lx1YYYCKcwYrUYYrUaty2im/PtyBDwBqCqFUmehqir8Hj/KT/DXVWeMNsIUbaKZGcIMBVKYEQQB8V34u5bEVIbCQ4VgCl1P6iyYwlB0qIjLWTsSuyVS6ygMUSCFGUEUEBUXBZ2Rv2Gsil9B0aEi6rrrBFRFReGhQm6WJT+XIcoAc4yZWkdhiAIpHAngspUEAF6HF2UnyiiUIpiqqCg7UQafQ/ubXs8noWsChVGYokAKQ42ryerNeq1LOS9HhQN1JXUUShFIVVTUldTBUcHPshLn0pv1wVm9qbsuLFEghSlBEJDcM/nSD9RI5elKeOo9FEoRRFVUeOo9qDxdqXUpF0Sto/BGgRSmBFGA2Wbm7r6kcxUfKYbX6aVQigCqosLr9KL4SLHWpVyQKdoEa4KVWkdhjAIpjImSGGwlcfr5YypD0cEi+Fw+CqUwpioqfC4fig4WcT0rR0rvFAqjMEeBFOZ0el1wNmNOMZWh8GAh/G4/hVIYUhUVfrcfhQcLuQ6jmNQY6I166q4LcxRIYU7UiUi8KhGijt+3kikMBQcKqKUUZlRFhc/pQ8H+Aq7vLxN1IhK78/0ZIC1D72AEEASByymFzsUUhsIDhfA66JpSOFAVFV6Hl/uWEQAk9UiillGEoECKAKIkBrssOB0G3qjxmhKtNsu3xqUkeL9mBASnCIpOjKblTyIEvYsRQhAEpPRK0bqMS2KMofRYKaryqiiUOKQqKqrOVqH0WCmXUwL9VGrvVBrIEEEokCKEIAowRZtgT7drXUqL1BbWovhIMVRZDYsTX6RjjEGRFRQfKUZtUa3W5bRITGoM9CYayBBJKJAiiCgFBziEyxow7lo38vblQfbJ1FrSkKqokH0y8vflw13r1rqcFpEMUnAgA3XVRRR6NyOMIApI65fG7b1JPxXwBJC3N48GO2ikcfBC3p48BDwBrctpsfR+6dRVF4EERv0lEUdVVNQW1aLqbJXWpVyWuMw4xHeNhyAK1A3TzhhjYCpDVV4VagvDo4uukT3DjsRu1DqKRBRIEUpVVBQeLIS3wat1KZfFYDEgrW8a9GY9nXDaiaoEF9YrPVoKv9uvdTmXxRBlQJfru9CxEaEokCKY7JNxZvcZrm9qvJC4rDjEd6HWUlsK51YREOyO7jq4Kw1kiGAUSBFMVVQ4Kh0oO16mdSmtYrAYkNYvDXoTtZaulKqoCHgCKDlaEnatokYpvVPonqMIR4EU4VRFRemxUjirnFqX0joCEJseG5yJQgCdjC6TqgSH1VflVaGuqE7rclrNlmxDcs9kev8jHL27EU6URKT2SQ2boeDNMKC2qBand55GbVEtVEWFqtJovEtRVTU0uOXMzjNhHUYGi+Gyw6ioqAi9evXC9u3bm/x85MiRKCoqavF+evXqhQkTJjT57+DBgy3e/nzuv/9+fPbZZ6G/v/TSSxg4cCD8/h9briNGjLhonU899RQOHz58RXXwSKd1AaT9CaKAzOsycXbPWahyeJ7MG2cQqC2uRULXBNiSbcEWk0jfqc6lqirAgIbyBlTlVUHxK1qXdEVESUTGtRmtGuKt1+vxzDPPYOPGjbBara2uYcOGDa3e9nyys7Oxb98+3HbbbQCAHTt2YMCAAdi3bx+GDRuG/Px8WCwWZGRkXHAfixYtatOaeEGf5k5AEASI+uAHO1zuT7oQxa+g/Pty5O3Jg6vGFeyS4ny+tY7AVAZVUeGqcSFvTx7Kvy8P+zCCAGT0z4BkkFo1iCEpKQnDhw/HSy+9dN5/X758OcaOHYvx48fjxRdfhKK0/PVyuVyYP38+Jk2ahAkTJmDTpk0AAEVR8MILL2DixInIzc3FP//5z2bbDhs2DPv37wcAlJeXw2AwYPTo0di2bRsAYO/evcjJyQEAfPTRR7jrrruQm5uL22+/Hd9++y0AYNq0afjmm28we/ZsfPLJJ6F9T5o0CUePHkV+fj7uv/9+TJw4EVOmTMHRo0cBAE888QRmzZqFMWPGYMuWLS1+vh2FAqmTEEURRqsRyVfzu+z55Qh4Ayg5UoK8vXmoK62DqqhQ5DA/AbeCIitQFRV1pXXI25uHkiMlCHjD5wbXi0ntkwqj1XhF142eeOIJbNu2rVnX3datW7FlyxasXbsW69evR35+Pt59993z7uPc7rrFixcDAJYtW4Z+/fph3bp1WL16NZYvX47CwkK89957AID169fjgw8+wOeff469e/c22V+/fv1QUFAAn8+Hbdu2IScnBzk5Oc0CSVVVvPvuu1i+fDk2btyImTNn4n//93+b1bZ582YAQF5eHnw+H/r27Yv58+dj3rx5WL9+PZ577jk8+uijoW3sdjs++ugjjBw5stWva3uhLrtORJRE2JJt8Lv9YTNf2aUEPAFUnKxA5elK2JJsiMuKg86ogyAIEXsnP1MZGGOQfTJqCmrQUNEQca3EhG4JsMZbr3gQg9VqxXPPPRfqumu0a9cu3HHHHTCbzQCAyZMn48MPP8S9997bbB/n67LbsWMHvF4v1q5dCwBwu904efIkdu7ciWPHjmHXrl2hn584cQKDBg0KbStJEq677jocPnwY27Ztw7333ovMzEx4vV7U19dj//79eOqppyCKIl555RVs2bIFZ8+exe7du5t1Ud9000344x//CKfTiU2bNiE3NxculwtHjhzBk08+GXqc2+1GbW3wM9+/f//WvpztjgKpkxElEQndEhDwBsJ35N15MJWhvqwe9WX1MEWbEJcZh6iEKDCVQdJJWpfXJhRZgSAKcFW5UFNYA68jvG56bqmY1BjEZsS22Yi6ESNGNOu6O9/AGFmWW7xPVVWxZMkS9OvXDwBQVVWFmJgYrF27FvPmzcOoUaMAADU1NYiKimq2fXZ2Nr799lscOnQIS5YsARDsyvv8888RGxsLq9UKl8uFO++8E7m5uRg8eDB69eqF1atXN9mPwWDALbfcgi1btuDjjz/Ga6+9BlVVYTAYmgRpWVkZ7PbgxMsmk6nFz7OjUZddJ9Q48s4Uze+BeSW8Di9Kjpbg9PbTKDtRBkelI9SlF053OTTOwB26n+xEGU5vP42SoyURG0ZRcVFI6pHU5sO7G7vuKioqAAQDYfPmzfB6vZBlGWvXrkV2dnaL95ednY133nkHAFBRUYHc3FyUlpYiOzsb7733HgKBAFwuF+655x4cOHCg2fbDhg3Dhg0b0LNnT+h0wXZBTk4OVq5cGbp+lJeXB0EQMGvWLAwdOhSffvrpea9zTZgwAStXroTdbkd6ejqio6PRtWvXUCBt3779vC0/HlELqZMSJREZ/TNQsL8gbG+UvBRVUeGsdMJZ6QQEwGwzw5pohS3RBlEngjH+Wk+KrEAQBKiyiobKBjgrnfA0eIDwydFWM1qNSOuX1i73GjV23f3qV78CANxyyy04duwYJk+eDFmWMWLECEydOrXF+5s9ezb+8Ic/YNy4cVAUBfPmzUNWVhbuvvtu5OfnY+LEiZBlGZMmTcLQoUObbd+zZ0/U1dXhnnvuCf0sOzsbc+bMwfDhwwEAvXv3Rp8+fTBmzBgIgoARI0Zg3759zfZ1ww03wOFwYMqUKaGfLVmyBH/4wx/w+uuvQ6/X4y9/+UtYzG5BN8Z2YowxqLIa0aF0IQaLASabCWabGeYYMwxmQ6gbp6NCqnEQhiiK8Hv88NR74GnwwNvg7XTvh86kQ9cbukLS8/UFgXQsCqROLhRKBwrgd3Wuk2ATAmC0GGGMNsJsM8NkM0Fn0IXC6dxrDqIoXnLABFNZs22AYAjJfhneBi88DR74HD743L5O0QK6EL1Jj6yBWZD0UsQORCEtQ4FEgqGkqCjcXwify6d1OdwRJRE6oy4YUAYJOmMwqARRaHJzbuNNqUwNXvuRfTIUfzCAaBHC8zNYDMgamAVRunTIk8hHgUQAUCiRjme0GpE5IDMYRmFwfYO0PwokEhIKpQOF8DkplEj7MdlMyOifQWFEmqBAIs0oskKhRNqNxW5B+rXpNHM3aYYCiZyXKqsoOlwET71H61JIBImKi2q3od0k/FEgkQtSFRUVJytQX1avdSkkAkQnRiOldwqFEbkgCiRyUaqior6sHhWnKjr10GRyZeK7xiMuM47CiFwUBRK5JFVR4XV4UXykOGzXUyLaECURaf3SYI4xUxiRS6JAIi2iKiqUgILCg4UIeCJjeQPSvgwWQ3A9I71EYURahAKJtBhjDExhKP6uGO5at9blEI5ZE61I7Z0KQRRoWDdpMQokctlCy4lHyJpKpG0ldk+EPc1OrSJy2SiQSKuoigpPgwelx0rDf6ls0iYkvYT0a9KveJVX0nlRIJFWa5xAtPxEORyVDq3LIRoy281I65sGUSc2W9WUkJaiQCJXTFVUuGpdKDteRqPwOhlBFJDUIwm2ZBu1isgVo0AibUJVVDCVofRYKVw1Lq3LIR3AbDMjtV8qJB2NoiNtgwKJtKnG5bbLT5aDKXRoRSJREpHYPZFaRaTNUSCRNqcqKlRFRemxUhoeHmGsCVak9EqBIAoURqTNUSCRdqMqKjz1HpSfLKebacOczqhDSq8UmnGBtCsKJNKumMrAGENdSR2q86pp1dQwI+pEJHRNQExqDARBoFVdSbuiQCIdQlVUMMZQdaYKdaV1NFEr5wRJQFxmHOIy4wCAWkWkQ1AgkQ6lysHrSxWnKujeJR4JgD3NjoRuCRAEuk5EOhYFEtGEKquQ/TIqT1fCWe3UuhwCwJZsQ2L3RAiiAEknaV0O6YQokIimFFkBUxiq86tRX1YPptLh2KEEIDohGglXJUDSSxRERFMUSIQLjYMd6orrUFtcC9kna1xRZJMMEmLTY2FPswe75nTUNUe0R4FEuNIYTK4aF6rzq+Fz+jSuKLKYY8yIy4qDxW4BQIMVCF8okAiXGoeL+91+1BTUwFntpO68VhIkAbYkG+Ky4qDT6yBItEYR4RMFEuGeIisQBAGuWhcayhrgqnFROF2KEGwN2ZJssCXbAAbqliPco0AiYSUUTtUu1JfVw1XronuafiCIAiyxFtiSbLDGW8HAIIoi3cxKwgYFEglLjDGoigpBEOCscqK+rB6eOg862+EsSiKi4qNgS7bBYreAMQZREqlLjoQlCiQS9hrDSRRF+Fw+uGpccNe54WnwRN6M4wJgjDKGuuOM0UYwldFwbRIRKJBIxGlcyVYURfg9frhqXXDXuuGp94TdAoKiToQp2gRzjBlRcVEwWoMBRLMokEhEgUQi3rkBJftleZs4tAAAASVJREFUeB1eeB1e+N1++Fw+BLwBza9DCaIAvUkPvUkPQ5QBFrsFpmgTJJ0Uqp2uBZFIR4FEOqXGkBIQnMFaCSgIeAPBkHIHQ0oJKMG5936Yf0+V1VZdoxLE4O+Q9BL05h9Cx2SAIcoAvUkPnVEHURKD92Ax0FpDpNOiQCLkJxrDCgzAD42Sc5deYErw3xtDCgJCLRhBFEKPFQQhtD1jDFCD/z/3cYSQH1EgEUII4QL1CxBCCOECBRIhhBAuUCARQgjhAgUSIYQQLlAgEUII4QIFEiGEEC5QIBFCCOECBRIhhBAuUCARQgjhAgUSIYQQLlAgEUII4QIFEiGEEC5QIBFCCOECBRIhhBAuUCARQgjhAgUSIYQQLlAgEUII4QIFEiGEEC78f2F03IwLFKeKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fee Waivers for SAT Test in Indiana Piecharts\n",
    "import matplotlib.pyplot as plt\n",
    "#Pie chart\n",
    "labels = ['Fee Waiver', 'No Fee Waiver']\n",
    "sizes = [12279,33343]\n",
    "#colors\n",
    "colors = ['rosybrown','thistle']\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, colors = colors, labels=labels, autopct='%1.1f%%', startangle=90)\n",
    "#draw circle\n",
    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle) \n",
    "\n",
    "#Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax1.axis('equal')\n",
    "ax1.title.set_text('Fee Waivers for SAT in Indiana, 2017')\n",
    "plt.tight_layout()\n",
    "\n",
    "#Second Pie Chart\n",
    "labels = ['Fee Waiver', 'No Fee Waiver']\n",
    "sizes_2 = [270,3225]\n",
    "#colors\n",
    "colors = ['rosybrown','thistle']\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.pie(sizes_2, colors = colors, labels=labels, autopct='%1.1f%%', startangle=130)\n",
    "#draw circle\n",
    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "fig = plt.gcf()\n",
    "fig.gca().add_artist(centre_circle) \n",
    "\n",
    "#Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax2.axis('equal')  \n",
    "ax2.title.set_text('Fee Waivers for SAT in Tennessee, 2017')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}