subplots_maps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "merged_df = pd.read_csv('merged_data_w_coord.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Country of Giftor</th>\n",
       "      <th>Institution Name</th>\n",
       "      <th>ID</th>\n",
       "      <th>OPEID</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Foreign Gift Received Date</th>\n",
       "      <th>Foreign Gift Amount</th>\n",
       "      <th>Gift Type</th>\n",
       "      <th>...</th>\n",
       "      <th>Functioning of government</th>\n",
       "      <th>Political participation</th>\n",
       "      <th>Political culture</th>\n",
       "      <th>Civil liberties</th>\n",
       "      <th>Regime type</th>\n",
       "      <th>Region</th>\n",
       "      <th>Country Latitude</th>\n",
       "      <th>Country Longitude</th>\n",
       "      <th>School Latitude</th>\n",
       "      <th>School Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Ball State University</td>\n",
       "      <td>830</td>\n",
       "      <td>178600</td>\n",
       "      <td>Muncie</td>\n",
       "      <td>IN</td>\n",
       "      <td>2013-08-28</td>\n",
       "      <td>3000000</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>0.64</td>\n",
       "      <td>3.89</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3.82</td>\n",
       "      <td>Authoritarian</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>33.768006</td>\n",
       "      <td>66.238514</td>\n",
       "      <td>38.958058</td>\n",
       "      <td>-95.247750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>654</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Ball State University</td>\n",
       "      <td>6491</td>\n",
       "      <td>178600</td>\n",
       "      <td>Muncie</td>\n",
       "      <td>IN</td>\n",
       "      <td>2014-08-18</td>\n",
       "      <td>264041</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>5.36</td>\n",
       "      <td>6.11</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.24</td>\n",
       "      <td>Flawed democracy</td>\n",
       "      <td>Latin America</td>\n",
       "      <td>-10.333333</td>\n",
       "      <td>-53.200000</td>\n",
       "      <td>38.958058</td>\n",
       "      <td>-95.247750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3097</td>\n",
       "      <td>China</td>\n",
       "      <td>Ball State University</td>\n",
       "      <td>201</td>\n",
       "      <td>178600</td>\n",
       "      <td>Muncie</td>\n",
       "      <td>IN</td>\n",
       "      <td>2014-08-18</td>\n",
       "      <td>7184164</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.18</td>\n",
       "      <td>Authoritarian</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>35.000074</td>\n",
       "      <td>104.999927</td>\n",
       "      <td>38.958058</td>\n",
       "      <td>-95.247750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3098</td>\n",
       "      <td>China</td>\n",
       "      <td>Ball State University</td>\n",
       "      <td>367</td>\n",
       "      <td>178600</td>\n",
       "      <td>Muncie</td>\n",
       "      <td>IN</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>4218639</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.18</td>\n",
       "      <td>Authoritarian</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>35.000074</td>\n",
       "      <td>104.999927</td>\n",
       "      <td>38.958058</td>\n",
       "      <td>-95.247750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3099</td>\n",
       "      <td>China</td>\n",
       "      <td>Ball State University</td>\n",
       "      <td>965</td>\n",
       "      <td>178600</td>\n",
       "      <td>Muncie</td>\n",
       "      <td>IN</td>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>1782825</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>4.29</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.18</td>\n",
       "      <td>Authoritarian</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>35.000074</td>\n",
       "      <td>104.999927</td>\n",
       "      <td>38.958058</td>\n",
       "      <td>-95.247750</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21047</th>\n",
       "      <td>18516</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Hult International Business School</td>\n",
       "      <td>64</td>\n",
       "      <td>4143200</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>MA</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>21532410</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>9.29</td>\n",
       "      <td>7.78</td>\n",
       "      <td>9.38</td>\n",
       "      <td>9.12</td>\n",
       "      <td>Full democracy</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>46.798562</td>\n",
       "      <td>8.231974</td>\n",
       "      <td>42.369985</td>\n",
       "      <td>-71.070640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21048</th>\n",
       "      <td>19790</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>Mount Holyoke College</td>\n",
       "      <td>9615</td>\n",
       "      <td>219200</td>\n",
       "      <td>South Hadley</td>\n",
       "      <td>MA</td>\n",
       "      <td>2013-08-27</td>\n",
       "      <td>300000</td>\n",
       "      <td>Monetary Gift</td>\n",
       "      <td>...</td>\n",
       "      <td>8.21</td>\n",
       "      <td>6.11</td>\n",
       "      <td>5.63</td>\n",
       "      <td>9.12</td>\n",
       "      <td>Flawed democracy</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>23.598298</td>\n",
       "      <td>120.835363</td>\n",
       "      <td>38.635284</td>\n",
       "      <td>-90.231677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21049</th>\n",
       "      <td>20120</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Saint Louis University</td>\n",
       "      <td>12123</td>\n",
       "      <td>250600</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>2013-05-09</td>\n",
       "      <td>250000</td>\n",
       "      <td>Monetary Gift</td>\n",
       "      <td>...</td>\n",
       "      <td>5.36</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.47</td>\n",
       "      <td>Flawed democracy</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>14.897192</td>\n",
       "      <td>100.832730</td>\n",
       "      <td>33.414809</td>\n",
       "      <td>-112.010831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21050</th>\n",
       "      <td>20121</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>Saint Louis University</td>\n",
       "      <td>12133</td>\n",
       "      <td>250600</td>\n",
       "      <td>Saint Louis</td>\n",
       "      <td>MO</td>\n",
       "      <td>2013-05-09</td>\n",
       "      <td>250000</td>\n",
       "      <td>Monetary Gift</td>\n",
       "      <td>...</td>\n",
       "      <td>5.36</td>\n",
       "      <td>6.11</td>\n",
       "      <td>6.25</td>\n",
       "      <td>6.47</td>\n",
       "      <td>Flawed democracy</td>\n",
       "      <td>Asia &amp; Australasia</td>\n",
       "      <td>14.897192</td>\n",
       "      <td>100.832730</td>\n",
       "      <td>33.414809</td>\n",
       "      <td>-112.010831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21051</th>\n",
       "      <td>20680</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Colorado School of Mines</td>\n",
       "      <td>5728</td>\n",
       "      <td>134800</td>\n",
       "      <td>Golden</td>\n",
       "      <td>CO</td>\n",
       "      <td>2012-06-06</td>\n",
       "      <td>3500000</td>\n",
       "      <td>Contract</td>\n",
       "      <td>...</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.22</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2.65</td>\n",
       "      <td>Authoritarian</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>24.000249</td>\n",
       "      <td>53.999483</td>\n",
       "      <td>34.364612</td>\n",
       "      <td>-89.539635</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21052 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     Country of Giftor                    Institution Name  \\\n",
       "0               0           Afghanistan               Ball State University   \n",
       "1             654                Brazil               Ball State University   \n",
       "2            3097                 China               Ball State University   \n",
       "3            3098                 China               Ball State University   \n",
       "4            3099                 China               Ball State University   \n",
       "...           ...                   ...                                 ...   \n",
       "21047       18516           Switzerland  Hult International Business School   \n",
       "21048       19790                Taiwan               Mount Holyoke College   \n",
       "21049       20120              Thailand              Saint Louis University   \n",
       "21050       20121              Thailand              Saint Louis University   \n",
       "21051       20680  United Arab Emirates            Colorado School of Mines   \n",
       "\n",
       "          ID    OPEID          City State Foreign Gift Received Date  \\\n",
       "0        830   178600        Muncie    IN                 2013-08-28   \n",
       "1       6491   178600        Muncie    IN                 2014-08-18   \n",
       "2        201   178600        Muncie    IN                 2014-08-18   \n",
       "3        367   178600        Muncie    IN                 2017-08-21   \n",
       "4        965   178600        Muncie    IN                 2018-01-08   \n",
       "...      ...      ...           ...   ...                        ...   \n",
       "21047     64  4143200     Cambridge    MA                 2012-10-01   \n",
       "21048   9615   219200  South Hadley    MA                 2013-08-27   \n",
       "21049  12123   250600   Saint Louis    MO                 2013-05-09   \n",
       "21050  12133   250600   Saint Louis    MO                 2013-05-09   \n",
       "21051   5728   134800        Golden    CO                 2012-06-06   \n",
       "\n",
       "       Foreign Gift Amount      Gift Type  ... Functioning of government  \\\n",
       "0                  3000000       Contract  ...                      0.64   \n",
       "1                   264041       Contract  ...                      5.36   \n",
       "2                  7184164       Contract  ...                      4.29   \n",
       "3                  4218639       Contract  ...                      4.29   \n",
       "4                  1782825       Contract  ...                      4.29   \n",
       "...                    ...            ...  ...                       ...   \n",
       "21047             21532410       Contract  ...                      9.29   \n",
       "21048               300000  Monetary Gift  ...                      8.21   \n",
       "21049               250000  Monetary Gift  ...                      5.36   \n",
       "21050               250000  Monetary Gift  ...                      5.36   \n",
       "21051              3500000       Contract  ...                      3.93   \n",
       "\n",
       "      Political participation  Political culture  Civil liberties  \\\n",
       "0                        3.89               2.50             3.82   \n",
       "1                        6.11               5.00             8.24   \n",
       "2                        3.33               2.50             1.18   \n",
       "3                        3.33               2.50             1.18   \n",
       "4                        3.33               2.50             1.18   \n",
       "...                       ...                ...              ...   \n",
       "21047                    7.78               9.38             9.12   \n",
       "21048                    6.11               5.63             9.12   \n",
       "21049                    6.11               6.25             6.47   \n",
       "21050                    6.11               6.25             6.47   \n",
       "21051                    2.22               5.00             2.65   \n",
       "\n",
       "            Regime type                      Region  Country Latitude  \\\n",
       "0         Authoritarian          Asia & Australasia         33.768006   \n",
       "1      Flawed democracy               Latin America        -10.333333   \n",
       "2         Authoritarian          Asia & Australasia         35.000074   \n",
       "3         Authoritarian          Asia & Australasia         35.000074   \n",
       "4         Authoritarian          Asia & Australasia         35.000074   \n",
       "...                 ...                         ...               ...   \n",
       "21047    Full democracy              Western Europe         46.798562   \n",
       "21048  Flawed democracy          Asia & Australasia         23.598298   \n",
       "21049  Flawed democracy          Asia & Australasia         14.897192   \n",
       "21050  Flawed democracy          Asia & Australasia         14.897192   \n",
       "21051     Authoritarian  Middle East & North Africa         24.000249   \n",
       "\n",
       "       Country Longitude  School Latitude School Longitude  \n",
       "0              66.238514        38.958058       -95.247750  \n",
       "1             -53.200000        38.958058       -95.247750  \n",
       "2             104.999927        38.958058       -95.247750  \n",
       "3             104.999927        38.958058       -95.247750  \n",
       "4             104.999927        38.958058       -95.247750  \n",
       "...                  ...              ...              ...  \n",
       "21047           8.231974        42.369985       -71.070640  \n",
       "21048         120.835363        38.635284       -90.231677  \n",
       "21049         100.832730        33.414809      -112.010831  \n",
       "21050         100.832730        33.414809      -112.010831  \n",
       "21051          53.999483        34.364612       -89.539635  \n",
       "\n",
       "[21052 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a8a13abd0664be0a82873a1b3f18c25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"650\"\n",
       "            src=\"school_map.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x171976f7a90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import mplleaflet\n",
    "import dill\n",
    "from IPython.display import IFrame    \n",
    "\n",
    "\n",
    "x = merged_df['School Longitude'].unique().tolist()\n",
    "y = merged_df['School Latitude'].unique().tolist()\n",
    "n = merged_df['Institution Name'].unique().tolist()\n",
    "\n",
    "top_10_schools_all = dill.load(open('./top_10_schools_all.pkd', 'rb'))\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.scatter(x,\n",
    "            y,\n",
    "            color='blue', \n",
    "            alpha=0.7, \n",
    "            s=100,\n",
    "           )\n",
    "mplleaflet.save_html(fileobj='school_map.html')\n",
    "IFrame('school_map.html', width=800, height=650)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9a99a80bbac44c8bfe341d4d2c84604",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"650\"\n",
       "            src=\"school_map.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x171996df970>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10_schools_auth = dill.load(open('./top_10_schools_auth.pkd', 'rb'))\n",
    "\n",
    "top_10_schools_auth.index\n",
    "\n",
    "school_coord_df = pd.DataFrame({'Lat': x, 'Lon': y}, index=n)\n",
    "top_10 = school_coord_df.loc[top_10_schools_auth.index]\n",
    "\n",
    "x1 = top_10['Lat'].tolist()\n",
    "y1 = top_10['Lon'].tolist()\n",
    "# n = merged_df['Institution Name'].unique().tolist()\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.scatter(x1,\n",
    "            y1,\n",
    "            color='blue', \n",
    "            alpha=0.7, \n",
    "            s=100,\n",
    "           )\n",
    "mplleaflet.save_html(fileobj='school_map.html')\n",
    "IFrame('school_map.html', width=800, height=650)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Institution Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Carnegie Mellon University</th>\n",
       "      <td>-79.942728</td>\n",
       "      <td>40.444162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgetown University</th>\n",
       "      <td>-77.074580</td>\n",
       "      <td>38.908939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northwestern University</th>\n",
       "      <td>-87.675811</td>\n",
       "      <td>42.055116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas A&amp;M University</th>\n",
       "      <td>-96.352128</td>\n",
       "      <td>30.610837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts Institute of Technology</th>\n",
       "      <td>-71.095678</td>\n",
       "      <td>42.358396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harvard University</th>\n",
       "      <td>-71.126782</td>\n",
       "      <td>42.367909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York University</th>\n",
       "      <td>-73.996254</td>\n",
       "      <td>40.729253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University of Southern California</th>\n",
       "      <td>-118.285867</td>\n",
       "      <td>34.021883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>George Washington University</th>\n",
       "      <td>-122.308009</td>\n",
       "      <td>47.654324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cornell University</th>\n",
       "      <td>-73.982917</td>\n",
       "      <td>40.773990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Lat        Lon\n",
       "Institution Name                                            \n",
       "Carnegie Mellon University             -79.942728  40.444162\n",
       "Georgetown University                  -77.074580  38.908939\n",
       "Northwestern University                -87.675811  42.055116\n",
       "Texas A&M University                   -96.352128  30.610837\n",
       "Massachusetts Institute of Technology  -71.095678  42.358396\n",
       "Harvard University                     -71.126782  42.367909\n",
       "New York University                    -73.996254  40.729253\n",
       "University of Southern California     -118.285867  34.021883\n",
       "George Washington University          -122.308009  47.654324\n",
       "Cornell University                     -73.982917  40.773990"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'School Longitude'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4409\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4410\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_at\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4411\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.get_value_at\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.get_value_at\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\util.pxd\u001b[0m in \u001b[0;36mpandas._libs.util.validate_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-2a9a8ba4abc4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m     .head(10))\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtop10schools_by_dollars\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'School Longitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtop10schools_by_dollars\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'School Latitude'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmerged_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Institution Name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    872\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4416\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4417\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4418\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4419\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4420\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\highered\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4402\u001b[0m         \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"getitem\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4403\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4404\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"tz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4405\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4406\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'School Longitude'"
     ]
    }
   ],
   "source": [
    "authoritarian_df = (\n",
    "    merged_df.\n",
    "    loc[merged_df['Regime type']=='Authoritarian'])\n",
    "top10schools_by_dollars = (\n",
    "    authoritarian_df.groupby('Institution Name')\n",
    "    .sum().loc[:, 'Foreign Gift Amount']\n",
    "    .sort_values(ascending=False)\n",
    "    .head(10))\n",
    "\n",
    "x = top10schools_by_dollars['School Longitude'].unique().tolist()\n",
    "y = top10schools_by_dollars['School Latitude'].unique().tolist()\n",
    "n = merged_df['Institution Name'].unique().tolist()\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.scatter(x,\n",
    "            y,\n",
    "            color='blue', \n",
    "            alpha=0.7, \n",
    "            s=100,\n",
    "           )\n",
    "mplleaflet.save_html(fileobj='school_map.html')\n",
    "IFrame('school_map.html', width=800, height=650)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Institution Name\n",
       "Carnegie Mellon University               389662174\n",
       "Georgetown University                    367377726\n",
       "Northwestern University                  361757194\n",
       "Texas A&M University                     284437639\n",
       "Massachusetts Institute of Technology    218533960\n",
       "Harvard University                       146026842\n",
       "New York University                      136785623\n",
       "University of Southern California        100818751\n",
       "George Washington University              99571723\n",
       "Cornell University                        80897485\n",
       "Name: Foreign Gift Amount, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10schools_by_dollars\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bdaad74b76b4dd1a6d02ce77e4e0ab1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"550\"\n",
       "            height=\"450\"\n",
       "            src=\"country_map.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x231e8870dc0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = merged_df['Country Longitude'].unique().tolist()\n",
    "y = merged_df['Country Latitude'].unique().tolist()\n",
    "n = merged_df['Country of Giftor'].unique().tolist()\n",
    "\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.scatter(x,\n",
    "            y,\n",
    "            color='blue', \n",
    "            alpha=0.7, \n",
    "            s=100,\n",
    "           )\n",
    "mplleaflet.save_html(fileobj='country_map.html')\n",
    "IFrame('country_map.html', width=550, height=450)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103    Afghanistan\n",
       "125        Albania\n",
       "82         Algeria\n",
       "74          Angola\n",
       "159     Antarctica\n",
       "          ...     \n",
       "2        W. Sahara\n",
       "157          Yemen\n",
       "70          Zambia\n",
       "48        Zimbabwe\n",
       "73        eSwatini\n",
       "Name: name, Length: 177, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas\n",
    "import geoplot\n",
    "import mapclassify\n",
    "\n",
    "\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "counts = merged_df.groupby(['Country to merge on']).count()['ID']\n",
    "\n",
    "world['name'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Country to merge on</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Malta</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>United States</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pop_est continent name iso_a3  gdp_md_est geometry  \\\n",
       "177      NaN       NaN  NaN    NaN         NaN     None   \n",
       "178      NaN       NaN  NaN    NaN         NaN     None   \n",
       "179      NaN       NaN  NaN    NaN         NaN     None   \n",
       "180      NaN       NaN  NaN    NaN         NaN     None   \n",
       "181      NaN       NaN  NaN    NaN         NaN     None   \n",
       "182      NaN       NaN  NaN    NaN         NaN     None   \n",
       "183      NaN       NaN  NaN    NaN         NaN     None   \n",
       "184      NaN       NaN  NaN    NaN         NaN     None   \n",
       "185      NaN       NaN  NaN    NaN         NaN     None   \n",
       "186      NaN       NaN  NaN    NaN         NaN     None   \n",
       "\n",
       "        Country to merge on     ID  \n",
       "177                 Bahrain   11.0  \n",
       "178  Bosnia and Herzegovina    4.0  \n",
       "179          Czech Republic   10.0  \n",
       "180      Dominican Republic    3.0  \n",
       "181               Hong Kong  806.0  \n",
       "182                   Malta    5.0  \n",
       "183               Mauritius    2.0  \n",
       "184               Not Rated  264.0  \n",
       "185               Singapore  300.0  \n",
       "186           United States   27.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_world = pd.merge(world, counts.reset_index(), left_on='name', right_on='Country to merge on', how='outer')\n",
    "merged_world[merged_world['name'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_dict = {'Bosnia and Herz.': 'Bosnia and Herzegovina',\n",
    "             'Czechia': 'Czech Republic',\n",
    "             'Dominican Rep.': 'Dominican Republic',\n",
    "             'Mauritania': 'Mauritius',\n",
    "             'United States of America': 'United States'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_world = world.replace(name_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
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       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603253.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "      <td>United States</td>\n",
       "      <td>27.0</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>177</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Malta</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pop_est      continent           name iso_a3  gdp_md_est  \\\n",
       "0       920938.0        Oceania           Fiji    FJI      8374.0   \n",
       "1     53950935.0         Africa       Tanzania    TZA    150600.0   \n",
       "2       603253.0         Africa      W. Sahara    ESH       906.5   \n",
       "3     35623680.0  North America         Canada    CAN   1674000.0   \n",
       "4    326625791.0  North America  United States    USA  18560000.0   \n",
       "..           ...            ...            ...    ...         ...   \n",
       "177          NaN            NaN            NaN    NaN         NaN   \n",
       "178          NaN            NaN            NaN    NaN         NaN   \n",
       "179          NaN            NaN            NaN    NaN         NaN   \n",
       "180          NaN            NaN            NaN    NaN         NaN   \n",
       "181          NaN            NaN            NaN    NaN         NaN   \n",
       "\n",
       "                                              geometry Country to merge on  \\\n",
       "0    MULTIPOLYGON (((180.00000 -16.06713, 180.00000...                 NaN   \n",
       "1    POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...            Tanzania   \n",
       "2    POLYGON ((-8.66559 27.65643, -8.66512 27.58948...                 NaN   \n",
       "3    MULTIPOLYGON (((-122.84000 49.00000, -122.9742...              Canada   \n",
       "4    MULTIPOLYGON (((-122.84000 49.00000, -120.0000...       United States   \n",
       "..                                                 ...                 ...   \n",
       "177                                               None             Bahrain   \n",
       "178                                               None           Hong Kong   \n",
       "179                                               None               Malta   \n",
       "180                                               None           Not Rated   \n",
       "181                                               None           Singapore   \n",
       "\n",
       "         ID  \n",
       "0       NaN  \n",
       "1      31.0  \n",
       "2       NaN  \n",
       "3    2076.0  \n",
       "4      27.0  \n",
       "..      ...  \n",
       "177    11.0  \n",
       "178   806.0  \n",
       "179     5.0  \n",
       "180   264.0  \n",
       "181   300.0  \n",
       "\n",
       "[182 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_merged_world = pd.merge(new_world, counts.reset_index(), left_on='name', right_on='Country to merge on', how='outer')\n",
    "new_merged_world"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Country to merge on</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938.0</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603253.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "      <td>United States</td>\n",
       "      <td>27.0</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>172</th>\n",
       "      <td>7111024.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>SRB</td>\n",
       "      <td>101800.0</td>\n",
       "      <td>POLYGON ((18.82982 45.90887, 18.82984 45.90888...</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>642550.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>MNE</td>\n",
       "      <td>10610.0</td>\n",
       "      <td>POLYGON ((20.07070 42.58863, 19.80161 42.50009...</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1895250.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>-99</td>\n",
       "      <td>18490.0</td>\n",
       "      <td>POLYGON ((20.59025 41.85541, 20.52295 42.21787...</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>1218208.0</td>\n",
       "      <td>North America</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>TTO</td>\n",
       "      <td>43570.0</td>\n",
       "      <td>POLYGON ((-61.68000 10.76000, -61.10500 10.890...</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13026129.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>S. Sudan</td>\n",
       "      <td>SSD</td>\n",
       "      <td>20880.0</td>\n",
       "      <td>POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...</td>\n",
       "      <td>S. Sudan</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         pop_est      continent                 name iso_a3  gdp_md_est  \\\n",
       "0       920938.0        Oceania                 Fiji    FJI      8374.0   \n",
       "1     53950935.0         Africa             Tanzania    TZA    150600.0   \n",
       "2       603253.0         Africa            W. Sahara    ESH       906.5   \n",
       "3     35623680.0  North America               Canada    CAN   1674000.0   \n",
       "4    326625791.0  North America        United States    USA  18560000.0   \n",
       "..           ...            ...                  ...    ...         ...   \n",
       "172    7111024.0         Europe               Serbia    SRB    101800.0   \n",
       "173     642550.0         Europe           Montenegro    MNE     10610.0   \n",
       "174    1895250.0         Europe               Kosovo    -99     18490.0   \n",
       "175    1218208.0  North America  Trinidad and Tobago    TTO     43570.0   \n",
       "176   13026129.0         Africa             S. Sudan    SSD     20880.0   \n",
       "\n",
       "                                              geometry  Country to merge on  \\\n",
       "0    MULTIPOLYGON (((180.00000 -16.06713, 180.00000...                 Fiji   \n",
       "1    POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...             Tanzania   \n",
       "2    POLYGON ((-8.66559 27.65643, -8.66512 27.58948...            W. Sahara   \n",
       "3    MULTIPOLYGON (((-122.84000 49.00000, -122.9742...               Canada   \n",
       "4    MULTIPOLYGON (((-122.84000 49.00000, -120.0000...        United States   \n",
       "..                                                 ...                  ...   \n",
       "172  POLYGON ((18.82982 45.90887, 18.82984 45.90888...               Serbia   \n",
       "173  POLYGON ((20.07070 42.58863, 19.80161 42.50009...           Montenegro   \n",
       "174  POLYGON ((20.59025 41.85541, 20.52295 42.21787...               Kosovo   \n",
       "175  POLYGON ((-61.68000 10.76000, -61.10500 10.890...  Trinidad and Tobago   \n",
       "176  POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...             S. Sudan   \n",
       "\n",
       "         ID  \n",
       "0       0.0  \n",
       "1      31.0  \n",
       "2       0.0  \n",
       "3    2076.0  \n",
       "4      27.0  \n",
       "..      ...  \n",
       "172     4.0  \n",
       "173     0.0  \n",
       "174     0.0  \n",
       "175     9.0  \n",
       "176     0.0  \n",
       "\n",
       "[177 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hacked_world = new_merged_world[~new_merged_world['pop_est'].isna()].copy()\n",
    "hacked_world['ID'] = hacked_world['ID'].fillna(0).copy()\n",
    "hacked_world.loc[hacked_world['Country to merge on'].isna(), 'Country to merge on'] = hacked_world.loc[hacked_world['Country to merge on'].isna(), 'name']\n",
    "hacked_world\n",
    "# hacked_world['ID'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05989f91881b486abc83419fd212c11f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231ecb3f610>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scheme = mapclassify.JenksCaspall(hacked_world['ID'], k=5)\n",
    "geoplot.choropleth(hacked_world,\n",
    "                   hue=hacked_world['ID'],\n",
    "                   scheme=scheme,\n",
    "                   cmap='Blues',\n",
    "                   figsize=(10, 8),\n",
    "                   legend=True\n",
    "                   )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "shapes_df = hacked_world[['Country to merge on', 'geometry', 'ID']].rename({'ID': 'Country donation counts'}, axis=1)\n",
    "shapes_df.to_csv('country_shapes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Country to merge on</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Country donation counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fiji</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "      <td>2076.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>POLYGON ((18.82982 45.90887, 18.82984 45.90888...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Montenegro</td>\n",
       "      <td>POLYGON ((20.07070 42.58863, 19.80161 42.50009...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Kosovo</td>\n",
       "      <td>POLYGON ((20.59025 41.85541, 20.52295 42.21787...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>POLYGON ((-61.68000 10.76000, -61.10500 10.890...</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>S. Sudan</td>\n",
       "      <td>POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country to merge on                                           geometry  \\\n",
       "0                   Fiji  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...   \n",
       "1               Tanzania  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...   \n",
       "2              W. Sahara  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...   \n",
       "3                 Canada  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...   \n",
       "4          United States  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...   \n",
       "..                   ...                                                ...   \n",
       "172               Serbia  POLYGON ((18.82982 45.90887, 18.82984 45.90888...   \n",
       "173           Montenegro  POLYGON ((20.07070 42.58863, 19.80161 42.50009...   \n",
       "174               Kosovo  POLYGON ((20.59025 41.85541, 20.52295 42.21787...   \n",
       "175  Trinidad and Tobago  POLYGON ((-61.68000 10.76000, -61.10500 10.890...   \n",
       "176             S. Sudan  POLYGON ((30.83385 3.50917, 29.95350 4.17370, ...   \n",
       "\n",
       "     Country donation counts  \n",
       "0                        0.0  \n",
       "1                       31.0  \n",
       "2                        0.0  \n",
       "3                     2076.0  \n",
       "4                       27.0  \n",
       "..                       ...  \n",
       "172                      4.0  \n",
       "173                      0.0  \n",
       "174                      0.0  \n",
       "175                      9.0  \n",
       "176                      0.0  \n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapes_df"
   ]
  }
 ],
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