final_project/universities.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69a659f6",
   "metadata": {},
   "source": [
    "# U.S. Universities - Regional Performance\n",
    "\n",
    "By [Sarina Noone](https://www.linkedin.com/in/sarinasnoone/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d49a04",
   "metadata": {},
   "source": [
    "Using datasets from the Integrated Postsecondary Education Data System, or IPEDS, I will explore the range of American 4-year universities (public, private or for-profit), their distribution across the country and look at one basic indicator of student outcomes: years spent in attaining a bachelor's degree. \n",
    "\n",
    "Hypothesis: Americans tend to hold biases towards elite, east coast universities because of their historical excellence and the reputation of the Ivy League. While based on density and presence of major employers in coastal cities probably leads to a higher number of institutions of higher education, this may not be linked to school performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f0e87cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea5c3b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import first dataset with all university data incl name, state, geo region\n",
    "allunis = pd.read_csv('/home/jovyan/python-public-policy/Sarina_Noone_Final/hd2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46c68eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6440"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total number of universities in dataset?\n",
    "allunis['UNITID'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd4f0b34",
   "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>UNITID</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>IALIAS</th>\n",
       "      <th>ADDR</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>OBEREG</th>\n",
       "      <th>CHFNM</th>\n",
       "      <th>...</th>\n",
       "      <th>CBSATYPE</th>\n",
       "      <th>CSA</th>\n",
       "      <th>NECTA</th>\n",
       "      <th>COUNTYCD</th>\n",
       "      <th>COUNTYNM</th>\n",
       "      <th>CNGDSTCD</th>\n",
       "      <th>LONGITUD</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>DFRCGID</th>\n",
       "      <th>DFRCUSCG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>AAMU</td>\n",
       "      <td>4900 Meridian Street</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>35762</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Dr. Andrew Hugine, Jr.</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>290</td>\n",
       "      <td>-2</td>\n",
       "      <td>1089</td>\n",
       "      <td>Madison County</td>\n",
       "      <td>105</td>\n",
       "      <td>-86.568502</td>\n",
       "      <td>34.783368</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td></td>\n",
       "      <td>Administration Bldg Suite 1070</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35294-0110</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Ray L. Watts</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>142</td>\n",
       "      <td>-2</td>\n",
       "      <td>1073</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>107</td>\n",
       "      <td>-86.799345</td>\n",
       "      <td>33.505697</td>\n",
       "      <td>95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Southern Christian University  Regions University</td>\n",
       "      <td>1200 Taylor Rd</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>36117-3553</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Michael C.Turner</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>388</td>\n",
       "      <td>-2</td>\n",
       "      <td>1101</td>\n",
       "      <td>Montgomery County</td>\n",
       "      <td>102</td>\n",
       "      <td>-86.174010</td>\n",
       "      <td>32.362609</td>\n",
       "      <td>126</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>UAH  University of Alabama Huntsville</td>\n",
       "      <td>301 Sparkman Dr</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>35899</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Darren Dawson</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>290</td>\n",
       "      <td>-2</td>\n",
       "      <td>1089</td>\n",
       "      <td>Madison County</td>\n",
       "      <td>105</td>\n",
       "      <td>-86.640449</td>\n",
       "      <td>34.724557</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td></td>\n",
       "      <td>915 S Jackson Street</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>36104-0271</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Quinton T. Ross</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>388</td>\n",
       "      <td>-2</td>\n",
       "      <td>1101</td>\n",
       "      <td>Montgomery County</td>\n",
       "      <td>107</td>\n",
       "      <td>-86.295677</td>\n",
       "      <td>32.364317</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID                               INSTNM  \\\n",
       "0  100654             Alabama A & M University   \n",
       "1  100663  University of Alabama at Birmingham   \n",
       "2  100690                   Amridge University   \n",
       "3  100706  University of Alabama in Huntsville   \n",
       "4  100724             Alabama State University   \n",
       "\n",
       "                                              IALIAS  \\\n",
       "0                                               AAMU   \n",
       "1                                                      \n",
       "2  Southern Christian University  Regions University   \n",
       "3              UAH  University of Alabama Huntsville   \n",
       "4                                                      \n",
       "\n",
       "                             ADDR        CITY STABBR         ZIP  FIPS  \\\n",
       "0            4900 Meridian Street      Normal     AL       35762     1   \n",
       "1  Administration Bldg Suite 1070  Birmingham     AL  35294-0110     1   \n",
       "2                  1200 Taylor Rd  Montgomery     AL  36117-3553     1   \n",
       "3                 301 Sparkman Dr  Huntsville     AL       35899     1   \n",
       "4            915 S Jackson Street  Montgomery     AL  36104-0271     1   \n",
       "\n",
       "   OBEREG                   CHFNM  ... CBSATYPE  CSA  NECTA COUNTYCD  \\\n",
       "0       5  Dr. Andrew Hugine, Jr.  ...        1  290     -2     1089   \n",
       "1       5            Ray L. Watts  ...        1  142     -2     1073   \n",
       "2       5        Michael C.Turner  ...        1  388     -2     1101   \n",
       "3       5           Darren Dawson  ...        1  290     -2     1089   \n",
       "4       5         Quinton T. Ross  ...        1  388     -2     1101   \n",
       "\n",
       "            COUNTYNM  CNGDSTCD   LONGITUD   LATITUDE DFRCGID DFRCUSCG  \n",
       "0     Madison County       105 -86.568502  34.783368     109        1  \n",
       "1   Jefferson County       107 -86.799345  33.505697      95        1  \n",
       "2  Montgomery County       102 -86.174010  32.362609     126        2  \n",
       "3     Madison County       105 -86.640449  34.724557      99        2  \n",
       "4  Montgomery County       107 -86.295677  32.364317     118        1  \n",
       "\n",
       "[5 rows x 73 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allunis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7c1d021f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allunis['SECTOR'].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36386b7",
   "metadata": {},
   "source": [
    "For the sake of this exploration, we'll limit our scope to \"traditional\" four-year college experiences. To do this, I have identified the appropriate sector codes in the IPEDS data set to filter out all community colleges, trade schools, certificate programs and graduate/professional degree granting insitutions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8aaad48",
   "metadata": {},
   "outputs": [],
   "source": [
    "sectorcodes = [1,2,3]\n",
    "fouryearunis = allunis[allunis.SECTOR.isin(sectorcodes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bb3c023",
   "metadata": {},
   "outputs": [],
   "source": [
    "#total number of four year universities to confirm filtered list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e5478a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2846"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fouryearunis['UNITID'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4a74c67",
   "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>UNITID</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>OBEREG</th>\n",
       "      <th>LONGITUD</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>SECTOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.568502</td>\n",
       "      <td>34.783368</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.799345</td>\n",
       "      <td>33.505697</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.174010</td>\n",
       "      <td>32.362609</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.640449</td>\n",
       "      <td>34.724557</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.295677</td>\n",
       "      <td>32.364317</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID                               INSTNM        CITY STABBR  OBEREG  \\\n",
       "0  100654             Alabama A & M University      Normal     AL       5   \n",
       "1  100663  University of Alabama at Birmingham  Birmingham     AL       5   \n",
       "2  100690                   Amridge University  Montgomery     AL       5   \n",
       "3  100706  University of Alabama in Huntsville  Huntsville     AL       5   \n",
       "4  100724             Alabama State University  Montgomery     AL       5   \n",
       "\n",
       "    LONGITUD   LATITUDE  SECTOR  \n",
       "0 -86.568502  34.783368       1  \n",
       "1 -86.799345  33.505697       1  \n",
       "2 -86.174010  32.362609       2  \n",
       "3 -86.640449  34.724557       1  \n",
       "4 -86.295677  32.364317       1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fouryearunis_cleaned = fouryearunis[[\"UNITID\",\"INSTNM\",\"CITY\",\"STABBR\",\"OBEREG\",\"LONGITUD\",\"LATITUDE\",\"SECTOR\"]]\n",
    "fouryearunis_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f86b1d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to contextualize sector number, add a column explaining sector code\n",
    "\n",
    "def label_sectortype(row):\n",
    "    if row['SECTOR']==1:\n",
    "        return \"Public 4-Year\"\n",
    "    elif row['SECTOR']==2:\n",
    "        return \"Private 4-Year\"\n",
    "    elif row['SECTOR']==3:\n",
    "        return \"For-Profit 4-Year\"\n",
    "    else:\n",
    "        return 'Invalid Sector'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f215108c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1558/675621173.py:2: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  fouryearunis_cleaned['sectortype'] = fouryearunis_cleaned.apply(label_sectortype, axis=1)\n"
     ]
    },
    {
     "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>UNITID</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>OBEREG</th>\n",
       "      <th>LONGITUD</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>SECTOR</th>\n",
       "      <th>sectortype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.568502</td>\n",
       "      <td>34.783368</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.799345</td>\n",
       "      <td>33.505697</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.174010</td>\n",
       "      <td>32.362609</td>\n",
       "      <td>2</td>\n",
       "      <td>Private 4-Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.640449</td>\n",
       "      <td>34.724557</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.295677</td>\n",
       "      <td>32.364317</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID                               INSTNM        CITY STABBR  OBEREG  \\\n",
       "0  100654             Alabama A & M University      Normal     AL       5   \n",
       "1  100663  University of Alabama at Birmingham  Birmingham     AL       5   \n",
       "2  100690                   Amridge University  Montgomery     AL       5   \n",
       "3  100706  University of Alabama in Huntsville  Huntsville     AL       5   \n",
       "4  100724             Alabama State University  Montgomery     AL       5   \n",
       "\n",
       "    LONGITUD   LATITUDE  SECTOR      sectortype  \n",
       "0 -86.568502  34.783368       1   Public 4-Year  \n",
       "1 -86.799345  33.505697       1   Public 4-Year  \n",
       "2 -86.174010  32.362609       2  Private 4-Year  \n",
       "3 -86.640449  34.724557       1   Public 4-Year  \n",
       "4 -86.295677  32.364317       1   Public 4-Year  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying that label to the dataset \n",
    "fouryearunis_cleaned['sectortype'] = fouryearunis_cleaned.apply(label_sectortype, axis=1)\n",
    "fouryearunis_cleaned.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7175eb64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          sectortype  counts\n",
      "0  For-Profit 4-Year     367\n",
      "1     Private 4-Year    1673\n",
      "2      Public 4-Year     806\n"
     ]
    }
   ],
   "source": [
    "#to get a sense of the type of universities represented in this set\n",
    "\n",
    "fouryear_bytype = fouryearunis_cleaned.groupby('sectortype').UNITID.size().reset_index(name='counts')\n",
    "print(fouryear_bytype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "010a2262",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b6dbe755",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/pdf": "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",
      "text/html": [
       "<div>                            <div id=\"23a1ccc7-46fc-434a-baf4-dfbf26f7367a\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"23a1ccc7-46fc-434a-baf4-dfbf26f7367a\")) {                    Plotly.newPlot(                        \"23a1ccc7-46fc-434a-baf4-dfbf26f7367a\",                        [{\"alignmentgroup\":\"True\",\"hovertemplate\":\"sectortype=%{x}<br>counts=%{y}<extra></extra>\",\"legendgroup\":\"\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"\",\"offsetgroup\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"textposition\":\"auto\",\"x\":[\"For-Profit 4-Year\",\"Private 4-Year\",\"Public 4-Year\"],\"xaxis\":\"x\",\"y\":[367,1673,806],\"yaxis\":\"y\",\"type\":\"bar\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"sectortype\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"counts\"}},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('23a1ccc7-46fc-434a-baf4-dfbf26f7367a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.bar(fouryear_bytype, x='sectortype', y='counts')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47736fde",
   "metadata": {},
   "source": [
    "It's not surprising to see that the number of Private Four-Year universities far exceeds the number of for-profit and public universities combined. \n",
    "\n",
    "Next up, we'll see how these institutions are spread across the country. To do this, I will add context to the IPEDS dataset's OBEREG data flag to indicate which part of the country is represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "545d0a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to contextualize regions in column OBEREG, add a column with more detail\n",
    "\n",
    "def label_region(row):\n",
    "    if row['OBEREG']==1:\n",
    "        return \"New England\"\n",
    "    elif row['OBEREG']==2:\n",
    "        return \"Mid Atlantic\"\n",
    "    elif row['OBEREG']==3:\n",
    "        return \"Great Lakes\"\n",
    "    elif row['OBEREG']==4:\n",
    "        return \"Plains\"\n",
    "    elif row['OBEREG']==5:\n",
    "        return \"Southeast\"\n",
    "    elif row['OBEREG']==6:\n",
    "        return \"Southwest\"\n",
    "    elif row['OBEREG']==7:\n",
    "        return \"Rocky Mountains\"\n",
    "    elif row['OBEREG']==8:\n",
    "        return \"Far West\"\n",
    "    elif row['OBEREG']==9:\n",
    "        return \"US Territories\"\n",
    "    else:\n",
    "        return 'N/A or Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6dea984",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1558/1056732486.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "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>UNITID</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>OBEREG</th>\n",
       "      <th>LONGITUD</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>SECTOR</th>\n",
       "      <th>sectortype</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.568502</td>\n",
       "      <td>34.783368</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.799345</td>\n",
       "      <td>33.505697</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.174010</td>\n",
       "      <td>32.362609</td>\n",
       "      <td>2</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.640449</td>\n",
       "      <td>34.724557</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.295677</td>\n",
       "      <td>32.364317</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID                               INSTNM        CITY STABBR  OBEREG  \\\n",
       "0  100654             Alabama A & M University      Normal     AL       5   \n",
       "1  100663  University of Alabama at Birmingham  Birmingham     AL       5   \n",
       "2  100690                   Amridge University  Montgomery     AL       5   \n",
       "3  100706  University of Alabama in Huntsville  Huntsville     AL       5   \n",
       "4  100724             Alabama State University  Montgomery     AL       5   \n",
       "\n",
       "    LONGITUD   LATITUDE  SECTOR      sectortype     region  \n",
       "0 -86.568502  34.783368       1   Public 4-Year  Southeast  \n",
       "1 -86.799345  33.505697       1   Public 4-Year  Southeast  \n",
       "2 -86.174010  32.362609       2  Private 4-Year  Southeast  \n",
       "3 -86.640449  34.724557       1   Public 4-Year  Southeast  \n",
       "4 -86.295677  32.364317       1   Public 4-Year  Southeast  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying that label to the dataset \n",
    "fouryearunis_cleaned['region'] = fouryearunis_cleaned.apply(label_region, axis=1)\n",
    "fouryearunis_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d5d31d12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             region         sectortype  UNITID\n",
      "0          Far West  For-Profit 4-Year      87\n",
      "1          Far West     Private 4-Year     216\n",
      "2          Far West      Public 4-Year     110\n",
      "3       Great Lakes  For-Profit 4-Year      26\n",
      "4       Great Lakes     Private 4-Year     264\n",
      "5       Great Lakes      Public 4-Year     107\n",
      "6      Mid Atlantic  For-Profit 4-Year      39\n",
      "7      Mid Atlantic     Private 4-Year     364\n",
      "8      Mid Atlantic      Public 4-Year     120\n",
      "9      N/A or Other      Public 4-Year       7\n",
      "10      New England  For-Profit 4-Year       5\n",
      "11      New England     Private 4-Year     138\n",
      "12      New England      Public 4-Year      44\n",
      "13           Plains  For-Profit 4-Year      26\n",
      "14           Plains     Private 4-Year     172\n",
      "15           Plains      Public 4-Year      60\n",
      "16  Rocky Mountains  For-Profit 4-Year      25\n",
      "17  Rocky Mountains     Private 4-Year      34\n",
      "18  Rocky Mountains      Public 4-Year      45\n",
      "19        Southeast  For-Profit 4-Year      94\n",
      "20        Southeast     Private 4-Year     340\n",
      "21        Southeast      Public 4-Year     199\n",
      "22        Southwest  For-Profit 4-Year      52\n",
      "23        Southwest     Private 4-Year      96\n",
      "24        Southwest      Public 4-Year      93\n",
      "25   US Territories  For-Profit 4-Year      13\n",
      "26   US Territories     Private 4-Year      49\n",
      "27   US Territories      Public 4-Year      21\n"
     ]
    }
   ],
   "source": [
    "fouryearunis_fullset = fouryearunis_cleaned.groupby(['region','sectortype'])['UNITID'].agg('count').reset_index()\n",
    "print(fouryearunis_fullset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6ef31dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": "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",
      "text/html": [
       "<div>                            <div id=\"50d8b6db-cf99-432f-977a-6cf385f505f5\" class=\"plotly-graph-div\" style=\"height:400px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"50d8b6db-cf99-432f-977a-6cf385f505f5\")) {                    Plotly.newPlot(                        \"50d8b6db-cf99-432f-977a-6cf385f505f5\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"sum\",\"hovertemplate\":\"sectortype=For-Profit 4-Year<br>region=%{x}<br>sum of UNITID=%{y}<extra></extra>\",\"legendgroup\":\"For-Profit 4-Year\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"For-Profit 4-Year\",\"offsetgroup\":\"For-Profit 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[87,26,39,5,26,25,94,52,13],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"sum\",\"hovertemplate\":\"sectortype=Private 4-Year<br>region=%{x}<br>sum of UNITID=%{y}<extra></extra>\",\"legendgroup\":\"Private 4-Year\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Private 4-Year\",\"offsetgroup\":\"Private 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[216,264,364,138,172,34,340,96,49],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"sum\",\"hovertemplate\":\"sectortype=Public 4-Year<br>region=%{x}<br>sum of UNITID=%{y}<extra></extra>\",\"legendgroup\":\"Public 4-Year\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Public 4-Year\",\"offsetgroup\":\"Public 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"N/A or Other\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[110,107,120,7,44,60,45,199,93,21],\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"region\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"sum of UNITID\"}},\"legend\":{\"title\":{\"text\":\"sectortype\"},\"tracegroupgap\":0},\"margin\":{\"t\":60},\"barmode\":\"group\",\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('50d8b6db-cf99-432f-977a-6cf385f505f5');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "df = fouryearunis_fullset\n",
    "fig = px.histogram(df, x=\"region\", y=\"UNITID\",\n",
    "             color='sectortype', barmode='group',\n",
    "             height=400)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350f3dd7",
   "metadata": {},
   "source": [
    "I was honestly surprised to find that the Southeast has nearly the same number of private 4-year universities as the Mid Atlantic region, and almost twice as many public universities. The Great Lakes region surprised me at first, but then I remembered that includes all Chicago/IL schools, Wisconsin, Michigan, etc. To represent these findings a little more simply, I'll run some sums by region next. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8a7c0cff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region\n",
      "Southeast          633\n",
      "Mid Atlantic       523\n",
      "Far West           413\n",
      "Great Lakes        397\n",
      "Plains             258\n",
      "Southwest          241\n",
      "New England        187\n",
      "Rocky Mountains    104\n",
      "US Territories      83\n",
      "N/A or Other         7\n",
      "Name: UNITID, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "total_regional =  fouryearunis_cleaned.groupby(['region'],sort=False).count()\n",
    "sorted_regional = total_regional.sort_values('UNITID',ascending=False)['UNITID']\n",
    "print(sorted_regional)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a651bec8",
   "metadata": {},
   "source": [
    "Now that we have established a sense of where American universities are located and the range of four year institutions, we'll look broadly at the number of students they serve and what kind of outcomes they generally have. To do so, I'll import a new data set from IPEDS that focuses on student outcomes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ccd1118",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import second dataset with university outcome measures such as enrollment, degree attainment\n",
    "uni_outcomes = pd.read_csv('/home/jovyan/python-public-policy/Sarina_Noone_Final/om2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb06fcef",
   "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>UNITID</th>\n",
       "      <th>OMCHRT</th>\n",
       "      <th>XOMRCHRT</th>\n",
       "      <th>OMRCHRT</th>\n",
       "      <th>XOMEXCLS</th>\n",
       "      <th>OMEXCLS</th>\n",
       "      <th>XOMACHRT</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>XOMCERT4</th>\n",
       "      <th>OMCERT4</th>\n",
       "      <th>...</th>\n",
       "      <th>XOMAWDP8</th>\n",
       "      <th>OMAWDP8</th>\n",
       "      <th>XOMENRTP</th>\n",
       "      <th>OMENRTP</th>\n",
       "      <th>XOMENRYP</th>\n",
       "      <th>OMENRYP</th>\n",
       "      <th>XOMENRAP</th>\n",
       "      <th>OMENRAP</th>\n",
       "      <th>XOMENRUP</th>\n",
       "      <th>OMENRUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>10</td>\n",
       "      <td>R</td>\n",
       "      <td>969</td>\n",
       "      <td>R</td>\n",
       "      <td>4</td>\n",
       "      <td>R</td>\n",
       "      <td>965</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>30.0</td>\n",
       "      <td>R</td>\n",
       "      <td>32.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2.0</td>\n",
       "      <td>R</td>\n",
       "      <td>30.0</td>\n",
       "      <td>R</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100654</td>\n",
       "      <td>11</td>\n",
       "      <td>R</td>\n",
       "      <td>788</td>\n",
       "      <td>R</td>\n",
       "      <td>4</td>\n",
       "      <td>R</td>\n",
       "      <td>784</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>28.0</td>\n",
       "      <td>R</td>\n",
       "      <td>32.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2.0</td>\n",
       "      <td>R</td>\n",
       "      <td>30.0</td>\n",
       "      <td>R</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100654</td>\n",
       "      <td>12</td>\n",
       "      <td>R</td>\n",
       "      <td>181</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>181</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>39.0</td>\n",
       "      <td>R</td>\n",
       "      <td>33.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2.0</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100654</td>\n",
       "      <td>20</td>\n",
       "      <td>R</td>\n",
       "      <td>106</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>105</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>14.0</td>\n",
       "      <td>R</td>\n",
       "      <td>34.0</td>\n",
       "      <td>R</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>34.0</td>\n",
       "      <td>R</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100654</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>80</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>79</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>13.0</td>\n",
       "      <td>R</td>\n",
       "      <td>34.0</td>\n",
       "      <td>R</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>34.0</td>\n",
       "      <td>R</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID  OMCHRT XOMRCHRT  OMRCHRT XOMEXCLS  OMEXCLS XOMACHRT  OMACHRT  \\\n",
       "0  100654      10        R      969        R        4        R      965   \n",
       "1  100654      11        R      788        R        4        R      784   \n",
       "2  100654      12        R      181        R        0        R      181   \n",
       "3  100654      20        R      106        R        1        R      105   \n",
       "4  100654      21        R       80        R        1        R       79   \n",
       "\n",
       "  XOMCERT4  OMCERT4  ... XOMAWDP8  OMAWDP8 XOMENRTP  OMENRTP XOMENRYP  \\\n",
       "0        R        0  ...        R     30.0        R     32.0        R   \n",
       "1        R        0  ...        R     28.0        R     32.0        R   \n",
       "2        R        0  ...        R     39.0        R     33.0        R   \n",
       "3        R        0  ...        R     14.0        R     34.0        R   \n",
       "4        R        0  ...        R     13.0        R     34.0        R   \n",
       "\n",
       "   OMENRYP XOMENRAP  OMENRAP XOMENRUP  OMENRUP   \n",
       "0      2.0        R     30.0        R      38.0  \n",
       "1      2.0        R     30.0        R      40.0  \n",
       "2      2.0        R     31.0        R      28.0  \n",
       "3      0.0        R     34.0        R      51.0  \n",
       "4      0.0        R     34.0        R      53.0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_outcomes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3d243f",
   "metadata": {},
   "source": [
    "Reviewing the IPEDS variable list and descriptions, I'm most interested in looking at the total number of students an institution serves. The relevant variable is OMCHRT, where a value of 50 = total entering students; 51 = Total entering Pell Grant recipients; and 52 = total entering non-Pell Grant recipients. While it would definitely be interesting to explore different outcomes for students based on their financial aid status, for the sake of this assignment, I'll use the total number of students entering in a cohort (OMCHRT = 50). The value in OMACHRT is the number of students who fit the descriptor in OMCHRT. \n",
    "\n",
    "To assess outcomes, we'll look at data in columns OMBACH4, OMBACH6 and OMNOAWD, which represent, respectively, the number of students who earned a bachelor's degree within four years, within six years or who at 8 years have not earned a degree yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2e023a49",
   "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>UNITID</th>\n",
       "      <th>OMCHRT</th>\n",
       "      <th>XOMRCHRT</th>\n",
       "      <th>OMRCHRT</th>\n",
       "      <th>XOMEXCLS</th>\n",
       "      <th>OMEXCLS</th>\n",
       "      <th>XOMACHRT</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>XOMCERT4</th>\n",
       "      <th>OMCERT4</th>\n",
       "      <th>...</th>\n",
       "      <th>XOMAWDP8</th>\n",
       "      <th>OMAWDP8</th>\n",
       "      <th>XOMENRTP</th>\n",
       "      <th>OMENRTP</th>\n",
       "      <th>XOMENRYP</th>\n",
       "      <th>OMENRYP</th>\n",
       "      <th>XOMENRAP</th>\n",
       "      <th>OMENRAP</th>\n",
       "      <th>XOMENRUP</th>\n",
       "      <th>OMENRUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100654</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1277</td>\n",
       "      <td>R</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>1272</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>30.0</td>\n",
       "      <td>R</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100663</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>3526</td>\n",
       "      <td>R</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>3521</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>57.0</td>\n",
       "      <td>R</td>\n",
       "      <td>26.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>25.0</td>\n",
       "      <td>R</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>100690</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>147</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>147</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>36.0</td>\n",
       "      <td>R</td>\n",
       "      <td>6.0</td>\n",
       "      <td>R</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>6.0</td>\n",
       "      <td>R</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>100706</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1573</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>1573</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>54.0</td>\n",
       "      <td>R</td>\n",
       "      <td>33.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2.0</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>100724</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1874</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>1873</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>R</td>\n",
       "      <td>32.0</td>\n",
       "      <td>R</td>\n",
       "      <td>36.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>35.0</td>\n",
       "      <td>R</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    UNITID  OMCHRT XOMRCHRT  OMRCHRT XOMEXCLS  OMEXCLS XOMACHRT  OMACHRT  \\\n",
       "12  100654      50        R     1277        R        5        R     1272   \n",
       "27  100663      50        R     3526        R        5        R     3521   \n",
       "41  100690      50        R      147        R        0        R      147   \n",
       "56  100706      50        R     1573        R        0        R     1573   \n",
       "71  100724      50        R     1874        R        1        R     1873   \n",
       "\n",
       "   XOMCERT4  OMCERT4  ... XOMAWDP8  OMAWDP8 XOMENRTP  OMENRTP XOMENRYP  \\\n",
       "12        R        0  ...        R     31.0        R     31.0        R   \n",
       "27        R        0  ...        R     57.0        R     26.0        R   \n",
       "41        R        0  ...        R     36.0        R      6.0        R   \n",
       "56        R        0  ...        R     54.0        R     33.0        R   \n",
       "71        R        0  ...        R     32.0        R     36.0        R   \n",
       "\n",
       "    OMENRYP XOMENRAP  OMENRAP XOMENRUP  OMENRUP   \n",
       "12      1.0        R     30.0        R      38.0  \n",
       "27      1.0        R     25.0        R      17.0  \n",
       "41      0.0        R      6.0        R      58.0  \n",
       "56      2.0        R     31.0        R      13.0  \n",
       "71      1.0        R     35.0        R      32.0  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PellCodes = [50]\n",
    "uni_students = uni_outcomes[uni_outcomes.OMCHRT.isin(PellCodes)]\n",
    "uni_students.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e8f6f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to contextualize student population served (OMCHRT) number, add a descriptor column\n",
    "\n",
    "def label_studentdetails(row):\n",
    "    if row['OMCHRT']==50:\n",
    "        return \"Total Students\"\n",
    "    else:\n",
    "        return 'Data Unavailable'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c4d105b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1558/3244937734.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "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>UNITID</th>\n",
       "      <th>OMCHRT</th>\n",
       "      <th>XOMRCHRT</th>\n",
       "      <th>OMRCHRT</th>\n",
       "      <th>XOMEXCLS</th>\n",
       "      <th>OMEXCLS</th>\n",
       "      <th>XOMACHRT</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>XOMCERT4</th>\n",
       "      <th>OMCERT4</th>\n",
       "      <th>...</th>\n",
       "      <th>OMAWDP8</th>\n",
       "      <th>XOMENRTP</th>\n",
       "      <th>OMENRTP</th>\n",
       "      <th>XOMENRYP</th>\n",
       "      <th>OMENRYP</th>\n",
       "      <th>XOMENRAP</th>\n",
       "      <th>OMENRAP</th>\n",
       "      <th>XOMENRUP</th>\n",
       "      <th>OMENRUP</th>\n",
       "      <th>studentdetails</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100654</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1277</td>\n",
       "      <td>R</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>1272</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>30.0</td>\n",
       "      <td>R</td>\n",
       "      <td>38.0</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100663</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>3526</td>\n",
       "      <td>R</td>\n",
       "      <td>5</td>\n",
       "      <td>R</td>\n",
       "      <td>3521</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>R</td>\n",
       "      <td>26.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>25.0</td>\n",
       "      <td>R</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>100690</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>147</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>147</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>R</td>\n",
       "      <td>6.0</td>\n",
       "      <td>R</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>6.0</td>\n",
       "      <td>R</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>100706</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1573</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "      <td>1573</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>54.0</td>\n",
       "      <td>R</td>\n",
       "      <td>33.0</td>\n",
       "      <td>R</td>\n",
       "      <td>2.0</td>\n",
       "      <td>R</td>\n",
       "      <td>31.0</td>\n",
       "      <td>R</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>100724</td>\n",
       "      <td>50</td>\n",
       "      <td>R</td>\n",
       "      <td>1874</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>R</td>\n",
       "      <td>1873</td>\n",
       "      <td>R</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>R</td>\n",
       "      <td>36.0</td>\n",
       "      <td>R</td>\n",
       "      <td>1.0</td>\n",
       "      <td>R</td>\n",
       "      <td>35.0</td>\n",
       "      <td>R</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    UNITID  OMCHRT XOMRCHRT  OMRCHRT XOMEXCLS  OMEXCLS XOMACHRT  OMACHRT  \\\n",
       "12  100654      50        R     1277        R        5        R     1272   \n",
       "27  100663      50        R     3526        R        5        R     3521   \n",
       "41  100690      50        R      147        R        0        R      147   \n",
       "56  100706      50        R     1573        R        0        R     1573   \n",
       "71  100724      50        R     1874        R        1        R     1873   \n",
       "\n",
       "   XOMCERT4  OMCERT4  ... OMAWDP8  XOMENRTP OMENRTP  XOMENRYP OMENRYP  \\\n",
       "12        R        0  ...    31.0         R    31.0         R     1.0   \n",
       "27        R        0  ...    57.0         R    26.0         R     1.0   \n",
       "41        R        0  ...    36.0         R     6.0         R     0.0   \n",
       "56        R        0  ...    54.0         R    33.0         R     2.0   \n",
       "71        R        0  ...    32.0         R    36.0         R     1.0   \n",
       "\n",
       "    XOMENRAP OMENRAP  XOMENRUP OMENRUP   studentdetails  \n",
       "12         R    30.0         R     38.0  Total Students  \n",
       "27         R    25.0         R     17.0  Total Students  \n",
       "41         R     6.0         R     58.0  Total Students  \n",
       "56         R    31.0         R     13.0  Total Students  \n",
       "71         R    35.0         R     32.0  Total Students  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying that label to the dataset \n",
    "uni_students['studentdetails'] = uni_students.apply(label_studentdetails, axis=1)\n",
    "uni_students.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d3eb3bda",
   "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>UNITID</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>OMBACH4</th>\n",
       "      <th>OMBACH6</th>\n",
       "      <th>OMNOAWD</th>\n",
       "      <th>studentdetails</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100654</td>\n",
       "      <td>1272</td>\n",
       "      <td>118.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>875</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100663</td>\n",
       "      <td>3521</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1531</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>100690</td>\n",
       "      <td>147</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>94</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>100706</td>\n",
       "      <td>1573</td>\n",
       "      <td>553.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>724</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>100724</td>\n",
       "      <td>1873</td>\n",
       "      <td>239.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>1278</td>\n",
       "      <td>Total Students</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    UNITID  OMACHRT  OMBACH4  OMBACH6  OMNOAWD  studentdetails\n",
       "12  100654     1272    118.0    349.0      875  Total Students\n",
       "27  100663     3521   1366.0   1899.0     1531  Total Students\n",
       "41  100690      147     43.0     49.0       94  Total Students\n",
       "56  100706     1573    553.0    807.0      724  Total Students\n",
       "71  100724     1873    239.0    553.0     1278  Total Students"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_students_cleaned = uni_students [[\"UNITID\",\"OMACHRT\",\"OMBACH4\",\"OMBACH6\",\"OMNOAWD\",\"studentdetails\"]]\n",
    "uni_students_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f0f886",
   "metadata": {},
   "source": [
    "For each university and each subset of students, we'll calculate the number of students that are \"well served\" as those who earn their degree within the four years; we'll calculate those \"poorly served\" as those who do not have a degree after eight years. Each of these will be represented as a percentage of the total subpopulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3c4c68ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UNITID              int64\n",
       "OMACHRT             int64\n",
       "OMBACH4           float64\n",
       "OMBACH6           float64\n",
       "OMNOAWD             int64\n",
       "studentdetails     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_students_cleaned.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "80945acb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1558/3501207542.py:1: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/tmp/ipykernel_1558/3501207542.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "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>UNITID</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>OMBACH4</th>\n",
       "      <th>OMBACH6</th>\n",
       "      <th>OMNOAWD</th>\n",
       "      <th>studentdetails</th>\n",
       "      <th>pct_well_served</th>\n",
       "      <th>pct_poorly_served</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100654</td>\n",
       "      <td>1272</td>\n",
       "      <td>118.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>875</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.092767</td>\n",
       "      <td>0.687893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>100663</td>\n",
       "      <td>3521</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1531</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.387958</td>\n",
       "      <td>0.434820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>100690</td>\n",
       "      <td>147</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>94</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.292517</td>\n",
       "      <td>0.639456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>100706</td>\n",
       "      <td>1573</td>\n",
       "      <td>553.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>724</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.351558</td>\n",
       "      <td>0.460267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>100724</td>\n",
       "      <td>1873</td>\n",
       "      <td>239.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>1278</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.127603</td>\n",
       "      <td>0.682328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>48192</th>\n",
       "      <td>495031</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48196</th>\n",
       "      <td>495147</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>495183</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48206</th>\n",
       "      <td>495280</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48220</th>\n",
       "      <td>495767</td>\n",
       "      <td>20061</td>\n",
       "      <td>9848.0</td>\n",
       "      <td>13258.0</td>\n",
       "      <td>5915</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.490903</td>\n",
       "      <td>0.294851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3694 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       UNITID  OMACHRT  OMBACH4  OMBACH6  OMNOAWD  studentdetails  \\\n",
       "12     100654     1272    118.0    349.0      875  Total Students   \n",
       "27     100663     3521   1366.0   1899.0     1531  Total Students   \n",
       "41     100690      147     43.0     49.0       94  Total Students   \n",
       "56     100706     1573    553.0    807.0      724  Total Students   \n",
       "71     100724     1873    239.0    553.0     1278  Total Students   \n",
       "...       ...      ...      ...      ...      ...             ...   \n",
       "48192  495031        2      0.0      2.0        0  Total Students   \n",
       "48196  495147        2      NaN      NaN        2  Total Students   \n",
       "48200  495183        2      NaN      NaN        0  Total Students   \n",
       "48206  495280       13      0.0      2.0        5  Total Students   \n",
       "48220  495767    20061   9848.0  13258.0     5915  Total Students   \n",
       "\n",
       "       pct_well_served  pct_poorly_served  \n",
       "12            0.092767           0.687893  \n",
       "27            0.387958           0.434820  \n",
       "41            0.292517           0.639456  \n",
       "56            0.351558           0.460267  \n",
       "71            0.127603           0.682328  \n",
       "...                ...                ...  \n",
       "48192         0.000000           0.000000  \n",
       "48196              NaN           1.000000  \n",
       "48200              NaN           0.000000  \n",
       "48206         0.000000           0.384615  \n",
       "48220         0.490903           0.294851  \n",
       "\n",
       "[3694 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_students_cleaned['pct_well_served']=(uni_students_cleaned['OMBACH4']/uni_students_cleaned['OMACHRT'])\n",
    "uni_students_cleaned['pct_poorly_served']=(uni_students_cleaned['OMNOAWD']/uni_students_cleaned['OMACHRT'])\n",
    "uni_students_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27cf3bc1",
   "metadata": {},
   "source": [
    "To attempt to put this into context with the data on institution type and region, I will merge these datasets using their unique UNITIDs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c915ceb",
   "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>UNITID</th>\n",
       "      <th>INSTNM</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STABBR</th>\n",
       "      <th>OBEREG</th>\n",
       "      <th>LONGITUD</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>SECTOR</th>\n",
       "      <th>sectortype</th>\n",
       "      <th>region</th>\n",
       "      <th>OMACHRT</th>\n",
       "      <th>OMBACH4</th>\n",
       "      <th>OMBACH6</th>\n",
       "      <th>OMNOAWD</th>\n",
       "      <th>studentdetails</th>\n",
       "      <th>pct_well_served</th>\n",
       "      <th>pct_poorly_served</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100654</td>\n",
       "      <td>Alabama A &amp; M University</td>\n",
       "      <td>Normal</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.568502</td>\n",
       "      <td>34.783368</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>875.0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.092767</td>\n",
       "      <td>0.687893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100663</td>\n",
       "      <td>University of Alabama at Birmingham</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.799345</td>\n",
       "      <td>33.505697</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>1366.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1531.0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.387958</td>\n",
       "      <td>0.434820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100690</td>\n",
       "      <td>Amridge University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.174010</td>\n",
       "      <td>32.362609</td>\n",
       "      <td>2</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>147.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.292517</td>\n",
       "      <td>0.639456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100706</td>\n",
       "      <td>University of Alabama in Huntsville</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.640449</td>\n",
       "      <td>34.724557</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1573.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>807.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.351558</td>\n",
       "      <td>0.460267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100724</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>AL</td>\n",
       "      <td>5</td>\n",
       "      <td>-86.295677</td>\n",
       "      <td>32.364317</td>\n",
       "      <td>1</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1873.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>Total Students</td>\n",
       "      <td>0.127603</td>\n",
       "      <td>0.682328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNITID                               INSTNM        CITY STABBR  OBEREG  \\\n",
       "0  100654             Alabama A & M University      Normal     AL       5   \n",
       "1  100663  University of Alabama at Birmingham  Birmingham     AL       5   \n",
       "2  100690                   Amridge University  Montgomery     AL       5   \n",
       "3  100706  University of Alabama in Huntsville  Huntsville     AL       5   \n",
       "4  100724             Alabama State University  Montgomery     AL       5   \n",
       "\n",
       "    LONGITUD   LATITUDE  SECTOR      sectortype     region  OMACHRT  OMBACH4  \\\n",
       "0 -86.568502  34.783368       1   Public 4-Year  Southeast   1272.0    118.0   \n",
       "1 -86.799345  33.505697       1   Public 4-Year  Southeast   3521.0   1366.0   \n",
       "2 -86.174010  32.362609       2  Private 4-Year  Southeast    147.0     43.0   \n",
       "3 -86.640449  34.724557       1   Public 4-Year  Southeast   1573.0    553.0   \n",
       "4 -86.295677  32.364317       1   Public 4-Year  Southeast   1873.0    239.0   \n",
       "\n",
       "   OMBACH6  OMNOAWD  studentdetails  pct_well_served  pct_poorly_served  \n",
       "0    349.0    875.0  Total Students         0.092767           0.687893  \n",
       "1   1899.0   1531.0  Total Students         0.387958           0.434820  \n",
       "2     49.0     94.0  Total Students         0.292517           0.639456  \n",
       "3    807.0    724.0  Total Students         0.351558           0.460267  \n",
       "4    553.0   1278.0  Total Students         0.127603           0.682328  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldata = pd.merge(\n",
    "    left=fouryearunis_cleaned,\n",
    "    right=uni_students_cleaned,\n",
    "    how=\"left\",\n",
    "    on=None,\n",
    "    left_on='UNITID',\n",
    "    right_on='UNITID',\n",
    "    left_index=False,\n",
    "    right_index=False,\n",
    "    sort=True,\n",
    "    suffixes=(\"_x\", \"_y\"),\n",
    "    copy=True,\n",
    "    indicator=False,\n",
    "    validate=None,\n",
    ")\n",
    "finaldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828d4ce4",
   "metadata": {},
   "source": [
    "Lastly, I'll try a few visualizations to see if there are any trends in quality of institutions by type or by region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26725303",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1558/2067835919.py:1: FutureWarning:\n",
      "\n",
      "Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "\n"
     ]
    },
    {
     "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>region</th>\n",
       "      <th>sectortype</th>\n",
       "      <th>pct_well_served</th>\n",
       "      <th>pct_poorly_served</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Far West</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.152861</td>\n",
       "      <td>0.424287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Far West</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.471077</td>\n",
       "      <td>0.368449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Far West</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.251621</td>\n",
       "      <td>0.471987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Great Lakes</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.120212</td>\n",
       "      <td>0.542366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Great Lakes</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.463626</td>\n",
       "      <td>0.395726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Great Lakes</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.246805</td>\n",
       "      <td>0.563913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mid Atlantic</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.168820</td>\n",
       "      <td>0.564898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mid Atlantic</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.425702</td>\n",
       "      <td>0.401470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mid Atlantic</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.436490</td>\n",
       "      <td>0.399721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>N/A or Other</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.828581</td>\n",
       "      <td>0.144768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New England</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.196760</td>\n",
       "      <td>0.553050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>New England</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.551633</td>\n",
       "      <td>0.312201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>New England</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.428574</td>\n",
       "      <td>0.396982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Plains</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.145377</td>\n",
       "      <td>0.583044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Plains</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.414622</td>\n",
       "      <td>0.407241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Plains</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.334296</td>\n",
       "      <td>0.485910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Rocky Mountains</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.347062</td>\n",
       "      <td>0.417599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rocky Mountains</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.313327</td>\n",
       "      <td>0.500538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Rocky Mountains</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.194103</td>\n",
       "      <td>0.558302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Southeast</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.120625</td>\n",
       "      <td>0.597062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Southeast</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.366566</td>\n",
       "      <td>0.489800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Southeast</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.300463</td>\n",
       "      <td>0.495468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.164060</td>\n",
       "      <td>0.555494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.319113</td>\n",
       "      <td>0.517798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Southwest</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.315166</td>\n",
       "      <td>0.518157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>US Territories</td>\n",
       "      <td>For-Profit 4-Year</td>\n",
       "      <td>0.078183</td>\n",
       "      <td>0.537755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>US Territories</td>\n",
       "      <td>Private 4-Year</td>\n",
       "      <td>0.140022</td>\n",
       "      <td>0.553903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>US Territories</td>\n",
       "      <td>Public 4-Year</td>\n",
       "      <td>0.123628</td>\n",
       "      <td>0.530386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             region         sectortype  pct_well_served  pct_poorly_served\n",
       "0          Far West  For-Profit 4-Year         0.152861           0.424287\n",
       "1          Far West     Private 4-Year         0.471077           0.368449\n",
       "2          Far West      Public 4-Year         0.251621           0.471987\n",
       "3       Great Lakes  For-Profit 4-Year         0.120212           0.542366\n",
       "4       Great Lakes     Private 4-Year         0.463626           0.395726\n",
       "5       Great Lakes      Public 4-Year         0.246805           0.563913\n",
       "6      Mid Atlantic  For-Profit 4-Year         0.168820           0.564898\n",
       "7      Mid Atlantic     Private 4-Year         0.425702           0.401470\n",
       "8      Mid Atlantic      Public 4-Year         0.436490           0.399721\n",
       "9      N/A or Other      Public 4-Year         0.828581           0.144768\n",
       "10      New England  For-Profit 4-Year         0.196760           0.553050\n",
       "11      New England     Private 4-Year         0.551633           0.312201\n",
       "12      New England      Public 4-Year         0.428574           0.396982\n",
       "13           Plains  For-Profit 4-Year         0.145377           0.583044\n",
       "14           Plains     Private 4-Year         0.414622           0.407241\n",
       "15           Plains      Public 4-Year         0.334296           0.485910\n",
       "16  Rocky Mountains  For-Profit 4-Year         0.347062           0.417599\n",
       "17  Rocky Mountains     Private 4-Year         0.313327           0.500538\n",
       "18  Rocky Mountains      Public 4-Year         0.194103           0.558302\n",
       "19        Southeast  For-Profit 4-Year         0.120625           0.597062\n",
       "20        Southeast     Private 4-Year         0.366566           0.489800\n",
       "21        Southeast      Public 4-Year         0.300463           0.495468\n",
       "22        Southwest  For-Profit 4-Year         0.164060           0.555494\n",
       "23        Southwest     Private 4-Year         0.319113           0.517798\n",
       "24        Southwest      Public 4-Year         0.315166           0.518157\n",
       "25   US Territories  For-Profit 4-Year         0.078183           0.537755\n",
       "26   US Territories     Private 4-Year         0.140022           0.553903\n",
       "27   US Territories      Public 4-Year         0.123628           0.530386"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finaldata_grouped = finaldata.groupby(['region','sectortype'])['pct_well_served','pct_poorly_served'].agg('mean').reset_index()\n",
    "finaldata_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34134035",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJdPr6eEKMSAwIG9iago8PC9DcmVhdG9yIChDaHJvbWl1bSkKL1Byb2R1Y2VyIChTa2lhL1BERiBtODgpCi9DcmVhdGlvbkRhdGUgKEQ6MjAyMjA1MTMwMjUzMjErMDAnMDAnKQovTW9kRGF0ZSAoRDoyMDIyMDUxMzAyNTMyMSswMCcwMCcpPj4KZW5kb2JqCjMgMCBvYmoKPDwvY2EgMQovQk0gL05vcm1hbD4+CmVuZG9iago0IDAgb2JqCjw8L0NBIDEKL2NhIDEKL0xDIDAKL0xKIDAKL0xXIDEKL01MIDQKL1NBIHRydWUKL0JNIC9Ob3JtYWw+PgplbmRvYmoKNSAwIG9iago8PC9DQSAxCi9jYSAxCi9MQyAwCi9MSiAwCi9MVyAyCi9NTCA0Ci9TQSB0cnVlCi9CTSAvTm9ybWFsPj4KZW5kb2JqCjcgMCBvYmoKPDwvRmlsdGVyIC9GbGF0ZURlY29kZQovTGVuZ3RoIDE0NDQ+PiBzdHJlYW0KeJzFWNuOGzcMffdX6LnAMpJI3YCiwGaRzXPaBfoBvQQomgJN/x/ooSjtaOq1m3Ud1961xzNnODyHFCWKIrf+ch7vO1p+svfup0+HPw96JYZaKLkQk3effzn8+I37A1eYcKLfuR3hluD0/f17ZwefPx7evGf38a9uqcCs+G7l18MHvC94ANVWHbWYEj6zFH3W0bntsdW7oE/NyUUf55Nfsl7A/AQBGZa8+3RIouDfDz+cMJOTEA6uYCn5RoJ4lGsY4yxUruJWEz5jKZ2wpLY0vhyihlqSmcscVK3TUd/u0dhzrdGRcBAEunrpwT86uUVfKCePF9zLmbIeZuQZWaZnx8kOxRIjBQqKjy4UobBBS3GxUOV+U4e2YqYBzRH5140ALv1xyXGjYI/r8CCwJwZKmSTikJ/xLTpJlDA8fBn45ikrHuYr8Q7KTDnjUjVo5GKmgQmUI0A7ePXEpaeRwStPy+C4yMFNOTbppDqU2U/LWVYcj0dzydMWrNa2YkKlELr0HUqNCyNWeDlUmmChOzqJ0IVCXnodapGaN403YQNGhW8b/cyUBlwW9jNswZcdffhEPhnKM4m6uOPWKqW6xVmjlQwPuXqJrDua0KzkTbMIo169TNSKZd5iO5OP3VfDSoNtANoLCVHiLiGi3mtSq0Bt+tHDJeKqTK0sMhJNFmAqeSO0wAtTM+oGr21az60/N6xojtPBjvYouA4qYdghiMsvRE+5JOOtdSLuJEA8UqVYNgkKZLIapyNkDLAl2gm5nTc/Q8Slgfd+UlhUCz5MtnYDRk/TcEQqvGqm4NYorqELCb4DIGCzSzsNR0GuLIMuB9jt3ODiTKgt0iAzeJm+vnZZAFctj5JORV+8Zswhwzr09BrttMKRG02jnQwuID2sI65cdkO6UuYFKmVa9lag9o6EFqyYfdkcDVV95qiZSymjXN+N7+1CxUiQXIvlOecWQ5/QEFdM1BSlBFSAgnu0IByd3Gr526fDm0dNDveEuNqaRb8YafL06fAtAgZNBAGz8Yq/++/c028HVVyYky5snn52AArCmxD5VDrg3ZNNT/9OBtJRqTovfX0y90ZEfe2kipGKj/Zb3k0er+SAAopSgsp7AxKaefKA/+n8ICKDRArjt2JeTSQK8t7nW0SDw1D7fhCp5rzc78nIazMqcM1Uwk3GB+bzZ2dV8a7+q1VPGE/Q4yaqI/9T1NSwfE8PY2B7O5/qkkL/iVRGfWe5zZjIi/Pq+NtlkF9SlELFrJhT5v/Hdx0SFxfU0DImpFzrDXyv5n9PIPMzohB6blgE72aGNCrvg5HshMfviypuDIEp1nCLACH7Q7RCq0Sn75103CdcSiuPc43elRzFsoWxcCrodig0W8Ka24HPuzJ68yv5gYaN0bBgQXDsB/5Vwn+RZmvyb+dSOe/Sbrfgdl61815t2w63c+ntOZewQo7Up9vnXYWzW1aB0VvldTX8TwuCThc/Yu5fp3cyXnjyxZrITpOozXa0fQ7TQmtxn1PyvoT1Yy1ruhKoX6aTBPTV9apBFI/o5bJGT9fu00vtS3Stkub037ZirIUslIF56AxKbwTRqO+quDUC5ym2wWxH8bV7THfZ3XWG4cz24iJn33W5avF4Qc4pX5dttkXFlHlBQu3K09FE+CoJV1pfvNdzmYSJmfpa7etLqCqksWp+sPH0knys7XsVuVi+ldK57ZXTgl22mX6pemWnHoOAX5uMsYqSUYbmGn2uSHRA9xXJLE/VWsQVm/LWac2mvndYD1vn1a+nbbUzr80Q9ec9rNEYdh8HPi09xey174cdLUZhRu9E4W1MXnS3F5pjYcNl1t+zxjRU5Pev0ALD9eLuLED2eRqFT7KUK5IIJ1H+rjej3OkecxMv0cXnSaUr2Dbl10j2CWVEU+LWFPSB8zjO5S0zup2675c/HP4Gr7JRdwplbmRzdHJlYW0KZW5kb2JqCjIgMCBvYmoKPDwvVHlwZSAvUGFnZQovUmVzb3VyY2VzIDw8L1Byb2NTZXQgWy9QREYgL1RleHQgL0ltYWdlQiAvSW1hZ2VDIC9JbWFnZUldCi9FeHRHU3RhdGUgPDwvRzMgMyAwIFIKL0c0IDQgMCBSCi9HNSA1IDAgUj4+Ci9Gb250IDw8L0Y2IDYgMCBSPj4+PgovTWVkaWFCb3ggWzAgMCA1MjUuMTIgMzAwXQovQ29udGVudHMgNyAwIFIKL1N0cnVjdFBhcmVudHMgMAovUGFyZW50IDggMCBSPj4KZW5kb2JqCjggMCBvYmoKPDwvVHlwZSAvUGFnZXMKL0NvdW50IDEKL0tpZHMgWzIgMCBSXT4+CmVuZG9iago5IDAgb2JqCjw8L1R5cGUgL0NhdGFsb2cKL1BhZ2VzIDggMCBSPj4KZW5kb2JqCjEwIDAgb2JqCjw8L0xlbmd0aDEgMTk5MzYKL0ZpbHRlciAvRmxhdGVEZWNvZGUKL0xlbmd0aCAxMzk1OT4+IHN0cmVhbQp4nI18C2ATx7XonJldSStZ1kqW5K8sCfkvWVos/23sxR9ZxiHYYINtMDZgbMzPNjaETxqbBAiYJDgJl4SQBNqSD/khKA2kSYrbm5v0kzS0SW9vbnqL25umTRsKt5fkNgm238xKJoakfd317s7nzJmZM+ecOWdmZAQIIQMaRgT1BQKN89dMjnfRlGv0SVqwyJezY82jOQhBHI13rNqwoo8/odLR+FYaf3PVlkFH5WOVf0IIV9HH2tXXvWHgHVcIIW4ZQmq+e8VAH4pDAkK6Tgovdq/f1qVdPzcBoag1CJWY16xe0Xn1qRMpNC7R/Pw1NCG6TtWBUM3tNJ6yZsPg1nebTQtp/Bitc3B976oVP8x85xGKb5Dmv7JhxdY+7ofqnyNUa6Zxx8YVG1bb/1D8Lo3nUvjTfb0Dg1Nj6BBC8/7I8vs2re7b/qPlnyJUp6Ht/SVNY71H/9nwIMe+5xOmhtn37Qfm/xYR7hyMIh4h/hHeTwETw1/yc9SFTRoe61QcZhcrSdCMa/bcRZVIRg70PP/OZAP41WVwmnbp6MVfUboU8ztZSxCmbxwpaaZvUL4cOke/WTSfQ1r6llAOykX5qBBVoBpUj5rQEtSC2lEPWo/60G1oGzqGnp+aUjB6rsPORdXoFrQwArsCrUMb0aZp2Kn//ifuVVOrwpT55y7+Tf5N9A3aMwvaprxvuLhi2rPbEJr6mMW+fE8u+edr+GcuTfhzBr2KTtK+zrz2ojvo+7kb0s6jf0XPKqEj6N5/gPYl9EwkdBAdRnf/Xbi16C6K5zit/8urg6ZuQw/Tms+hpygPzQI/rXVdJPd99OOvRwW/hR+jB9DTFPIBdJa+j1Bm2YH/ih7AC9FG/CuyE92J9tE+HoUedIDCd6DjsBQtp6nhazlajXpvQjqCRtETaDsa/jKJ3zn1v0h/7Sna8n0UzyHKV/0zSjwNn7EPsdO2v4C+q6TtnM5UB8la/CLGEw/SyP2omz4r4D3aznvJXFTFG+EEQnJ1S3NT46KFDfULbp1/S9282mBNoLqqsmKuXF42p7SkuKiwID9vtuTzZnsy0tNSU1yznPY4s1E0ROt1WkGjVvEcwYA81a5AhyOU1hHi0lzBYDaLu1bQhBUzEjpCDpoUuBEm5OhQwBw3QsoUsusmSDkMKV+HBNFRikqzPY5qlyP0VpXLcQ5aG5pp+N4qV4sjdEkJz1fCXJoS0dOI00lLOKrj1lQ5QtDhqA4FtqwZqe6oovhO6bSVrsrV2mwPOqXV0aCOhkIZrr5TkFEGSgBnVBefwkijZ9WGSGr1is5QfUNzdVWi09mS7akNRbuqlCxUqaAMqSpDagWlo4c1He13nPKMjdxzTkQrO9xRna7OFcuaQ2QFLTtCqkdG7g4Z3aFMV1Uoc/sHcbTnq0MeV1V1yM2w1i28Xk/dl1VCiE8VXY6RTxDtjuvSxzemrIikqFLFTxALBih5R0YCLkdgpGNkxbmp4ZUuh+gaORUVNdJXTSmM6ptpqXNT39ufGArc0xISO9ZAcaSzgYV1oZiGpc0hnBpwrFlBU+hfuctZmOg0tkzD1P+9bEQJQclBaep0so7vPyejlTQSGm5oDscdaGXiaST73C0h3MFyxqZzLE0sZ3g653rxDhcdzbpFzSMhLrW201VNabx/RWh4JeWntWwoXGIo+tNEp2vEZHQU+VoUWAdtVW1njyPEp1Gy0FIzC1BOYUVGRCUS/Wn4cymRVpBmNDmKXBQNw1Ptqu6I/G1ZE0cROLI9oaA7PPSNzSG5igbkFZExqj4l+WiJFR10iHqqlOEL+Vx9IbOr4vp4smZV9yxqVopEioXMlSE6qUdKhXzVVaxmR/VIR1W4CQyXq6H5JeSfGj+V60j8jp/OMC1VDNhaSfkqrXqkubMrZO9I7KSS1uVoTnSG5BY6wC2u5tUtjNEohTLHaXVOpcYQrmxsrlvkqmtobS6MNCScwdBxqdU3oXE1J4bRUJYLaVI1jmacSFoooEgTHAEacFWU0ndInaqhj0gJrqQyVq0odTRDIpqGps0IZTqqV1dF4Fj8BqQ8Y6fK4DQ2FYtSPJXBRGeLM3xlezDNdkQqpiU0jKjB6SySSjUBTcMUjZLEaBnHeN7R7FrtanGtcYTk+mbWN0YehcoRYig0j4xV4w2xGcSiZEJOmj0dYcQMBdyJM4kbqlHi16PBm7Jrp7MdIxpX3aIRhtwVQYhoy2tDiLGwXGhMVKSfybMrsIIKMZVoRZ5HTskyk+U1TGxHXLWdI65FzaUKNNUg30jczuoyoTqoa6zI9lBlVnHKBXsbTsmwd1Fr80siNVX2NjafxoArOypaTqXQvOaXHHSuUFIxS2WJLOJgEYZpIY1oFPjEl2SEhpVcTklQ4qvOAVLSNNNpgFadw+E0cToN0zQunCYraeyioxS3htKY6u9qRycbn9tb1ox0tDAeR1ZKEfoHIXCVUeq4yk4BVkWFtK7VFSGdq4Kll7P08nC6iqWrKWeAFbI920fEatcncdnh6ZJQ8wQjIwJqrTGbz/0P77XUzjiBfg419L4AF3A+vcfxODl5882t4P6P3XweP6RyXL/r1M9oZmueErT0fkrbp9uge0/3XnRDdIPhgRl3yBASl4n/Y+wyXjYtN/1HzBPmaPNSel+1FNL7CavFeoreU7HPxenjdsb/LPFK4hXbiGIXgvHPV4uOLWk3lH6C7GG7a6btPNkx8aCmWx1ESOl1+MKI+QidfBPtvxp5TwHylZ5Wc5pLOadU/K9LTxNMg+gUYck8Sz6tVgnXSk8DS/cbncZUp9FZhR2TKfDw5Bq+6fNnq7i3aGvOTX4OO6ktFYVufQlxU+Nndcag9iF0SH1uaky2CsagejhqNArLUfVRoSgyGnUsCkexrOi0zGCUrBeDUeqn0REd8k18AD63u61/k989cWm25E61mKOx2lUGea482CmYbeYd2bOb3386b0ldhWPurrnvM+9hKR2dBbiP9skpmxFlrD5MaI9fPgpvA/YB0F629V8Cn69tthST57Qshatw4dgxWvJlWvwO2m6CvLKDUB9llBanNn4IjaMriB+l5h1GrH0IqPnnozhY22jD/Hl+y8v/+v77bCSmPsZF1PYmyC8nbKNGHIY4vTGYSaVLRJTzHXiYygk6N3XlDIOOQeXl4Ct8q61wttTmbrOAH+DI45M9Zn78cwcbofqpjzkXf4gCpqM+ub4ltScVB5yLnV1OsjixKxG3xPbEYs4EW4x7jHibfp8e66JAp4Ft6n1qvIXsIbQ2UKON8qj5mBmbM4eTN5ZrQRs/YFC7Bvh4VH6pnPaj7VKC+Ou2BNqV5W0zLjDjaHDN8gLOyzWVgT8nGUzqXC92zYrGFnMy9ueUYc41f/eZru7Td9XV7TqzdvWpXbeczbi1P3jL4IKMzAWbams2LXDjH/x08s/Pzpv3DFje/CXEPllZ+eTkR798+uLugsLdF5/61m/uLim5+zeUGt3UM4mifc1A35Tnb+Zgc8LuBLxdHBHx6lRYnAqZVO/3OEmPC5JcEG+BzYm7E7EqEdJtG2WNnJYVlDVwQAOarGHTxpjBtF1pOCZNVDw7xl325LQg0qTdZ4JlpvWm201Ea0owYZN+IE4NaYPUKS6/VFR+yU+7bSpiI+v3t/kUsvS7Ey7l0OFpQwpNwl9/XhmfFyGFOr2MUNpQkkSr1E5Lt//Bbx0dXpBS1V6c3z7Ppz4nVAx+e13P8f5Sf1Pf9ts3LI7DF4c2f+f+22/fu7h0aZk9ubSlxHjLntXFOStHl9cMD67vXt3VU3SYjb6PUqSA+m9aFIs2y/WPGOCwCvaqYLf4LyLeIkKcFbZY91gPWYmVl6MsQX6pdp12h5ZoNdAbq7Jj6KNKCjO+k3A9DfIajLgBjdUKOpUKaJfL/aYin/9SDviWMzGjoZw2n9vf1s/6y3oKRpfRmQd+o98C0UTNHifpf2ZiDd796uuTo1g0WzSTD/ExZrMK/grlkz+A8nvIi9duuY/cxttSUqMmPtYkJCaoaV/WUE6Op6ObQv3hk7J0jwW2W0esuEXdo8atZC3ZTkgj7sS40dXpGnSRxlmdswZnkbzk6mS8OwdyqMTI2Vp9cFcCpCbkJVQnbE7grAlg6TWbKXf7Uo+m4tFUSC0e9tk2ilqHFmvZoMfRQdfasjY7HLlxA/GWwxZsMahzI4zvb7tEe8867267lKMMNR1p3yWl7242zDA96FTv3CAHOFbtBUUMwtGvSEV8ycAzvTvObZ9TfefLW4J3dN8a+1zSjoZ5WxuzZ58e6Hi0d87ZlODawOzOBn9G3dqKud3BNHhr7amhmuWnAI6/Aknf70iu3FhvX9oQ2P+Le5a2l21+qq92y6Js29y1t9x6d2dxdtN2NoO1UJomUJpmojmoAZ2Xd23O2J2BNzt3O/Fm224b3py0Owlvjtsdh7fHjsTi7TEjMXh7FGzXjGjwdvWIGm8nIwRvxrsxbqpeXY2b6lbX4da5a+fi3I2WrI12Q4p9gQUsdotdS4lsKLOXYXuZr+xoGRktg7JFw8GNF6gySTFUbi4qmu/bnKCevzmiUxhhi6gc9YdFaFPCJWMRtaT7TUVF4iXx0jR7MQpf/0aoTIlchgvyvIR9I3KF1ZZkQm4icszNRE8o2vzi7bd/97Yi34LV+SVt5c6ivic3DJzozXeWt82Zs+YWz28Syzpra1aWJ1mLu+qbuguMrqSqgcULequpCbmpYWFflQ32tT60saxsw0Mt87cuyRO46LIla0vm3bWqqHjVrnnFa5fMieK0eUu24lvymstdrvLmvKzmoNcbbJ74tr+9Njt73orcuevmZ2XN30A5nqP+/EIqvWpkBpP8sy68Be/BpCtmS8yeGNID22AfkB7zNvM+MxlQ3aXCq1VwO38Pj9fysB2NIFyEWqj/TzaT3QTnk8Wki5BWDoIcNKmhRg0xmIAZWVSpqjwVoeL8oepTFU7gs/hingg8fMR/xmMVr9dzCSgLFSMiIPgIfUbbJaodaklNHGpQq60WkkryCFER+JB8SjA1YM5zmKu3hqxYsnZYR61j1itW3mcFwO3mmJgtetDzhEMmVJ5DBSisPNra+n1tVGn0G/10bmUBU9Ecn58FlHAR/QuP+MzLSZzEBX4BvCSdqhXi5A58c+KOb72Oy9/D+RMviDarAXB0rM1wBhvg0clOfufnQxzOWFiZzfPeqoUZk7OpBDxNNeSfKI2tyIm86Ii8/kjcs3H4QQfsdsCD2bA5e3c23p4ykvJICuF1Fl2qjqiwFadh8qwJjppgnWmHab+JmJL0rbEynadjYzNRq2yf5ZuFT86CWdJwUmYrUokqWUXUKlNG+1ASJCVlJrarSWa7yjTN6f2XipgCva5DaJKb8jvt4/VpI8Ld1GCaZuhkArlpSkKEk8NB7k+N972yZuI1jDafG650Vq6ubLqr2Tv5l0cPTp6HuY2DQUfD7GU76ycfhYHaHS05cO+6h9o9/M70xp2tJWua5hi0xa234YpNKycrnHMWT3y/cnlp0iQXV9pJ51dGq0lKKx2a/101JThSVGSqWhfUavW80IqQiGRE1MihB037EA88L3DtQIR2NKOr0KZ0lQ6128REmpo/RqfFGXme5rKvPUByrv2MPMTvfHSy9JFJy6MoUvf9tG4B9byE8NTYi/q4IMYqDWtBsToqqNHogP+S1JIOuPbLAAYoh14YgqNwkppvF0GjATk2OQjAIzoGPG2W0ir38rabGuZzz5ZAaROdvyzQTUzX/nKefMT9fuLq4xP/RpvG7I2pj/ljVHtmo5fkO/e4Ya0bKlIXpmI+zhLXFEcWx8LiGOBNFlOTiWyPGonCLVE9UXgtgbUYalNbUnGeDXbo9+uxTJWgNmWZXXY60ZD9gB3bfcMOX4dv2EcsS0+i8wg7EBhojc72K5SrZvEJ7ZkxYjsv6WQdHtWN67BOx3OonEmPSOlsLKIWGbXK2toutSWIb7bReIJIZybKTqgtojMjV0yuKT8lMiGRmcqwIM+Z5zRG07SUNNzdcgpUz9/52min68WE2jX7modfvX1OxZ0/GFp0T/9i2+RS3OQbevyVdacnPz3Vgt94ArQvr/Yu3jE/P7ep1Fl/8MLQ0DuHm5KyC5Imj00mSEsq0jb8K0RNj6qPjiqPCuUMkZOoZcuJnIOTuQ5umDvGXeE0HPmSr4CQCCsp4uJjZga1LBjbnMc/4nd+kTjNKypMsXqwR456OBMeskOUaIoLUifhyneokOqZLZBIEzKoMhJpQpbDmUpfpiT6ou5F8rmp8e/QFOVLE9lXnqIZ+qiUlETPsqwUlIvwuwj2oyMIcwg0yLvfC4NeKPHCT71wxgs6L7z9rBdyveDwgtkLyAtXvXDBC695IcRAd3mPe0mHFxq9ICtwohc4Lzx0hRV/zfuBlxxjYAe9uN4LVV6QWHaKF1Ms4wzkXS8e9cIuL/Sx0lXeTi8J1xSuJlzBa16ug2U3enEYfTfDGMbP14cxVnmJ2RvGsMvL8F71aljJq16yn0Gw0oNerkBe9IHSOVYijIWnnWTg+GUvsMK4jjWAaqIvvHA83IdhavLI3npvn5eUMyI4vDg5cRlKkpOwOkllsTDpFU2U9hYbqUsBpE8hSXR8Y/1t5X6/2+gPa0XKpu306WfXJnoxH6M/8m2bzpjO3DQj43rm8nBA/LUSyLkUnl6oRxi+2tjT5jTm5hfkF6jU0aAGF5tV0tKtsclgoSrAD8kktowUUEOWX4wxJtEGvd0weXDP5AGV3mBQG0Wgqc98AbepzSYDIaLFrIG+T8hz/rUev+TPca9IvyaTMUNGti82r6iwwNedfq2R33nNZy6vKBHF0ooyM/n550MR7uUdlHtj0GsvIS3lvPQodzBfG9Bi5KChTFSEahHRiTZnUMdY9pDuSR3O1AEw7qaJwLg1j2Y8As8AJmkAMctQldgoHhTJOG0mEiVRFvvEMfGCqBJlC8iWMcsFy7iFCw9HNHW21cuQRtTIGqLWgJJocweNIBCdTAM6pKFjRMeHPeERcrMJvJ1O3ZeYhmlvY+R0u4EawW4nfEnDtHQv5FF55YonoqlTTZ75Df6cUA/zOS40W8pc6rrWTGU4OHt21qps8iijBkYrqI79VcR/XS/PW5oKCakgpMJCJ1icoHZCYyJYEmFpLMTHQpcR1kRRD142mIG6qo7M4UycvPSk9rwWO7Rg0NqpVR/fbuBc7XzMtOca1pG/7k+4dKOFQX1XFacov7ywiuSsX/Fd+V+1n5m89sQLk5+/0LzsNPAnngb+1LJ/nTv0yo47vj9UPnfo1R27zu8ooRpx8q9ja5jSO6roxq5XJj/99tCFg/XTunHx4XdpX6dtkWjFW1t0AsODsXBEfFbEWpJAsgjhoyxRqVGEWhiG+GE5HuifWd0aGSqNqt1u9pkXmNvNQ2beYH7bPGUmarNM9ZbZrI5pF4j6xunOx0Srn/Y94VJ4XrhuY7gUK0MdMS/yuT/N2fbitsmV53HDN753e9nY8eOTu+GuJ46Q95Yd3Vw18T6/c07vYyv27J9494FwL/g1tBdGlIx+IZed0MOI+RHzM2Zy0AaKU/ECgscQ3I7uQXgeakVrESHHADbBnfAAELwKQAa2jJEKQMRzU33yEmNtnzgsjoqkUewUcQXlYZfoFzGIYrypVadDyCgZZWOHcdR4zKgyyo5RxzEHiafzBp1LZI6oORG3++Lb43vjD8Rz8fEorv26Bcqo0RZ2XykxqBFK3Q5TkZGqBkXluNmLhdojbgbVEjBNH8UKAzLD9Lpn8oHJ2vP4oa0v3VGR3njnUhj9m6dx6y2TJfDWwq3zU3HtxFl+Z8GaQ8sr71x/qzjxTfKxvLzcPvG3zOBKJvlt1Bv7G+X1cvQ7eeOhMqgqgydLYE8+7JoND2XACSfonIlOt/OIk2uxnbDh/UbYr4ZDGDhsxnhXMXTkQ48FthghqzWT2aGhGIiZOyy0amQxhlpIua3ILtplO1HbY8QYa3BrzN6Yh2NISQzkshnRR5Nuy70796FcUpwLMbm8r703C1qyoC4LVFmQlcJFt3cIsFCAKgEEJkFsseOS8maecBFzetvCGoHaUJElIcUfnl75uEG+KKs5UtILkgkTJbYWku7lp122mdZIbDLP/a169P1Dk59N/mfGS9HFqx7oarqvq6h80+MdJbdt6AhkNIy+tumu7w3Pj30lOm/xjkUrdze4ytffVz9355buW9ywu+XQhjnnXkgtaJ2bYittr6heXJhm1dvdxQ3rAp0HlmVlLtxW7/TX5ye5Sht85Q35KSYDzWzcREfESyXyDPPA4F1qAAjwY+E94TOBvCxArdAibBP2CVwJpYUQL+BPBTgs/FjA+8PxWqFH4N54T/hIwD8V4EUBMmmBHlrgsMAnCqASIF7IVHAcFk5QrOqPKGL8vgAnBDgkQBGFxdkCgE6Ah9YJO4T9wrPCy8KfhS8EdaNAU91CCWvHFwI+LkCJUEdBSIoA+4UjFOynNJ0fEgAvENoFLAlgEKD7beGigEMszFIPCNwVAY4KJwWWzvUJ0C6ALICd/ZVTgF7hKM24LKiRAAWXBRiW24RR4YJAegWoF8AnAM24IMBJAUYF6BWGBCwKDkEW6gVuSoBxAc4zhB200DGBKxfAoTSDGt1cNLRiGauj+9TH1CHmRw6rsbK2a4hNCqodmM5RHBVRUNQVlc22N+n84oaEOHH+xAc57TdM+9cn/OsWwPKwxF6PtUegZ5gGYVBm4LPFKacFX/j+ZBK3h/v9F4nc7x9VbMjH6byTSUc+Hu2V5yiLpC2kh+AW3INxtQjVWrC0yrKp3nTMRCTTqOmKiRwwHTWdpL5g4rCcCHLiWOKFxPFELpF1LIVOqprWBXw738uTA/xRHr/NA+JFXuaJmo9REX07UmYkxeJhi8JuRTMrCx/TyplnSxt09oxMPkowGuP3By88tux8Udf+RQsf2Fj+/bZjvzKX3fXjEbLz2oF1/7Lc7ek40ks6r91/79t7K2i/BITUe2i/vNwSuXabBzYbYQfej3EthsGoXVE4EAXbk0eScW1yS3JPMtls323HNfYl9m47uS8blmavy96RTXaK0CkOirhJBHCEDWpqINPAXQg2I6hCjagTkXwE90TD9mioi14avS6aRItCVDAhOiu6OJoI0fBR9GdUvvWp+jw9UenhQ/2nehyttyYkZyUXJxMhGT5K/owqWXuqPc9OVHb40P4pdYyS1QmQBcXUqQT4CD4DbEGpKA8RFYIP0acIA0rfkQ6D1l1WbFWnp6utxJCQnZVdnE2IJhv+mP23bJz9PjXOvUCt1pNeOOKFA17Y4YVeLyz1wgJqsC7wHvCe9BKvHJ8UdHglLzZ4QfDyIvxe/ETEL4qvi/8uUgtPYyg0bDXsNTxsOGdQRRlkeSo+LWi4TXpI+pVE8qWAtFgisVK6hFUSFEjd0m3SU9JZ6Q3pD9L/SZo0CdRSrIR//AaF/oNEtkoPS09L5ySuR4IMqVBqlkg8A4E/SvCeBE9LP5LwYQlGJGiW1ki4lqEEjRQn4T9I8CMJngrHMqSgtFfiD78RhturYOVrGU4QpHgJ/4f0Rwn/VIJHpGek70lkvwTS2O1DwSIJsiSgNWol+EyCPymV/kSCcxLskw5LJ1gDgTatWJontUokU4IECaIkWDMhwccS/JcEb0ogT70qwbMSPCoBxXu7BOskWCZBnQSlErglSJJAJ8E1Cf4swa8loK14ZRoe3SvBkAQbJGiXYL4EPqlcwjYJDBLQGi4rNbwtAcV/UoLHJDjAYL8h4aUKdIkE2RIkSqCXoPALCS5J8L4Eb0nwsgTPS3BEAop+h4K+Tloq4SKlOfFKcz5TmvNfSnPCzX9Maf43lOa3Kc2fIwErYJcAt0tD0lHpvHRRmpJUiBK9St1IlVdyNjGQdNlg3WHdTxnPIUQHrRAdtpLbjH7m0ACV6/b+/vb+mdemG68bNk9mODbtXw9+IzwDcF+HWj6z/Azdx2LM/vMzFcl0jbttRpP8bbQuUyz1jnzUMjZOr8B9NUBdp0i1bvdNjXa6iBeo66T4UWyBLiYZYq2x+QXUfYqhEUIjbH0hxs//4YOrunhtVJQ+Shen+/SDyRVvTBjtOr3OIKqjDQbVJ2c/URkM0WrRAGKczfDZG2QobY2voKi4QOpKu7aTuk87y2+fXZxbXZlUVloQSzZcezC2oKQsqTJQvWZbLlG8qWxqjTI9rsGLZHOyGqLUoI6LMgSNPBAeRMKfm/qjPEUT6LSEQROt7tGc0GCVxqrJ1wQ0XLRGBa1peDE+hMkW/CHGRVRTYhW1PfFh7Qntj7VksRYEbZEWx2tbtPu0n2k5lRZ+8pkWPmTp8dqXKAz3uhZatNsoPMnXQiaFfkn7kZbTaeEwBXxd++9afFoLx7VwSAt3amFQC4u1XVpcoYUUba4Wm7TAaeGqgvI17bta/KT2RS1+QAu7tLBFCyu10KgFxUFM0YJVAf6rFuCCdlyLX9PCMW1Iiw9qoU8LnVqQtWDWMrQEaaHnA+1VLb6glV+ktZ/RvqYlw9pRLaYNqNd2aHGVFhwMnVmLae3jkdpDrL5O7aD2oPa4lpe0slIvYrkU2Wi4QIq2SttIYXZp1UXjrKXHaVHSxzJZ5QwBr1Q+poUzWoiUYhm7tPy72g+0+GWFIrQEllhbDFofdYBJCakjOwghoOHC9oHfVET5FCIW+o1LBDdLzZfi0v6laNwsV2FT4nrAp2CaKPoZ/ea4gZogH+ZQ5m9XJEfZ3WIr0H4ifH/id7+A5+HZX+DgxDkcJEUTK/BRyoFTv53s4UYm/4wIcsmxIu7Aw/gYvoB5FriibC1/D5TNYGU5i1BzxM6dmOy5807qSy2hHsEAdyvKQXPRN+WcbXP2zcHbovZFYZwh6INaPoHH7jjq4PFJliScmpockL1Cb+FQ4YFCUlg5bK5RPHqzJSlosZTX2AkQqXKsEh+rhEqW4Uh2BZ0NGdaiBkFI8LebwWc+YMZms6E+QfT665GVWiTURWKrika2H0P9ZcXRv27d5+Qw+17ZeXYjZqCkMfu9HG7c2LRajGarPye/wKJsBuN0VzRJpxpgDqijicVshce/fbzhrqeX/G9S8ZKS3MayNNUr2sLuIxvf/FlWiSE5elZlmr/WG0dUtuplm12LdzZl/VvFba157ebnDq3bd2sy5koqlxcnGtIr/UZ53a3ul09NeusbONKn0SQWNOTnNpY47i5fOZjXwoExp7W2uYOtKuyidLXxbyI78qMHZKFn9rbZeJsL7MyOiVNrg3uSD1ETKLElEddyLRzeA4cAK+sq5TQT1cjHHODIG87iYwLIJJokav1xGlMoD8rzoC9vNA/b82AqD8byxvNwfGq9TTShKAvvq8dhiipWLeOuTZsUvzNnevNcUaWMrUTnrLR0V/iwAlEMPZVapfazvULTzbtYnG1y045v+zGTihcAYzgD9OJyntj+1g9eTSrrqK7ZUJuWVrs+UNEh2/GsiZ+0rEoolGZxgrs0mM5dmmxJLrDEW9e0Tn48+bu+Y92St+vJrYOPr3J7u75N9SflQPITyoHpaLPcsE2EbbGwKhVWEXAE7HZN4BjzdjJRjZ16mq76BLtjyHHAcdHBORwJokPTpxnWXNCMa3i2RNGhRMdoglqj4eujrfZMaENh9mIr16KyImH0f8PXH0cTI8sxoPQyvyCWrUQrW3vczO1TiEmUe+Z37DS8KJR2H1wxdLo3J2Vuc/em4qX3dcv6l6I39czvlhPxrLZH+8vWrI+qvH150eKH3tq64alvNPljc5ZsqYpuXevvfpTxxGLa08sRntghL2matXoWbs1Zm4OLoBawol8FLp7bxu3jOJXaqt6i3qPmYgJyFjpgumzCprxhR41dBaqvMIDFhHS+eo2IUutJRKQoDygMEDk4weRo2spvS2VSgo2iM8ca68/1Qm7E5o91eUkeTBv/ESYgeco4w307judgoENPx/85ygp44j+vD/28dcrQv7CmFcwQh/NbV2rdviwBvv1FTHqw1C1oU6W8BOhTxr77ya0DR1cqYx/WPnF07JOoib1O9ra61rpwa/LaZNxEVhOsqRWExBrZboNRG9jSh1MpFxjBKKWPpV9IJ+lMwcRQBUPHGtWnpvKOeqvIRv367q6xyAduRoEbd87RTWOOjZElARukh8dcDWZX7ab6zffGP26c03V4/ZUvbtkV6tx7ttf3PcPo3dmrGos5+L+mA91Fy4PZ2UtrfZAMCQ+/s6uk+cgvtseNPPuYbd7QSsrZNjropfxPUCLVrDqijdH6tZVaTq9lYr5WQz0VgwjRYrwIAZ66FSZst/lsC2zttiHbAdtRm9pgK6fBk7bztou2yzZ1STsN4XAescmLO4M2Od0TdNgkW4eNnFSAiGwDA8WCY+qjECL18SoDWNgcxliCrY70u939dN5hkw47ScE+lC2otcbmGVeeP7w6Yo21hAlhA78Fes48/LC1pKvBUZ1gzDZl+G26d8jZa7Xk7F3bS1bXuVWqfYS3Zpamr7iL8fjkEnKZq0OzUTV6Tw5unz0yGzM+xqvLoClqdRRuLV5bjNNIPsFpJsh0ghAbH7stdl8sp7JZbVtse2yc4AvIObOkaBiKvhiNo2uGVQGejXUD9d15vrTGkADaBEeNXIPfrgFU46gZrQnVcPUXa2CsBhbUwHDNsRpsqPHV4As1V1gINJmGWQX1dtEwt95iFerzVJBGxYiOS3kOW14KywssDytMZVqeucLUHxGeGbYwsEmmIBn8yiTjuq45FNr52eEAP514qE69+QAATmkb7ZKjv2va0TmnM5CGzSVNfcHuB6mNu+JI78AJLxUsDj/LZOyiZ3Z9d371qrl2u7yyKr97Yc7kkrSalaUJdQ2z6rYufiGzrthVPfLW3XdeuH9+z4r4soIMQvVtbfq1f/vv35PX+7/ZJUnd3+zbfHRllrfzcWan3kcZ4i9U+zjRhpeQhi0ZqHVBXUCuj4KjUVNROMo1jFxjrguucRc35gKDC4Zd4FJm8ZjYYFJcYCweULwYL8WPx1+J5zXxCSheZ0Gmel5UVI5f2TLrV5bq+zdFJI7OOBA5ekQZbJoYbKneqKxtzkkONHaWrdk93/Zdo9QckLtrM86coSYLkJ0F83NiC1ff0zjhwy9Ur6l2eRu31k3cyb85eYezojCdndfZR7s1RznJpkYDsp6oA4gtyEoc0XCs3VmWuCDHaYTwYtFFAULCmICPCtAnDAvYrqwvXVEyBAZunJUaXKCsT/EGzoIWAWKahKpR5vRMG2/G8OK2n523isnzWwjtyb4zZ87wjuee+3ycK/7idUbrEdoQQlumQ1/Ijbdj2K6BLWpYLHQJe4RDAqfs6rGTE9sQYRbsQS2hljBotbosDRCNIA6ySQ/pxEbdoO6gjrDXGd27ug90V3Vs4xnrWHt7qKWgU5NAuM9XaE85u75cj9mrXT+l5wz6cHBIzxfp5UWLgx36Yf0x/Zj+gp6/qAekD8c5pBf1kl6OZI7rr+gFNQa1ltMYeEQpwTRIeXlsEZUQSgi2tB+2Knxs8bq/0E+n1kJ/+yZjeFU7vI5NJ942J6iVkWb2K5EmH9h15gy8/85kLfwM/rJhcoh/89oKrJ/0TTwUphiUKWPZK+eSwBiCC+z0oYgkdAVxGjTOX+HxRR5C/BiPj/LQxw/z2MDbeXyFB5quaIhENoI8TIWzx/gL/DhPQYAiYn0IK8DpsVTOk10Kb++OnOHf/Jz9/AvtZVJCZ6RUJiWzqKJmUpJKpQTBUTRFmS59GCnzz3g6N5YOhnQYTof0aSmJjwqMMcIyejI68hp9vIoqnhiDnrUgJ7za+HVS4pwpHGyl2p9MLP5p0SFFsblN5Yp0MN3Adt0wSShbuDbQemdjOn5hwdq5idlN32iYuJcsmlVXKal5T1GJ2XdLns2z7P7OCV/E+uBstGfRaBYalEu2OfY58GDSriTMzufhbaZ9Jnwo6skozEWZo7BOSBSwjk/ksbLyr6YW6agBDCnDUgqkKCY/nXgvpkB8jV0DGnO9VkwOmx7hs5pt7v6vHtekHq2iJkXn1x1Hs33+l/7TO+bC7+84u7nw1fS69VXVvbdmeub3lFX33ZqFkyc/mPxT1T3vHMBS4J5f3HPH8ZXpmauO77jjiZUZ6SufVCzuySW0f/NRGipGB2Wxp3BbIe7J2paF96QcSsEpbNKNoRJTa2+x41p1ixrvIYcoFWfa3HQkS4dnJxkCSBRFSbwichoxVArlpdBXOlqK7aUwVQpjpeOlOMlTP0u0GgyJmvx6/p+wud3T+xHM6qYj+9VB/ipBMtoO9w2+4OWZsa0M+wtALXAuXl64urzvcFvGq3ElK+eVrl3gZaZ33aqSODxrx4VDTc2d2CGV2CZbeFV6sCRLICn+4oTcWp+l/v63dnY+ur5wVseJu5kJVrzxaITj8V7KF3ZUJmeIFsmCLRZnlJ2JYFj+lKPFGhSfEWOlDG4S1YawRvC/5Y7wMjtUxQTphi5dNyIsxvvCTEvdBy7GXVxfZM3QmaTksiUFCaRsVk1FcWxsSVmRuWxpiU1NnuD5wlX7GibeZG0rohbzi9SaKECvyk3sxAHutQxZDljIOiuk5kNWIlhygccWjHXJick4pdblQkEqiVIMHo05FhOKITFFw7parRyfHNRqPUFmXWEHNSY7isaK8HARFCnzRHpWsLwIxCKI8fCZ9Q6UAqMpVyjPpDjE6Hq+Q9enw8M60OnYSEdOwIQ/pqIiYIPbzw5sX7cXrh/RRNPHM6fPDU7bDOG9KJKXm8/opOxEqSIbUeTFkr5v9yx/aNN809HY0eHiFYF078LNgbnD3fI7P/nOO0nfEqSqJu/2Qff89XPdrU11hU5w33Jbg9sm99xiX9Igps+VZpdn2WOMWdVd8w8euWO/OavIZZhX5ylKt4m6eJevoplR1j51BWfxHuqBU08kIxp6ordF74smGXro0bND2mQ/B5xD0AfXc7dzj3LPcRyNRQV7rUNWbI3SW4kYEDQHwlsMDl7mOTU/HAcGVX0UO7wtGGIi6uCtNuX4HeMTv/9SbPiEkLuNuvlt/W39iieSxwzPAr/Fb3FF3HicldlU+B/f2JW39Uc/8pcnzLZpdPpP8C/u+utf75pourVco2I92E15489cMdXU2+TqQwQSnFnOYieJjw7IPt0BHT6vgwO6o7opHdGlD0PgYsrlFIxSxBSJDi2nSQmFvYhQ+pV0PJUOfREtrmdanKpsa4wlChmmVTZrrTJhXD8r6le8KbhhqyTiQU2bg0Yo/kdamyueWDitt/HAtedv1tujq/HPWT/VdEb6jPZTxEvlAsVTPAyQjwII70GHEC42zDPgRwzQY9hm2GcgeaSa4H8hQLrJbeRuQqLZ6SOObb6W0QC1eARsEEW3uEPEnGgOv9jRjV3iQfE18V1R874IX8b5RBE4ETQiwQzFlA4vxTgL60yJJuVVZ1pq2m86Yvqp6X2TZsoEr5neNeFjJthlOmjCHSaoMjWasMMEnMlswm+MfwnAElgmA1RNB1imKpFlwvsMFI4wTLCU4YFw+kNfqTX8IRTu5vrGv9qe6Wq57pkNYFCav1djOD1crbwqXLGqYGYTVOUm+Ad13tCmmzNxvQl8JmBrPFhtwAaBuWv+cj9E9hhvXGO8Yc1x+cwV/C9XHmeARnYlKd8W+n3LI1uUhf4vjyaxVUpFDtv6ncoyunICKcaPV/9y8raxv6hjzEaVKsZs0Xx6nnKrbC2vKrdYyivKrfiHbN1xgv2Km3JmFjklp/w+Fooz52Xi7ZkjmY9kkjyxWsSbRfYzAJJvC9hwPtW6jIeslA2LkmqTcFESJDHmVFiZ/dxEFnQ0pg/osXJ8zk9jCruDyELRtdGYbfAZg9F6qy1JDciV4YJmF1jVLhfbhMvMErOYBNf6coK1WZCbBWlZ8FkWvJ71YRY+ngWHsmBbFuRnBbK6skh8FlzNghdZ1q6sg1m4K2tLFi5SipiVswiaLNGgsLxgaDEw2eK0htc9H3o+9ZDjHjjkgW0e6PJAowfyPQEPjvfAVQ986IHXPPCiBw57YI8HBhWQIg+YPSkerPLATz5jRV/0MERcT6So4In3YFryJQ8s9nR59ngILeFmhYAW+cAD/z6N9VseOKgg3uSBTgYNuZ4qD541DXv4Uw/80PMLDz7jgSc9sMsDW1gLOz24goGC1ZPmwZwHfuf5qwe/64HXPUD78oAC2eXZ4sHTvUlhsMCxPsm/jPTqtALM2nfIQ6o8jR6cP11vz6cMJ7w73Tky6NnFsgO0OySFgVg9+CrrwocefNBz3INpH3qUDlSx3HwPvt7NJykGvE/pInSwNqTQqkjhcc9rnnc9Vz3csELWOg9IEbJ+oRQ7ppBmR5ginR6S6IErCvF+yki1y3PQc8bDlXsAI4/owRo1s/oyoo3BCjXkqmGWGtRJmcRgcGVEGYPZlKeUrxXA6iLRFuW4oLvNzT7s+Mn0jv/NW1/tMyXya88K/p0tgZt25WYCfOWgYXjHYFqgbwR3U+EOb631bzL6/eE/tpdAvR7lbmN/7O7/+3tooOyhzYhwh37+vMao0QqCVhOjOX1h8uenz6qj1WqNRtCIqtd+8KpapGGNRm1Qnw/h7yXWp3l82Z60hfaJeVRrOGMrHanpaSl22YL/MBGfUGGb5aKxygR8kc1u/OQSco3qEDt+Wy55kMCDGA6LcAjBveJjIr4XPYbwdtuI7REb6bHBY8mQLOrF4AMxsCcGNsXA4piuGPyACYiJyWoKzRJRnIbexmS7eNgOe+zQYoeAHeLtoLKDxm4yKoBGlRNUzjRnvjPg7HJuce5xPul80fm680Pnp86oN9gbO5kumXrvo+BrTmCZeNeNRVR/t7zKaaVZ4Z/AsYxwsu6hq04Yd8IPnb9w4jNOOOaEO50POPGgEzqcUOFc6MS5TnA4ATtNTvyB86oTK6DHnWecWIHsdA46sQKY4sx14n8Mt5jhBAXQynBCtwL676wBoMAeYg2ArweehpWfpNC0qSHW/YNO3OHsc+IqZ6MTO5ySE3NOsxOPO6848WvOd534H8MV0M5HwCACBBEQiCD6Sj5GToag3snVO4edo84xJ+dzAnKKTqymI40cyUZDVD2fqBxsUdhdmTgj29lfEa8v97r/zh7312/XKVG3smz6pdyxeZSKWpwvImJtkZ2V6z8ZcjIzMC09j1n8+eUwc3pdlrZg5dZbZxVTR8W4YK/fOLlo7AOt3R6HSawtWfvu91c+1lvCqan1tmWnm8ubeCaxtTUo6ObWL0zGa5lXpi6bvBVVasYmOyavarq/8l9X2vDH7HezkZ/QFn39/wv5exeFP4efQUvp92VWlhtA9SydfrsjX1/kuybybfn/4eQXI44+T8+IP839N3paVRTGOfOi+J5WPYOe5t9Q8ldMl2NxGm6j5bw0/DhLU9/LThsp+LK/rl5uYOq3EZxL6LOLQ8p3MYvTvtki4fvgDbaWiNjPoUZoeCRSZm8kf5dCl2doHKEiWs5O03Zz7J8VvcFsIKUcr4xCGyxEjegeGsPUdfahVmq7P86PIaL8ollN4wQBx9o8hh6OhAElo+2RMEbRaH0kTFAuKoyEuRkwPEqidzisoiGEbkE9aCVajTahFWiQhnvRRrSIhjci2n4lfSCS6kCzkZe69AX0nYvm0vRVNH8j6lSgHJSKjq/F5riObyHN6UabaStXUIh/DFtJ431oG83toWXWUAgHyqA1ZtJvDm0Fa4dDwdhJv0EFh4eGamnpVbSFDtrC9fR2zMAwoMRW0y+rdYtS1qu0uhZVoGqKbS4dgVq0AN1KU3sU+BX0GVTayPq5QWn5OprWi7r+Yf3X/0fT1Kvs/0F99TolyN8HNfOmlfdR4OT7YGwCTk4AmgDtgi/A8QV8Up9h/2sgw/4/gSz7lYDb3n556DI2XF5wuf3ygcsnL/O633+QbP/v3wXsht+B/LuA1f7b8YD97fGL45fHiTzuzw+MB+Ls/zXnYtNv5pCmi0Cafk2m7IZf2n+JlZf8k7jEwNs/hFfHSu0/qE+zv/L9DPvUS1B/ru/c8DmiTGLnTDkB+9nyswvO9p4dOnv07Mmz6r7Tx06HThPDaRj9LoS+C4bvgsbwnfLvXP4OGQ6NhnAoNBa6ECK+k+Un8bHnQ8/jsecvPI99z5U/h48+C2PPXHgGLzhx4AT2neg9cf7E1Anu0SMp9voj0HsIzh+CQwGb/V8OxtqHDh44OHWQSPfL9+Ph+6HvwPABPHoAxg5cOIAX3NN+T+89ZE9gyn50N+y6a7Z9cKDcPkB70Lux1L4xkGdPgLimeH9ck9pPmlS0zx00r50+ywKz7Utbg/ZW+o3JMTXxlCZcDmnqJWAg5QRfbphqwHJDXmFAbkjNCLwtN9ZDbcBhD1KcNfQ5GYCLgcsBPBwAa46lyQiGJjHH0IQBNQECu91Qbmg3DBk4g8FnWGDoNRwwXDRMGdTlNO2ygfQiGLYCD+dg9FTjIre77px6amFdSF2/NAR7Q6mL2FtuaA2p9oZQU+vS5lMA97XsvvdeVGGrC+Usag512FrqQp00ILPAMA2ItlNWVNEyODC4WfmxCIQDaNDtHhhgIWCx8A9JQAmBe4BmU7CBwQEaGdyMBtwDgzAwQAVkkKYPwHIaHhhgyQNAS9BnwB1GTzFQxMspAvoaDKMeGKDwA7T8QNxyytf/DzsOGUgKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iago8PC9UeXBlIC9Gb250RGVzY3JpcHRvcgovRm9udE5hbWUgL0xpYmVyYXRpb25TYW5zCi9GbGFncyA0Ci9Bc2NlbnQgOTA1LjI3MzQ0Ci9EZXNjZW50IC0yMTEuOTE0MDYKL1N0ZW1WIDQ1Ljg5ODQzOAovQ2FwSGVpZ2h0IDY4Ny45ODgyOAovSXRhbGljQW5nbGUgMAovRm9udEJCb3ggWy0yMDMuMTI1IC0zMDMuMjIyNjYgMTA1MC4yOTI5NyA5MTAuMTU2MjVdCi9Gb250RmlsZTIgMTAgMCBSPj4KZW5kb2JqCjEyIDAgb2JqCjw8L1R5cGUgL0ZvbnQKL0ZvbnREZXNjcmlwdG9yIDExIDAgUgovQmFzZUZvbnQgL0xpYmVyYXRpb25TYW5zCi9TdWJ0eXBlIC9DSURGb250VHlwZTIKL0NJRFRvR0lETWFwIC9JZGVudGl0eQovQ0lEU3lzdGVtSW5mbyA8PC9SZWdpc3RyeSAoQWRvYmUpCi9PcmRlcmluZyAoSWRlbnRpdHkpCi9TdXBwbGVtZW50IDA+PgovVyBbMCBbMzY1LjIzNDM4XSAzIDE1IDI3Ny44MzIwMyAxNiBbMzMzLjAwNzgxIDI3Ny44MzIwMyAyNzcuODMyMDNdIDE5IDI3IDU1Ni4xNTIzNCAzNiA0MCA2NjYuOTkyMTkgNDEgWzYxMC44Mzk4NCA3NzcuODMyMDNdIDQ3IFs1NTYuMTUyMzQgODMzLjAwNzgxIDcyMi4xNjc5NyA3NzcuODMyMDMgNjY2Ljk5MjE5IDAgNzIyLjE2Nzk3IDY2Ni45OTIxOSA2MTAuODM5ODQgNzIyLjE2Nzk3IDAgOTQzLjg0NzY2IDAgNjY2Ljk5MjE5XSA2NiA2OSA1NTYuMTUyMzQgNzAgWzUwMCA1NTYuMTUyMzQgNTU2LjE1MjM0IDI3Ny44MzIwMyA1NTYuMTUyMzQgNTU2LjE1MjM0IDIyMi4xNjc5NyAwIDUwMCAyMjIuMTY3OTddIDgxIDgzIDU1Ni4xNTIzNCA4NSBbMzMzLjAwNzgxIDUwMCAyNzcuODMyMDMgNTU2LjE1MjM0IDUwMCA3MjIuMTY3OTcgMCA1MDBdXQovRFcgMD4+CmVuZG9iagoxMyAwIG9iago8PC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9MZW5ndGggMzQxPj4gc3RyZWFtCnicXZLLboMwEEX3fIWX7SLiZSBICCmhicSiDzXtBxB7SJGKsQxZ8Pc1viSRigTozMwd7jD2q/qlVt3E/A8ziBNNrO2UNDQOVyOInenSKS+MmOzEtJJ7ir7Rnm/Fp3mcqK9VO3hFwZj/abPjZGb2tJPDmZ49/91IMp26sKfv6mT5dNX6l3pSEwu8smSSWtvptdFvTU/Md7JNLW2+m+aN1TwqvmZNLHIcwo0YJI26EWQadSGvCOxVsuJor9IjJf/lc6jOrfhpjKuObXUQREG5UJg4iiNQBuKgHJSC9qCto3jnKMlAFSh3xCPQ0VGCXJY7d6uPu6vHEEdXFq7uKqeNOAgmeYjgFkE44AmCkMeQc8jj5GbZ+cGMnN9KlleKnvyAILqkezgPoUPP9IAgeib4ehatU2GO5fcvx+S+W3E1xq7VnSW3z2WTnaL7cdODXlTL/QeL1LONCmVuZHN0cmVhbQplbmRvYmoKNiAwIG9iago8PC9UeXBlIC9Gb250Ci9TdWJ0eXBlIC9UeXBlMAovQmFzZUZvbnQgL0xpYmVyYXRpb25TYW5zCi9FbmNvZGluZyAvSWRlbnRpdHktSAovRGVzY2VuZGFudEZvbnRzIFsxMiAwIFJdCi9Ub1VuaWNvZGUgMTMgMCBSPj4KZW5kb2JqCnhyZWYKMCAxNAowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTUgMDAwMDAgbiAKMDAwMDAwMTg1OCAwMDAwMCBuIAowMDAwMDAwMTU0IDAwMDAwIG4gCjAwMDAwMDAxOTEgMDAwMDAgbiAKMDAwMDAwMDI2NyAwMDAwMCBuIAowMDAwMDE3NTE3IDAwMDAwIG4gCjAwMDAwMDAzNDMgMDAwMDAgbiAKMDAwMDAwMjA4OSAwMDAwMCBuIAowMDAwMDAyMTQ0IDAwMDAwIG4gCjAwMDAwMDIxOTEgMDAwMDAgbiAKMDAwMDAxNjIzOCAwMDAwMCBuIAowMDAwMDE2NDc3IDAwMDAwIG4gCjAwMDAwMTcxMDUgMDAwMDAgbiAKdHJhaWxlcgo8PC9TaXplIDE0Ci9Sb290IDkgMCBSCi9JbmZvIDEgMCBSPj4Kc3RhcnR4cmVmCjE3NjU2CiUlRU9G",
      "text/html": [
       "<div>                            <div id=\"8a553f5f-d811-423b-9609-9120e23071cf\" class=\"plotly-graph-div\" style=\"height:400px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"8a553f5f-d811-423b-9609-9120e23071cf\")) {                    Plotly.newPlot(                        \"8a553f5f-d811-423b-9609-9120e23071cf\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=For-Profit 4-Year<br>region=%{x}<br>avg of pct_well_served=%{y}<extra></extra>\",\"legendgroup\":\"For-Profit 4-Year\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"For-Profit 4-Year\",\"offsetgroup\":\"For-Profit 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.15286124862934833,0.1202115829962569,0.16882029870400217,0.19676037740764513,0.1453765578877513,0.3470619804339273,0.12062464928239883,0.16405975340778914,0.07818256117839997],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=Private 4-Year<br>region=%{x}<br>avg of pct_well_served=%{y}<extra></extra>\",\"legendgroup\":\"Private 4-Year\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Private 4-Year\",\"offsetgroup\":\"Private 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.4710773499848339,0.4636264086974191,0.42570187621177336,0.5516332631885233,0.4146223883626925,0.31332666412082694,0.36656553470957165,0.3191131299081677,0.14002217176174298],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=Public 4-Year<br>region=%{x}<br>avg of pct_well_served=%{y}<extra></extra>\",\"legendgroup\":\"Public 4-Year\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Public 4-Year\",\"offsetgroup\":\"Public 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"N/A or Other\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.25162128095380437,0.24680492578734567,0.4364904761859417,0.8285812219222976,0.42857362168480495,0.3342958905317087,0.194103421279557,0.3004625341169,0.31516631760050046,0.12362817090729518],\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"region\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"avg of pct_well_served\"}},\"legend\":{\"title\":{\"text\":\"sectortype\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Percent of Students Earning BA in 4 years, by Region\"},\"barmode\":\"group\",\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('8a553f5f-d811-423b-9609-9120e23071cf');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "df = finaldata_grouped\n",
    "fig = px.histogram(df, x=\"region\", y=\"pct_well_served\", histfunc='avg',\n",
    "             color='sectortype', barmode='group',\n",
    "             height=400,\n",
    "                  title=\"Percent of Students Earning BA in 4 years, by Region\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30ae3cc",
   "metadata": {},
   "source": [
    "The outlier on the right under \"N/A or Other region\" for public 4-year institution is likely representative of US Armed Forces academies which had their own classification in IPEDS for regions. It is also worth noting that in calculating the size of the student cohort and degree attainment, students who are called into active duty, injured or deceased are excluded from the data set, which would also impact the military colleges' performance data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0f475aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJdPr6eEKMSAwIG9iago8PC9DcmVhdG9yIChDaHJvbWl1bSkKL1Byb2R1Y2VyIChTa2lhL1BERiBtODgpCi9DcmVhdGlvbkRhdGUgKEQ6MjAyMjA1MTMwMjUzMjIrMDAnMDAnKQovTW9kRGF0ZSAoRDoyMDIyMDUxMzAyNTMyMiswMCcwMCcpPj4KZW5kb2JqCjMgMCBvYmoKPDwvY2EgMQovQk0gL05vcm1hbD4+CmVuZG9iago0IDAgb2JqCjw8L0NBIDEKL2NhIDEKL0xDIDAKL0xKIDAKL0xXIDEKL01MIDQKL1NBIHRydWUKL0JNIC9Ob3JtYWw+PgplbmRvYmoKNSAwIG9iago8PC9DQSAxCi9jYSAxCi9MQyAwCi9MSiAwCi9MVyAyCi9NTCA0Ci9TQSB0cnVlCi9CTSAvTm9ybWFsPj4KZW5kb2JqCjcgMCBvYmoKPDwvRmlsdGVyIC9GbGF0ZURlY29kZQovTGVuZ3RoIDE0NTM+PiBzdHJlYW0KeJzFWNuOGzcMffdX6LnAKqJI6gIUBTaLJM9pF+gH9BKgaAo0/X+gh7p4NPHaybob1961PTMciufwInJ85NpeLuB955dDDsH98vHw98GuRCrZq6OowX367fDzd+4vXGGPE+3O7RduIWfvH9+5/uPTh8Ord+w+/NM0ZaiV0LT8fniP9xUL+FKL8zWq4jNJtrVOzm3LluDIVk3qYohz5ae0Z4m+ZD0HQoa24D4eVOyGPw8/nVGlEl9CDQu1Xy+gqgr789j0jCbTZU5hiuYf0a4uMXl8nXfVdo85jEuJzguTwDslSPPYycnNZeKTBrzIJfUpIiIzYsP36EyOOHlO+KndmwqSTD66mH0qR9GMu7J4jqapSdbcNUMyeJG2BKSlLQZ8hbymLkoingRaxEUxsFOuRmjNxmWXq8EnkwM7PoU6jT3KIrK5dtnIuet0CQpyS7VVlLFQDEdrY+GpOngyIbA0wUEnVULc2/UmzhyG9shedGo/SufiVY7KOaehPFZPaS+JPJAu5itnhrPwcqgP1H13chK+o+yDtEVhtpbG8sotFS+Ey6UrThDq4nBRCe3C6ji4EtSGmbEECoK5jh2sq6fogCLYWoMM0uS1y1Mgn9IJeSEAQIunTnZgLOAyYolmXBxlwWxZmI5SodxZctKJE5PJ4veIj1hT58UhoPQYGlFgjWYvFt08XIL60zhxrJ8x2MRT2jHIpQ7N8HdNqyFNHP4OQ3NAgXRQiYyD+5Yj+A3EiTbEGnysO2LNEciu1KO6qcrqq7RDpFDWz9MnzrSION/lMjwdT2iKCIe8xTqlAq9aLAhNV22y2BzK4ipSmOxEfYk7Ofic8sZnTASdtgSM0ScSAshHmHMojQVy8EFHdRTTekTFKkMj61MpBg/pEoSCyOtaUS+VT8IqowgNWkXy0Eyqk5lFVtIk8au2ToAIiSOYsIoG8+/G93ahBKyTSgNbrZ5Gyq1ak+2fPkompHjGPZbxJye3av368fDqrRUQ9wg/9lbCvhgh8fjx8D3CEYls1bYnJP7uf3CPfxzYQoRZrd94/NVBUBD0ilTV3ATePPYN6MtgQJ3PheItwNx3IGZrA5U7qPi2H8ubieOZGChYqUBpvQEIi0V5wP80fgCRAUJpHJvMs4FEVEYJ6RbeYBps3w8gpRsv93sw8tyIIi7JZ7pJfmDnPhprjDf2n826Ip/Ax01YR/xrtNDo8a4PI7FDP69lCaH/BCqh1WK5TU6kxXgz/PWS5NcUJSrYd5Im/n9st5S4uqBSRYtaUyk3sL10+1sAdTsjCmHgGtJ+Z9BReR86yAZ4HF9VcSMR+1joFg5C9FPshdaATtsb6LgPONUVx6VR7oUMRUfNaJZydJh5WgdYhtnEl01ZRuYXsiXBFirWFJzagn+j8Qv0jNn7dvbky/YsQ/ztbKqXbKKMraJtbccZ/eJTGwymrqS18/xcg2C6w0FMZQzGZ54LPLHy1ZzIjhObtH3sTw06F1b3Wv1O+3LRflsJsV23fB1PYsP9ywa6BHgv5dV71idPK20Ksr5A51Zbt8JnRQPDQ5d5aAgwSlQMurqvmL3pvgyxDmQ7iM99YnOX3F1DSBeesC10Ys5r/eE3pXPS12ibI0juzDxBoSJ+9GTTeRaFK6yvfnByHYXK7Ftf9O0pNBZ0dKgPPZ+eos+e+tSCifpa+lZIl55YnCfsuufJ17KXd+wxAIS1oR8di4wyNPvhuftbQrfdf5an0sexVVZnW0SjMORtypkDdZtuHrapx67L6DLi0CfrzDqif97TXPl6rNNdyUn9uWIyjHo7lOjS/Ou2WFNuBtN0/ZmqXdkHQZfEcBg6EM6zeF9UZn72Yf+iSgw82d117/bP81L49D1es6jHScB9ue3oTgLazFBycfG4IzUG6+amNQzabjRCQWI/bqBnk/u2M92upS28mr6yH3DfH/4Fr2Ys3QplbmRzdHJlYW0KZW5kb2JqCjIgMCBvYmoKPDwvVHlwZSAvUGFnZQovUmVzb3VyY2VzIDw8L1Byb2NTZXQgWy9QREYgL1RleHQgL0ltYWdlQiAvSW1hZ2VDIC9JbWFnZUldCi9FeHRHU3RhdGUgPDwvRzMgMyAwIFIKL0c0IDQgMCBSCi9HNSA1IDAgUj4+Ci9Gb250IDw8L0Y2IDYgMCBSPj4+PgovTWVkaWFCb3ggWzAgMCA1MjUuMTIgMzAwXQovQ29udGVudHMgNyAwIFIKL1N0cnVjdFBhcmVudHMgMAovUGFyZW50IDggMCBSPj4KZW5kb2JqCjggMCBvYmoKPDwvVHlwZSAvUGFnZXMKL0NvdW50IDEKL0tpZHMgWzIgMCBSXT4+CmVuZG9iago5IDAgb2JqCjw8L1R5cGUgL0NhdGFsb2cKL1BhZ2VzIDggMCBSPj4KZW5kb2JqCjEwIDAgb2JqCjw8L0xlbmd0aDEgMTk4MjgKL0ZpbHRlciAvRmxhdGVEZWNvZGUKL0xlbmd0aCAxMzg3Nj4+IHN0cmVhbQp4nI28C2AT15UwfM+dGUkj2dZIluSnLAn5LVkaLD/wA3sAW5ZxCDbYYBuMDRgbk4BtbAiPNJgECJgkOClLQkkCbWga8kJQEkjSFLebTfpIGprS7XbTLW43TTdtKGw3ydck2P7PHcnEkLR/R9bMfZx77rnnnnPuOfeOTIAQYiRDhCN9wWDjgrUTY11Ycg2/qQsX+/O3r300nxBIxHzH6vUr+4SnNHrMb8H8m6s3DzrnPTbvT4TQKvzauvq61w/8wh0mhF9OiFboXjnQRxKJSIihE+Gl7tu3dsW8+nMPITFrCSl9au2alZ0ffedEOuZlrC9aiwVxdZoOQmokzKevXT+45eKPzYswX4B9Dt7eu3rlK797C/OGQaz/3vqVW/r4H2p/TkjoKcw7N6xcv8bxx9KLmP8Rwp/u6x0YnBwlhwiZfyer79u4pm/bj1Z8gvlHkd5fYhkbPfnPhq/z7Hk+eXKIPd9+aMHvCMefgxEiECJ8QwggYErkyf2cdFGzTqAGDU/ZxVpyZNo1c87ieUQhTvKc8IuJBghoK+A0DunopV8hX0qFnYwSQvFOoy0teAf1yZNz+MzFep7o8S6TfFJAisgsMofUkHrSRJaSFtJOesjtpI/cQbaSY+S5yUkVo3cabBW5hSyKwq4kt5ENZOMU7OR//xOf1ZOrI5z55y7hTeFN8jUcmZVsVe83XHwpjuwOQiY/ZLkv7hNL//ke/plLF3mcIa+SkzjW6ddechfen72h7Dz5V/KMmjpC7v8HaF8iT0dTB8lhcu/fhVtH7kE8x7H/L64OLN1KHsGez5HvoAzNgAD2elu09l3y469GBb+DH5OHyFMI+RA5i/cjKCzb6V/JQ3QR2UB/xe0kd5N9OMaj0EMOIHwHOQ7LyAosjVwryBrSexPSYTJCvk22kaEvioSdk/9HYq99Bynfh3gOoVz1T2vxFHzKHpwDaX+evKCW7Zyq1Ia4dfRFSse/jpkHSTd+V8Kvkc77OZQ/wQQnCFGqW5qbGhcvaqhfeOuCW+rm14ZqgtVV8+bOUSorZpeXlZbMKi4qnCn7fXne7KzMjHT3DJcj0WKSjHGxBr2o02oEnqNAvNXuYIcznNkR5jPdoVAey7tXYsHKaQUdYScWBW+ECTs7VDDnjZAKQnbdBKlEIJXrkCA5y0l5ntdZ7XaG36pyO89Ba0Mzpu+vcrc4w5fV9AI1zWeqmVjMuFzYwlmduLbKGYYOZ3U4uHntcHVHFeI7ZdDPc89bo8/zklN6AyYNmApnu/tOQXYFqAmaXV16ihJdLOs2zGVUr+wM1zc0V1eluFwted7acJy7Sq0i81SUYc28sFZF6exhpJP9zlPe0eH7zklkVYcnptPduXJ5c5hbiW2Huerh4XvDJk84x10Vztn2XiKOfE3Y666qDnsY1rpF1/up+6JLCAsZkts5/DHB4bgvf3hjycpoiSZD+piwZBDZOzwcdDuDwx3DK89NDq1yOyX38KmYmOG+auQwqW/GVucmX96fEg7e1xKWOtZCaXSwwUV14fiGZc1hmhF0rl2JJfhX6XbNSnGZWqZg6v9eNUFGIDuQpy4XG/j+cwpZhZnwUENzJO8kq1JOE8XvaQnTDlYzOlVjbWI1Q1M115t3uHE26xY3D4f5jNpOdzXyeP/K8NAqlKd1bCrcUjjukxSXe9hscpb4W1RYJ1JV29njDAuZyBZsNb0BSgprMiypmbhPIo/LKdhBpsnsLHEjGoan2l3dEf3bvDYRETjzvOGQJzL1jc1hpQoTysroHFWfkv3YYmUHTlFPlTp9Yb+7L2xxz70+n4ys6p7FzWqTaLOwZV4YF/Voq7C/uor17Kwe7qiKkMBwuRuaXyKBybFTBc6U7wZwhWmpYsC2eShXmdXDzZ1dYUdHSidqWpezOcUVVlpwglvczWtamKAhh3LGsDuX2mOYzmtsrlvsrmtobZ4VJSRSwdDxGdU3oXE3p0TQoMiFdRk6ZzNN4VoQUMICZxAT7rnleA9rM3T4lZDhaikT1bnlzmZIIVPQSEY4x1m9pioKx/I3IBWYOM0LTWHTsCzimRdKcbW4Ileel2K1M9oxttAxpoamqrgMtARYRhGNWsR4mchk3tnsXuNuca91hpX6ZjY2xh6Vy1FmqDyPzlXjDblpzEI2ERdWT2UYM8NBT8p05oZr1Pz1bOim6tqpauewzl23eJghd0cREqS8NkyYCCuzTCmq9jN9dgdXohKjRqv6PHxKUZgur2VqO+yu7Rx2L24uV6HRgnwtZRvry0zqoK5xbp4XjdncU27Y23BKgb2LW5tfQr/Oubex+TQFOq9jbsupdKxrfsmJa4VaSlkpK2QZJ8swTIswo1PhU15SCBlSa3m1QM2vPgdELdNNlQFZfY5GyqSpMoplfKRMUcvYhbOUuBZ5jPa72tnJ5ufOlrXDHS1MxokNOYJ/EAZ3BXLHXXEKqCYmrHevmRs2uOey8kpWXhkp17ByLUoG2CDPu21YqnZ/nJgXWS45dE8oMRFAb435fJ5/+FmHfsYJ8nOowc8FuECL8DNGx7iTN3/4lernB/ykUCv8aeqjsWoHtZ/r+nUXdRfFfn2podjwhOGJuJS4FOPyaZ9txm1SpvSKSTa9ZM42fyu+N/4/LBn4+b6V4qfXesm2HT8/Ttic8KvEuqRHUl5OedneqPqFYPrz58EDr7Qbyz8mjojfNd13nugY/7quWxsiRB115KKExQidQhOOX0t8p4D4y09red3l/FMa4TflpzmKSXKKY8UCKz6t1YjXyk8DKw+YXKYMl8lVRZ0T6fDIxFqh6bNnqvi3kJpzE5/BTvSlYsitLxF+cuyswRTSP0wOac9Njio20RTSDsWMxFAlpj4mHMONxByLoTGsKi4zJxSjxEqhGO1T5IiB+MffA7/H09a/MeAZvzxT9mRYLXFU666AQnch7BQtdsv2vJnN7z5VuLRurnPOrjnvsuhhGc7OQtqHY3IpFoKC1Uc5HPErR+FtoH4AHGVb/2Xw+9tmyvGFLusy+AguHDuGLV/B5nch3RzxKU4OY5QRbI4+fpiMkatEGEH3jhJGHwF0//yIg9GGhAUKA9ZX/vXdd9lMTH5IS9D35khASd6KThyFxFhTKAe1SyIo+U46hHpCzk1ePcOg40llJfhnvdU2a6bc5mmzQgDgyOMTPRZh7DMnm6H6yQ95t3AIAbNIn1LfktGTQYOuJa4uF7ckpSuFtiT0JFDeDJtNe0x0a+y+WGqIAYMOtmr3aelmbg+HvYGWbFBGLMcs1JIzlLahUg/6pAGj1j0gJJHKy5U4jrbLydJv2pJxKCvapl1goXHgnuEDWlhgroBAfhqYtQU+6p4RR62WNBrIr6C8e8HuM13dp++pq9t1Zt2aU7tuOZt9a3/olsGF2TkLN9bWbFzooT/46cSfn5k//2mwvvlLSHhy3rwnJz745VOXdhfP2n3pO9/67b1lZff+FrnRjZFJDI41m3xTWbCJh03Ju5PpNmlYomsyYEkG5KDd73FxPW5IdUOSFTal7E6hmhTIsm9QdEpmbkjRwQEd6HKHzBviBzN3ZdL4TEmN7Jh0OdIyQ0SX+YAZlptvN99p5vTmZDM1xw4kaiFzEIPiyssllZcDOGxzCZvZQKDNr7Kl35N8OR+np42oPIk8A4UVQmGUFdqsCg55gyyJ02hd1u7A1791dGhhelV7aVH7fL/2nDh38Inbeo73lwea+rbduX5JIr20Y9N3H7zzzr1LypdVONLKW8pMt+xZU5q/amRFzdDg7d1runpKDrPZ9yNHijF+05MEskmp/4YRDmtgrwZ2S/8i0c0SJNpgs22P7ZCNswlKjDUkLNPfpt+u5/Q66E3QOCj0oZGiTO5kWo9JQUcJP6Cz2cCg0QAOuTJgLvEHLueDfwVTM0zlt/k9gbZ+Nl42UjC5Ta5CCJgCVojjtOzr4vqfHl9Ld7/6+sQIlSxW3cTDQrzFooG/QuXED6DyPu7Fa7c8wN0h2NMzYsY/1CWnJGtxLGtRkpNwdtMxHj6pyPdZYZtt2EZbtD1a2sqt47ZxXCPtpLTR3ekedHONMzpnDM7gCtOq0+jufMhHjVHy9LGhXcmQkVyYXJ28KZm3JYO112JB6fZnHM2gIxmQUTrkt2+Q9E491bNJT8RJ19tzNzmdBYkDSdbDaEWN2oKo4AfaLuPo2eA9bZfz1anGmfZfVsfuYdMMU5OOducGPaAJWh+oahDJfkkrksoGnu7dfm7b7Oq7X9kcuqv71oRnU7c3zN/SmDfz9EDHo72zz6aH1gVndjYEsuvWzZ3THcqEt9ad2lGz4hTA8e9B6vc70uZtqHcsawjuf+e+Ze0Vm77TV7t5cZ59zrpbbr23szSvaRtbwVqQp8nI0xwymzSQ88quTdm7s+km124X3WTfbaebUnen0k2JuxPptoThBLotfjiebouBbbphHd2mHdbSbdwwRzfR3ZQ2Va+ppk11a+po65x1c2jBBmvuBocx3bHQClaH1aFHJhsrHBXUUeGvOFrBjVRAxeKh0IYLaEzSjfM2lZQs8G9K1i7YFLUpjLElqEf9ERXamHzZVIKedL+5pES6LF2eEi/G4evPKJeRyRW0uNDHsWdUr6jWmsZxNzE5/mamJ5dsevHOO1+4o8S/cE1RWVulq6TvyfUDJ3qLXJVts2evvcX725SKztqaVZWpttKu+qbuYpM7tWpgycLeanQhNzYs6quyw77WhzdUVKx/uGXBlqWFIh9XsXRd2fx7VpeUrt41v3Td0tkxvL5w6RZ6S2Fzpdtd2VyY2xzy+ULN408E2mvz8uavLJhz24Lc3AXrUeJ5jOcXofZqiQXMys+66Ga6h3Jd8Zvj98RzPbAV9gHXY9lq2WfhBjT3aOgaDdwp3CfQdQJsI8OElpAWjP+5TdxujhZxS7gujmvlIcRDkxZqtBBPObAQqyZDU6jhUJ3f13yioclCrlAqcKIAHwifClQjxMbyySSXlBJOJPAB+RTpkrROrazlnFrQam1WLoMr5DgNB+9zn3AUHZjzPOXrbWEblW0dthHbqO2qTfDbAGi7JT5+cyzEChxPzKQyHxUoYjza2vr9bWg0+k0BXFtZwlwy2x9gCTVdgn+RGZ9+uTgX54aACD4uC80K5+IPfHP8rm+9Tit/TYvGn5fsNiPQuAS78Qw1wqMTncLOz3bwNHvRvDxB8FUtyp6YiWN5Cnm8BnkcQ5JIpxI8kQjbE+HZBEhJ8CSUJWxP4E9IkCJ5pDJpu8RvR/vJwVYKpFXx40BShpQUsLQSjaRRNJxWE9+u5SztGnN0VVzRdrkNpbc/+fI04iNrIjo++bwtshiydAUV1qw79/mD4/8L7zwB8a/3ji46+LPtE/8Lpb2vDt9K3w5P/N8LbcLOhhMT184c+Ondsz8/FXrgIvNWkH5+Auk3kAUvaJEJRDVbGVpDSK+PFcRWQiSiEE5LnLGga98hgCCIfDtwYjsxTykaejNtjNh8ZL/HzNQMXRKTy+qKfp/i8649xOVf+xn3sLDz0Ynyb0xYH53q+0HsWyQ9LxE6OfpibGKIUo2OUVCqjQnpdAYQvuCObAC+/QqAESqhF3bAUTiJLtUl0OlASUgLAQgE+ScgWSpVnhVtNxHm98yUQaUJ1xQrdHPma385z33A/2H8o8fH/w1JYz7A5IfCMbRoeeQl5e49HljngbkZizKokGhNbErkliTAkngQzFZzk5nbFjMcQ1tiemLoOg7WUajNaMmghXbYHrs/lipomPTpyx2Ky0V2OA44qMM/5PR3+If8nHXZSXKeUCcBI/boar86A2bMEJLbc+KldkE2KAY6YhgzUINB4Eklk2gJ+WwqQS8JPaU2VSjebMN8soSrBXqRpC1qx6JXfIG5KD26SHDTDVRxoavQZYrDsvRM2t1yCjTP3f3aSKf7xeTatfuah169c/bcu3+wY/F9/UvsE8tok3/H49+77fTEJ6da6BvfBv0ra3xLti8oKmgqd9UfvLBjxy8ON6XmFadOHJtIlpfOzVz/rxAzNat+nFWBzFKyJV5Gb5OXeCev8B38EH+Mv8rreO4LuQKOi4qSKvB+tvTjas/E5jz9kbDz85QpWdFQxOqlXiXmkRx42AExkjkxhI771e+igxvL1ucULMhGvZKwINfpysCbORVv6PKnnZsc+y6WqE8sZE9lEitiY9LTU7zLc9NJAaEXCewnRwjlCeiIb78PBn1Q5oOf+uCMDww+ePsZHxT4wOkDiw+IDz7ywQUfvOaDMAPd5Tvu4zp80OgDRYWTfMD74OGrrPlrvvd83DEGdtBH631Q5QOZVaf7KGIZYyAXfXTEB7t80MdaV/k6fVykp0g3kQ5e8/EdrLrRRyPouxnGCH6hPoKxysdZfBEMu3wM70c+HWv5kY/bzyBY60EfX6wsfk8dHGsRwSLgIBk4fcUHrDGtYwSglfncB8cjYxhCN0Tx1fv6fFwlY4LTR9NSlpNUJZVqUzVWK9NeyYy8t9q5unQgselcKs5vQqCtMhDwmALqNOejmLbjt59dG/Fifn9/9Nk2VTFVuXFaxfXKFZGE9Bs1kX85YvIxSotcbezb5jIVFBUXFWu0caAFN7P0mVm2hDSwogkIQBqXUMEVo3MpLKGUcnHGWIdx4uCeiQOaWKNRa5IAS5/+HO7QWsxGjpOsFh30fcw9G1jnDciBfM/KrGsKN2rMzvMnFJbMKvZ3Z11rFHZe81sq55ZJUvncCgv38892RKVXcKL0xpPXXiJ6lLysGE+oSB/UU+LEVA4pIbWEM0h2V8jARPaQ4UkDzTEAMOnGQmDSWogV34CngXKZAPHLSZXUKB2UuDEkk0iypEh90qh0QdJIihUU66j1gnXMykemIw4DYO1yopN0io7T6kAttHtCJhA5g4IJA9HhHOH8sG9khjxsUW3H5fQyszDtbYydHg+gY+pxwRc8zMzyQSHqK186HoeBLvf0b+lnHEZ9z/LhmXLOMve1ZtTh0MyZuavzuEcZNyhZiTb2V9GY8nZl/rIMSM4AMQMWucDqAq0LGlPAmgLLEiApAbpMsDYGo2rFaAEMH505Qzk0bdlJ/Xk9derBqHegp53UbuTd7UL8VDTZ9lULZ2Tt1PCq8SuMmMipJXSajRR+1X5m4tq3n5/47Pnm5adBOPEUCKeW/+ucHd/bftf3d1TO2fHq9l3nt5ehRZz46+haZvSOqrax63sTnzyx48LB+inbuOTwxYh/wP8JZz5OjaAWn6Dw9QQ4Ij0jUT2XzOVynBBjjcmI4dAjMCYNKUmAfxZta3SqdJp2h8VvWWhpt+ywCEbL25ZJC6e1KGi3LBZtfLvIaW9c7vxMtfpx7MmXI+tC1KtVlzw1TshU/YUi/k+zt764dWLVedrwtZfvrBg9fnxiN9zz7SPcr5cf3VQ1/q6wc3bvYyv37B+/+FDUy1mLozCRNPKOUnEiFoYt37A8beEO2kF19J8n8BiBO8l9hM4nrWQd4bhjABvhbngIOLoaQAG2tZABwEnnJvuUpabaPmlIGpG4RqlTonNRht1SQKIgSUnmVoOBEJNsUkwdphHTMZPGpDhHnMecXBKuG7iWKDyn5SXa7k9qT+pNOpDEJyWRxPbrXiHjRlskpERmoGOIoYC5xISmQTU5HnZjqfao649WAqb4o7r6wH3hVvH3TTw0UXuePrzlpbvmZjXevQxG/uZt3HLLRBm8tWjLggxaO35W2Fm89tCKeXfffqs0/k3uQ2VFpWP8bzmhVUzz2zBC+hvKeiX5vbLhUAVUVcCTZbCnCHbNhIez4YQLDK4Ul8d1xMW32E/Y6X4T7NfCIQo8tVC6qxQ6iqDHCptNkNuak4NSEo6H+DlDYqtOkeLRQypoJQ7JoTg4rSNeireFtsTvjX8kniuLhwK2Ivqx6I6CewseLuBKCyC+QPC39+ZCSy7U5YImF3LT+bj2DhEWiVAlgsg0iG1AXFbvLDotYYFoW8QioA8V3aZRY9Sp3Ygb9AtFzZmeVZzGMVVi+xNZPmEqjJrujSSkCfzfqkfePTTx6cR/Zr8UV7r6oa6mB7pKKjc+3lF2x/qOYHbDyGsb73l5aEHC9+IKl2xfvGp3g7vy9gfq5+zc3H2LB3a3HFo/+9zzGcWtc9Lt5e1zq5fMyrTFOjylDbcFOw8sz81ZtLXeFagvSnWXN/grG4rSzUasbNyIM+JDjTzDoiK4iA6ACD8Wfy1+KnKviFArtohbxX0iX4a8EJNE+okIh8Ufi3R/JF8r9oj8G78WPxDpT0V4UYQcbNCDDQ6LQooIGhGSxBwVx2HxBGLVfoCI6bsinBDhkAglCEvzRACDCA/fJm4X94vPiK+IfxY/F7WNIpZ6xDJGx+ciPS5CmViHIFy6CPvFIwj2UywXdohAF4rtIpVFMIrQ/bZ4SaRhlmalB0T+qghHxZMiK+f7RGgXQRHBwf4qEaBXPIoVV0QtEaH4ighDSps4Il4QuV4R6kXwi4AVF0Q4KcKICL3iDpFKolNUxHqRnxRhTITzDGEHNjom8pUiOFUy0Onm46CVKlQb16c9pg2z2G5IS9X9VmNCakjrpLhG8aiioJor1M22N3F98UByorRg/L389huW/esL/nUPYEVEY6/n2qPQ01yDCChz8NmGkctKL3x/IpXfw//h8xT+D4+qPuTjuO7k4Mwnkb3KbHXjsoXr4WgL7aG0WoJqPVhbFcVcbz5m5mTziPmqmTtgPmo+aebMaqSmpIymXEgZS+FT2MDScVHVtS4U2oVegTsgHBXo2wIQQRIUgdMK8Routp2oK5Lq8bCNWo9qmdXNiCnjLLDtBlw9o4uPmoyj9N3BC48tP1/StX/xooc2VH6/7divLBX3/HiY23ntwG3/ssLj7TjSy3Vee/D+t/fOxXGJhGj34Lh8/FKldqsXNplgO91PaS2FwZhdMTQYA9vShtNobVpLWk8at8mx20FrHEsd3Q7ugTxYlndb3vY8bqcEndKgRJskAGfEoUYHGRP3ENhEoIo0kk7CFRG4Lw62xUFd3LK42+K4OEmMCSXH5caVxnFiHHwQ9ynqd2xGbGEsp4mF92M/iaVxsbbktNy00jROTIMP0j5FI+vIcBQ6OI0D3nd8goFRmjYZcqEUg0qAD+BToFaSQQoJpyHwPvmEYLictT0LBm27bNSmzcrS2jhjcl5uXmkex+ny4H/y/pZH895F59wH6LWe9MERHxzwwXYf9PpgmQ8WosO60HfAd9LH+ZSk1JDTJ/uo0QeiT5DgD9LHEn1Rel36dwk9PJ1xlnGLca/xEeM5oybGqCiTSZkh4x3yw/KvZK5IDspLZC5BzpKpRoZiuVu+Q/6OfFZ+Q/6j/P9kXaYMWjlBpj9+A6H/KHNb5Efkp+RzMt8jQ7Y8S26WuSQGAv8jw69leEr+kUwPyzAsQ7O8Vqa1DCXo5ESZ/lGGH8nwnUguWw7Je2Xh8BsRuL0qVqGW4QRRTpLpf8j/I9OfyvAN+Wn5ZZnbL4M8eueOUIkMuTJgj3oZPpXhT2qnP5HhnAz75MPyCUYgIGml8ny5VeZyZEiWIUaGteMyfCjDf8nwpgzK5KsyPCPDozIg3jtluE2G5TLUyVAug0eGVBkMMlyT4c8y/EYGpOJ7U/Dkfhl2yLBehnYZFsjglytlapfBKAP2cEXt4W0ZEP9JGR6T4QCD/ZpMl6nQZTLkyZAiQ6wMsz6X4bIM78rwlgyvyPCcDEdkQPTbVfR18jKZlqjkJKnkfKqS818qORHyH1PJ/5pKfptK/mwZWAOHDLRd3iEflc/Ll+RJWUOQ6VXaRjReaXmckctSjLbttv0oeE4xLmSDuIiX3GYKsIAGUK/b+/vb+6dfG2+8bjjQmBbYtH81+I3wDMBzHWrF9PbTbB/LMf8vwEwkszWetmkkBdqwL3MCRkd+9IxNU7tiX05g6BTt1uO5iWiXm/MBhk5qHMU2zeLTIMGWUFSM4VM8ZjjMsP2F+IDwx/c+MiTpY2JiYwyJhk/em1j5xrjJYYg1GCVtnNGo+fjsxxqjMU4rGUFKtBs/fYPbkbnWX1xSWix3ZV7bieHTzso7Z5YWVM9LrSgvTuDWX/t6QnFZReq8YPXarQWcGk3loTfK7LiOLlYsaVqI0YI2McYYMgnACSBxwrnJ/1EmsQCXJQq6OG2P7oSOanQ2XZEuqOPjdBpozaRL6CHKbabvU1qClpJq0Pekh/Un9D/Wc0v0IOpL9DRJ36Lfp/9Uz2v08JNP9fA+K0/Sv4Qw/Ot6aNFvRXiuSA85CP2S/gM9b9DDYQR8Xf/venpaD8f1cEgPd+thUA9L9F16OlcP6foCPTXrgdfDRyrK1/QX9fRJ/Yt6+pAedulhsx5W6aFRD2qAmK4Hmwr8Vz3ABf2Ynr6mh2P6sJ4e1EOfHjr1oOjBomdoOaKHnvf0H+npBb3yIvZ+Rv+anhvSj+gpElCv79DTKj04GTqLnmLvY9Hew6y/Tv2g/qD+uF6Q9YraL2G1iGwk0iBdX6VvRJhdem3JGKP0ODbl+lgl65whENTOR/VwRg/RVqxil164qH9PT19ROYItqMxoMer9GABzZVwdt53jONDxEf8gYC5BOYWoh37jFsHNWvOFurR/oRo361XElbie8KuYxkt+hs98D6AL8n4+Cn+7qjnqiRPbFQ5w4vfHf/8OPAfPvEND4+doiCsZX0mPogRO/m6ihx+e+DPhiFtJkGgHHaLH6AUqsMRV9bj3ZVAPaNXtLA7dEQd/YqLn7rsxllqKEcEAfyvJJ3PIN5X8rbP3zaZbY/bFUJotxob0QrJAPYkY4Amp1lSakZEWVHxi76wdsw7M4mbNG7LUqBG9xZoasloraxwccPK80Xn02DyYxyqcae6QqyHbVtIgismBdgv4LQcs1GIx1idLvkA9saFHgiES21U0sTMSjJfVQP+6d5+fz/x79TTYQ5iDksn890q48bDRZjVZbIH8omKrekBLs9xxXBZagNmgjeOsFhs8/sTxhnueWvp/qaVLywoaKzI139PP6j6y4c2f5ZYZ0+JmzMsM1PoSOY29evkm95KdTbn/NveO1sJ2y7OHbtt3axrly+atKE0xZs0LmJTbbvW8cmrCV9/Ac306XUpxQ1FBY5nz3spVg4UtPJjyW2ubO9iuwi7kq114kzhIgDykiD0zt86kW93gYH5MolYf2pN2CF2glJYUWsu38HQPHAKq7qtUYiWpUY45wVk4lCvEB4lZMsvo/fE6c7gQKguhr3CkkDoKYbIQRgvHCmlSRr1dMpMYq+CvpxGOql4tk66NG9W4M3/qQFs1pUysJNeMzCx35AUCTnX0NFqNNsDO78w3nyzx9omN258IUKYVzwOlcAbw4vO/ve2tH7yaWtFRXbO+NjOz9vbg3A7FQWeM/6RldfIseQYvespDWfzliZa0YmuSbW3rxIcTv+871i37up7cMvj4ao+v6wm0nyiB3E9QArPIJqVhqwRbE2B1BqzmwBl0OHTBYyzaySE1Dow03fXJDucO5wHnJSfvdCZLTl2fbkh3QTemE9gWRYeaHcUCrU4n1MfZHDnQRiLixXauJXVHwhT4mr8/EQuj2zGgjrKoOIHtRKvHbfz0I02IT1F6FnTsNL4olncfXLnjdG9++pzm7o2lyx7oVmJfitvYs6BbSaEz2h7tr1h7e8y8O1eULHn4rS3rv/O1pkBC/tLNVXGt6wLd7HVusgRHeiUqE9uVpU0z1sygrfnr8mkJ1AJV7avIJ/Fb+X08r9HatJu1e7R8fFDJJQfMV8zUXDjkrHFoQPMlAbCaicFfr5NIRj0XVSmUAVUAoi8zMD2a8vLbMpiWUJPkyrclBAp8UBD1+RPcPq4Qppz/qBBwheo8wwPbj+dTwKnH+X8WRYGO/+f1qZ9/mzr1z69tBQsk0qLWVXqPP1eEJz6PzwqVe0R9hlyYDH3q3Hc/uWXg6Cp17iPWJxHnPhVd7NsUX6t7nZu2pq1Lo03cGo7qakUxpUZx2GHEDvasoQyUAhOY5KzRrAtZXBYzMPFoYHCuSX1GhuCst0ls1q+fuJpK/OBhHLjxNJvcNOfUFN0SsENWZM61YHHXbqzfdH/S46bZXYdvv/r5LbvCnXvP9vpfNo7cm7e6sZSH/9d0oLtkRSgvb1mtH9Ig+ZFf7CprPvLOtsThZx6zz9+xCiXbjpNeLvyEpKBlNXD6eH1AP0/Px+qZmq/TYaRilCBOSpIgKGBYYaYOu9++0N5u32E/YD9q1xrtlZg8aT9vv2S/YteWtWOKRuo4u7KkM2RXsrwhp122d9i5kyoQp9jBiFhofH0MIVx9ksYIVraGMZFguyP9Hk8/rjts0WFvN7AHigV6a2ydcRcGIrsjtgRrhBF2CFih58wjj9jKuhqc1cmmPHN2wG74BXf2Wi139p5tZWvqPBrNPk6w5ZRnrbyHyfjEUu4KX0dmkmryayW0bebwTMrkmK6pgKaYNTG0tXRdKc3kijiaaYYcF4gJSQlbE/Yl8Bq7zb7ZvsfOi/6gkj9DjoMdcZfiaFzNkCYosLluwNhdEMprjMmgT3bWKDX07RogNc6akZpwDV9/qQZGa2BhDQzVHKuhxhp/Db1Qc5WlQJdjnFFc75CMc+qtNrG+UAOZqEY4L5X5bHspoi+wImIw1WV5+g5Tf1R5pvnCwBaZ4jQIqIuM+7rlUHkXYAf2AVx40KbefChP09tGupS4F8zbO2d3BjOppaypL9T9dfRxVx7pHTjhQ8Xi6TNMxy55Z9Z3F1WvnuNwKKuqiroX5U8szaxZVZ5c1zCjbsuS53PqSt3Vw2/de/eFBxf0rEyqKM7m0N7WZl37t//+A/d6/ze7ZLn7m32bjq7K9XU+zvzUB1Ag/oLWx0XWv0R0bMtAawgZgkp9DByNmYyhMe4h4h51X3CPuflRNxjdMOQGt7qKxyeEUhODo0lAkqQkOWks6WqSoEtKJkkGKzHXC5JqcgLqkVm/ulXfvzGqcbjiQPR1IBSwKWawrXqTurc5Oy3Y2FmxdvcC+wsmuTmodNdmnzmDLgtwO4sX5CfMWnNf47ifPl+9ttrta9xSN3638ObEXa65s7LYOzT7cFiz1bfLtGRAieW0QcI2ZGWe0/GM7lxrYojndWJks+iSCGFxVKRHRegTh0TqUPeXrqoVIgM3zcgILVT3pwQjbyWLgTBLgmaUBT1TzpspsrkdYO9AxRcGrByOZN+ZM2cE57PPfjbGl37+OuP1MBLCIWUG8rnSeCeFbTrYrIUlYpe4Rzwk8uqpHnubYSvhmAd7UM+hJwx6vSFXB5xOlAbZokcMUqNh0HDQwLHbGcNFw3uGjwzs4JkaGL096CkYtFwwMuarOFLeEVsZS9mtPXYyljfGRpI7YoWSWGXxklBH7FDssdjR2AuxwqVYILGRPE9ipVg5VolWjsVejRW1FLR6XmcUCHKCWZDKyoQS1BBkBNvaj3gVfrZ53T8rgEvrrED7RlNkVzuyj40Lb5sLtOpMM/+Vkyce2nXmDLz7i4la+Bn8Zf3EDuHNaytp7IR//OEIx6BCnctepYALjhK4wN4IlIhMrhJeR8aEqwK9JEBYGBXoUQH6hCGBGgWHQK8KgOWqhUhhMyjAZKR6VLggjAkIAoiIjSFiAKfmUn3H63LkeHf4jPDmZ+wnXGQv0xJckTKYlsxAQ820JAO1hMBRMolClzVE1PVnLIsfzQJjFgxlQdaUliTFBEcZYxk/GR8FXWySBg1PvDGWUZAf2W38Ki1xTVcOtlMdSOOsgSnV4UoSCpoqVe1gtoGdulEuuWLRumDr3Y1Z9PmF6+ak5DV9rWH8fm7xjLp5slbwlpRZ/LcU2r3LH+wc90e9D96OI4sjM8igUrbVuc9JB1N3pVL2zhzdat5npodinoyhfIwlhhrEFJEahBSBqjv/WvRIR4xgTB+S0yFddflx4b2UDkk1Dh3oLPV6KS3iekTen2zz9H/5FUqMaFUzKbm+6hUx+2d/6T+9fQ784a6zm2a9mlV3e1V176053gU9FdV9t+bStIn3Jv5Udd8vDlA5eN879911fFVWzurj2+/69qrsrFVPqh73xFIc3wKSSUrJQUXqmbV1Fu3J3ZpL96QfSqfpbNGNR42pdbQ4aK22RUv3cIeQi9N9bpzJ8qGZqcYgkSRJlq5KvE4Kl0NlOfSVj5RTRzlMlsNo+Vg5TfXWz5BsRmOKrqhe+Cd8bs/UeQTzunFmvzzJX2ZIdtvhvsHnfQJzttVpfx7QA+eTlEVrKvsOt2W/mli2an75uoU+5nrXrS5LpDO2XzjU1NxJnXKZfaJF0GSFynJFLj1QmlxQ67fWP/jWzs5Hb581o+PEvcwFK91wNCrxdC/KhYNUKNmSVbZSq9UV42AqGNE/9XVfHUnKjrehgJslrTFiEQJveaKyzF50Yop0w5CuOxFW0wMRocXwgY/3lNaX2LINZjmtYmlxMlcxo2ZuaUJCWUWJpWJZmV3LfVsQZq3e1zD+JqOtBD3mF9GbKCavKk3sjQPaa91hPWDlbrNBRhHkpoC1AARqpdSQlpJG02vdbhJCTZTj6Uj8sfhwPBdfMmSo1StJaSG93hti3hV1ojPZUTJaQodKoERdJ7JyQ5UlIJVAvFfIqXeSdBhJv4oyk+6U4uqFDkOfgQ4ZwGBgMx19AybyMJeUAJvcfvYS9XV/4fprk2Tqlcmpd/mmfIbIWRRXWFDE+KSeRGmiB1Hci2V9T/SseHjjAvPRhJGh0pXBLN+iTcE5Q93KL37y3V+kfkuUq5p82wY9C26f42ltqpvlAs8tdzR47ErPLY6lDVLWHHlmZa4j3pRb3bXg4JG79ltyS9zG+XXekiy7ZEhy++c2M846Jq/SXMGLEThGItlx0BO3NW5fHJcdCz2x7MVpbj8PvFOMDd3O38k/yj/L85iLCfXadtioLSbWxklBUXcgcsTgFBSB1wpDiWDU1MewF6pFY3zUHLzVpr4Sx+QkELicEHlDyNOGYX5bf1u/GokUMsezOGANWN3RMJ7m5jTN+o+v7Src8qMfBSqTZ9p1htiP6Tv3/PWv94w33Vqp07AR7EbZ+DNfipZ6q1J9iINkV66r1MUlxQUVv+GAgZ43wAHDUcOkgTNkDUHwUvqVdErSpXQZp5bXpYcjUUQ462oWncyCvqgVj2VWHE22Ld4aQ4xTJptRqy4Y19/fDKjRFNxwVBKNoKbcQROU/iOrzZeOL5qy23Tg2nM32+2RNfTnbJxaXJE+xXFKdJlSrEaKhwGKSJDQPeQQoaXG+Ub6DSP0GLca9xm5Qq6ao//CAdfN3cHdy3Fx7O0jnh2+VmACPR6RGiXJI22XKC9ZIjf26sYu6aD0mnRR0r0rwRd5IUUCXgKdxFGGYtJAl1GaSw3mFLN6qzMvM+83HzH/1PyuWTdphtfMF830mBl2mQ+aaYcZqsyNZuo0A2+2mOkbY18AsAJWyQA1UwlWqUlhlfAuA4UjDBMsY3ggUv7wl3qNPDiEu7m/sS/TM9Ut3z2dAAal+3s9Rsoj3SqrIx1riqeToKk0wz/o8waabq6k9Wbwm4Ht8VCtkRpFFq4FKgMQPWO8cY/xhj3HFdN38L/YeZwGGj2VRLmdFfCviB5Rzgp88WoS26VU9bCt36Vuo6tvIMUH6JpfTtwx+hdtvMWk0cRbrLpPzqO0KrbKqkqrtXJupY3+kO07jrNfVqNk5nKnlPQ/JEBpzvwcui1nOOcbOVyhVC3RTRJ7NZ8rsgfttAitLpMhG4phSWptKi1JhVQmnKoos5+AKKIBc7HBWKq+PhfAnCruILFUXG0cZQd8plBcrM2eqgXiznZDsxtsWrebHcLl5Eq5TINr/fmh2lwoyIXMXPg0F17PfT+XHs+FQ7mwNReKcoO5XblcUi58lAsvsqpduQdzaVfu5lxaojaxqO8i6HIloyryorHFyHSL1xtf977v/cTLHffCIS9s9UKXFxq9UOQNemmSFz7ywvteeM0LL3rhsBf2eGFQBSnxgsWb7qUaL/zkU9b0RS9DxPdEm4reJC/Fli95YYm3y7vHy2ELD2sE2OQ9L/z7FNZveeGginijFzoZNBR4q7x0xhTs4U+88EPvO156xgtPemGXFzYzCju9dC4DBZs300t5L/ze+1cvveiF172AY3lIhezybvbSqdGkM1jg2ZiUX0ZHdVoFZvQd8nJV3kYvLZrqt+cThhMuTg2OG/TuYtVBHA6XzkBsXvoRG8L7XnrQe9xLcQw96gCqWG2Rl14f5pOIge5ThwgdjIZ07Iqbddz7mvei9yMvP6Sytc4LcpStn6vNjqms2R7hSKeXS/HCVZV5P2Ws2uU96D3j5Su9QIlX8lKdlnl92XGm0FwtFGhhhha0qTmc0ejOjjGF8lCm1KcNwObm4qzq64KeNg97sNdPpk78bz76ap+ukV/5ruDfORK46VRuOsCXXjSMnBhMKfSN4B5U7sjRWv9GUyAQ+WNnCRj1qJ829sc+/X//DA3UM7RpGf7Qz5/TmXR6UdTr4nWnL0z8/PRZbZxWq9OJOknz2g9e1UqY1um0Ru35MH05pT7T68/zZi5yjM9Hq+FKmOfMyMpMdyhW+sfxpOS59hluzM1LppfY6iZMLOWuoQ1x0LeVsq9z8HUKhyU4ROB+6TGJ3k8eI3Sbfdj+DTvXY4fH0iBNipVCD8XDnnjYGA9L4rvi6UNm4MxMV9OxSiKJOvyY0hzSYQfscUCLA4IOSHKAxgE6h9mkApo0LtC4Ml1FrqCry7XZtcf1pOtF1+uu912fuGLeYHfqYrZk8tcfhF5zAauku25sovm77TUuG1ZFfpbGKiLFhoc/csGYC37oesdFz7jgmAvudj3kooMu6HDBXNciFy1wgdMF1GV20fdcH7moCnrcdcZFVchO16CLqoDprgIX/cdwSxhOUAFtDCd0q6D/zggAFfYQIwC+GngKVnkSoZHUMBv+QRftcPW5aJWr0UWdLtlFeZfFRcdcV130NddFF/3HcMU4+CgYRIEgCgJRRF+qp8TFENS7+HrXkGvENeri/S4gLslFtTjTxJlmMsbUCynqiy2quKsLZ/Q4+0vq9cVZ99854/7q4zo161G3Tb/QO7aOoqol+qMq1hY9Wbn+Mx4XcwMzswqZx19UCdOX1+WZC1dtuXVGKQYqpoV7A6aJxaPv6R2ORMol2NP0F7+/6rHeMl6L3tvmnR6+cPzplNbWkGiYU78oja5jUZm2YuJWMk83OtEx8ZGu+0v/CaWNfsh+yxr9WWvJV/8Pj793Ifw5+jRZhs9XWFt+gNSzcnx2R5/+6HNt9Nny/4dTWEL46+k3yFOYf4r/b/KUpiSCc/qF+J7SPI0wb6j1KxnstHZt2M6H6cdZmfZ+9raRij/vq/rlByZ/F8W5FL+7kAj2XMLyODZ7NP0AvMH2EgkjchjTw9E2e6P1u1S+PI15QkqwnQPLdvPsHwi9wXwgtZ2gzkIbLCKN5D7MUQyd/aQVfffHhVHCqb8y1mKeI8AzmkfJI9E0kDSyLZqmJI7cHk1zpIDMiqb5aTACScVPJK3BFCG3kB6yiqwhG8lKMojpXrKBLMb0BoL0q+UD0VInmUl8GNIX472AzMHy1Vi/gXSqUE7kovMrsTmv41uENd1kE1K5EiH+Mew8zPeRrVjbg23WIoSTZGOPOfjMRyoYHU4VYyc+QyoOL6ZqsfVqpNCJFN6OH+c0DANqbg0+Wa+b1bY+lepaMpdUI7Y5OAO1ZCG5FUt7VPiV+B1UaWTjXK9SfhuW9ZKuf9j/9f+bNPkq+x9NX75Oicr3QcuiafV+FHjlARgdh5PjQMZBv/BzcH4OH9dnO/4azHb8bzDXcTXocbRf2XGFGq8svNJ+5cCVk1cEwx/eS3P89++DDuPvQfl90Ob43VjQ8fbYpbErY5wyFigKjgUTHf81+1LTb2dzTZeAa/oNN+kw/tLxS6relJ8kpgTf/iG8Olru+EF9puN73892TL4E9ef6zg2d49RF7Jw5P+g4W3l24dneszvOHj178qy27/Sx0+HTnPE0jLwA4RfA+ALojN+t/O6V73JD4ZEwDYdHwxfCnP9k5Ul67Lnwc3T0uQvPUf+zlc/So8/A6NMXnqYLTxw4Qf0nek+cPzF5gn/0SLqj/gj0HoLzh+BQ0O74l4MJjh0HDxycPMjJDyoP0qEHoe/A0AE6cgBGD1w4QBfe135f733cnuCk4+hu2HXPTMfgQKVjAEfQu6HcsSFY6EiGxKakQGKTNsA1aXDMHVjXjt/lwZmOZa0hRys+4/PNTQLyhM/nmno5MHKVHL3SMNlAlYbCWUGlISM7+LbSWA+1QacjhDhr8HsyCJeCV4J0KAi2fGuTCYxNUr6xiQJpAgIOh7HS2G7cYeSNRr9xobHXeMB4yThp1FZi2RUj10tgyAYCnIORU42LPZ66c9rJRXVhbf2yMOwNZyxmd6WhNazZGyZNrcuaTwE80LL7/vvJXHtdOH9xc7jD3lIX7sSEwhJDmJDsp2xkbsvgwOAm9cciEEmQQY9nYIClgOUiPyQBNQWeAaxGsIHBAcwMbiIDnoFBGBhABRnE8gFYgemBAVY8ANgCvwOeCHrEgIhXIAK8DUZQDwwg/AC2H0hcgXL9/wEFiAdVCmVuZHN0cmVhbQplbmRvYmoKMTEgMCBvYmoKPDwvVHlwZSAvRm9udERlc2NyaXB0b3IKL0ZvbnROYW1lIC9MaWJlcmF0aW9uU2FucwovRmxhZ3MgNAovQXNjZW50IDkwNS4yNzM0NAovRGVzY2VudCAtMjExLjkxNDA2Ci9TdGVtViA0NS44OTg0MzgKL0NhcEhlaWdodCA2ODcuOTg4MjgKL0l0YWxpY0FuZ2xlIDAKL0ZvbnRCQm94IFstMjAzLjEyNSAtMzAzLjIyMjY2IDEwNTAuMjkyOTcgOTEwLjE1NjI1XQovRm9udEZpbGUyIDEwIDAgUj4+CmVuZG9iagoxMiAwIG9iago8PC9UeXBlIC9Gb250Ci9Gb250RGVzY3JpcHRvciAxMSAwIFIKL0Jhc2VGb250IC9MaWJlcmF0aW9uU2FucwovU3VidHlwZSAvQ0lERm9udFR5cGUyCi9DSURUb0dJRE1hcCAvSWRlbnRpdHkKL0NJRFN5c3RlbUluZm8gPDwvUmVnaXN0cnkgKEFkb2JlKQovT3JkZXJpbmcgKElkZW50aXR5KQovU3VwcGxlbWVudCAwPj4KL1cgWzAgWzM2NS4yMzQzOF0gMyAxNSAyNzcuODMyMDMgMTYgWzMzMy4wMDc4MSAyNzcuODMyMDMgMjc3LjgzMjAzXSAxOSAyNyA1NTYuMTUyMzQgMzYgWzY2Ni45OTIxOSAwIDAgNzIyLjE2Nzk3IDY2Ni45OTIxOSA2MTAuODM5ODQgNzc3LjgzMjAzXSA0NyBbNTU2LjE1MjM0IDgzMy4wMDc4MSA3MjIuMTY3OTcgNzc3LjgzMjAzIDY2Ni45OTIxOSAwIDcyMi4xNjc5NyA2NjYuOTkyMTkgNjEwLjgzOTg0IDcyMi4xNjc5NyAwIDk0My44NDc2NiAwIDY2Ni45OTIxOV0gNjYgNjkgNTU2LjE1MjM0IDcwIFs1MDAgNTU2LjE1MjM0IDU1Ni4xNTIzNCAyNzcuODMyMDMgNTU2LjE1MjM0IDU1Ni4xNTIzNCAyMjIuMTY3OTcgMCA1MDAgMjIyLjE2Nzk3XSA4MSA4MyA1NTYuMTUyMzQgODUgWzMzMy4wMDc4MSA1MDAgMjc3LjgzMjAzIDU1Ni4xNTIzNCA1MDAgNzIyLjE2Nzk3IDAgNTAwXV0KL0RXIDA+PgplbmRvYmoKMTMgMCBvYmoKPDwvRmlsdGVyIC9GbGF0ZURlY29kZQovTGVuZ3RoIDM0Mj4+IHN0cmVhbQp4nF2Sz26DMAzG7zxFjtuhgoQArYSQWtZKHPZHY3sAmpgOaYQo0ANvv2DTVlokQL/4s/0FJyyrl8p0Ews/3KBqmFjbGe1gHK5OATvDpTMBF0x3aloJ36pvbBD65HoeJ+gr0w5BnjMWfvroOLmZPe31cIbnIHx3GlxnLuzpu6w911drf6EHM7EoKAqmofWVXhv71vTAQkzbVNrHu2ne+JyH4mu2wAQyJzdq0DDaRoFrzAWCPPKrYPnJryIAo//FeURp51b9NA7lsZdHkYiKhXiCFAuijEgS7YhSogPRFklIJMmR4j1SkhGVRDskKYhOSAnFsh2aXV1tbx4fZzqhjK9eS+pJ7gQ1k2RSkDImpSRlnNy8Yms6nJQ3yfJJybk80iZVSQ9kklMe1UyPtEk1E+qeifUAZHn58csFuU9VXZ3zA8VbhJNcZtgZuF80O9gla3n+AEYHslEKZW5kc3RyZWFtCmVuZG9iago2IDAgb2JqCjw8L1R5cGUgL0ZvbnQKL1N1YnR5cGUgL1R5cGUwCi9CYXNlRm9udCAvTGliZXJhdGlvblNhbnMKL0VuY29kaW5nIC9JZGVudGl0eS1ICi9EZXNjZW5kYW50Rm9udHMgWzEyIDAgUl0KL1RvVW5pY29kZSAxMyAwIFI+PgplbmRvYmoKeHJlZgowIDE0CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNSAwMDAwMCBuIAowMDAwMDAxODY3IDAwMDAwIG4gCjAwMDAwMDAxNTQgMDAwMDAgbiAKMDAwMDAwMDE5MSAwMDAwMCBuIAowMDAwMDAwMjY3IDAwMDAwIG4gCjAwMDAwMTc0NjIgMDAwMDAgbiAKMDAwMDAwMDM0MyAwMDAwMCBuIAowMDAwMDAyMDk4IDAwMDAwIG4gCjAwMDAwMDIxNTMgMDAwMDAgbiAKMDAwMDAwMjIwMCAwMDAwMCBuIAowMDAwMDE2MTY0IDAwMDAwIG4gCjAwMDAwMTY0MDMgMDAwMDAgbiAKMDAwMDAxNzA0OSAwMDAwMCBuIAp0cmFpbGVyCjw8L1NpemUgMTQKL1Jvb3QgOSAwIFIKL0luZm8gMSAwIFI+PgpzdGFydHhyZWYKMTc2MDEKJSVFT0Y=",
      "text/html": [
       "<div>                            <div id=\"7a64c42a-32ce-4241-b8dc-5906a8db6a49\" class=\"plotly-graph-div\" style=\"height:400px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"7a64c42a-32ce-4241-b8dc-5906a8db6a49\")) {                    Plotly.newPlot(                        \"7a64c42a-32ce-4241-b8dc-5906a8db6a49\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=For-Profit 4-Year<br>region=%{x}<br>avg of pct_poorly_served=%{y}<extra></extra>\",\"legendgroup\":\"For-Profit 4-Year\",\"marker\":{\"color\":\"#636efa\",\"pattern\":{\"shape\":\"\"}},\"name\":\"For-Profit 4-Year\",\"offsetgroup\":\"For-Profit 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.42428739797481735,0.5423663243202307,0.5648977995626075,0.5530495794611806,0.5830443900290938,0.4175989545084286,0.5970618521294487,0.5554939375188037,0.5377548787306065],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=Private 4-Year<br>region=%{x}<br>avg of pct_poorly_served=%{y}<extra></extra>\",\"legendgroup\":\"Private 4-Year\",\"marker\":{\"color\":\"#EF553B\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Private 4-Year\",\"offsetgroup\":\"Private 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.36844944760782083,0.39572633337897567,0.4014703549322173,0.3122007613938768,0.4072411306441336,0.5005384211916057,0.48979963825752904,0.5177983060951145,0.5539025995396757],\"yaxis\":\"y\",\"type\":\"histogram\"},{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"avg\",\"hovertemplate\":\"sectortype=Public 4-Year<br>region=%{x}<br>avg of pct_poorly_served=%{y}<extra></extra>\",\"legendgroup\":\"Public 4-Year\",\"marker\":{\"color\":\"#00cc96\",\"pattern\":{\"shape\":\"\"}},\"name\":\"Public 4-Year\",\"offsetgroup\":\"Public 4-Year\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[\"Far West\",\"Great Lakes\",\"Mid Atlantic\",\"N/A or Other\",\"New England\",\"Plains\",\"Rocky Mountains\",\"Southeast\",\"Southwest\",\"US Territories\"],\"xaxis\":\"x\",\"y\":[0.47198734340229304,0.5639126057775571,0.39972084113592876,0.14476769745574464,0.3969824554327629,0.4859104731058936,0.5583020891527495,0.4954679329187486,0.5181568830315988,0.5303864670948568],\"yaxis\":\"y\",\"type\":\"histogram\"}],                        {\"template\":{\"data\":{\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"choropleth\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"choropleth\"}],\"contour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"contour\"}],\"contourcarpet\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"contourcarpet\"}],\"heatmap\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmap\"}],\"heatmapgl\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"heatmapgl\"}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"histogram2d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2d\"}],\"histogram2dcontour\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"histogram2dcontour\"}],\"mesh3d\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"type\":\"mesh3d\"}],\"parcoords\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"parcoords\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}],\"scatter\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter\"}],\"scatter3d\":[{\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatter3d\"}],\"scattercarpet\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattercarpet\"}],\"scattergeo\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergeo\"}],\"scattergl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattergl\"}],\"scattermapbox\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scattermapbox\"}],\"scatterpolar\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolar\"}],\"scatterpolargl\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterpolargl\"}],\"scatterternary\":[{\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"type\":\"scatterternary\"}],\"surface\":[{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"type\":\"surface\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}]},\"layout\":{\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"autotypenumbers\":\"strict\",\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]],\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]},\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"geo\":{\"bgcolor\":\"white\",\"lakecolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"showlakes\":true,\"showland\":true,\"subunitcolor\":\"white\"},\"hoverlabel\":{\"align\":\"left\"},\"hovermode\":\"closest\",\"mapbox\":{\"style\":\"light\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"gridwidth\":2,\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\"}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"ternary\":{\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"bgcolor\":\"#E5ECF6\",\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"title\":{\"x\":0.05},\"xaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2},\"yaxis\":{\"automargin\":true,\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"zerolinewidth\":2}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"region\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"avg of pct_poorly_served\"}},\"legend\":{\"title\":{\"text\":\"sectortype\"},\"tracegroupgap\":0},\"title\":{\"text\":\"Percent of Students Not Earning a Degree in 8 Years, by Region\"},\"barmode\":\"group\",\"height\":400},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('7a64c42a-32ce-4241-b8dc-5906a8db6a49');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = finaldata_grouped\n",
    "fig = px.histogram(df, x=\"region\", y=\"pct_poorly_served\", histfunc='avg',\n",
    "             color='sectortype', barmode='group',\n",
    "             height=400,\n",
    "                  title=\"Percent of Students Not Earning a Degree in 8 Years, by Region\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f60f39c",
   "metadata": {},
   "source": [
    "These visualizations show generally little variation in terms of academic outcomes for the students served. There may be a slightly higher percentage of students who are \"well-served\" by Mid-Atlantic private universities, but this may, of course, be conflated with the academic competitiveness of gaining admissions to certain schools and a student's past performance and aptitude. \n",
    "\n",
    "Altogether, this study on U.S. universities and regional performance reveals the real depth of educational data and complexity in comparing school-to-school. As we know, student experiences vary based on their PK-12 educational preparation, household support and income, community resources and so many other factors that are out of the hands of the learner. \n",
    "\n",
    "A more rigorous study could leverage IPEDS data on students' SAT scores or high school GPAs, family income, post-college job placement or more to gauge the quality or impact of the university on student outcomes. It would also be interesting to dive into the specifics of one region, for example looking within the Mid Atlantic to surface deeper variation. The four-year university dataset included 2,846 colleges which are difficult to compare.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4be73d",
   "metadata": {},
   "source": [
    "Personal note: I was glad to have the chance to engage with IPEDS data through this assignment as I will be graduating this month and working in postsecondary education consulting. This was my first foray into using this robust data set myself, though I've read countless studies that leverage the data. I know this is a very amateur first step to exploring here, but appreciated the chance to get more familiar with it and learn how to read the descriptions for variables a little more closely. "
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python [conda env:python-public-policy] *",
   "language": "python",
   "name": "conda-env-python-public-policy-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}