data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DATA CLEANING AND PREPARATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = pd.read_csv('/resources/data/nei_bor.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "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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       "    }\n",
       "\n",
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       "        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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUBUASHIE</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DARKUMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KISSEMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAIFA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DZORWULU</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Borough\n",
       "0    BUBUASHIE   Accra\n",
       "1     DARKUMAN   Accra\n",
       "2     KISSEMAN   Accra\n",
       "3        TAIFA   Accra\n",
       "4     DZORWULU   Accra"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = nb[~nb[['Neighborhood']].apply(frozenset, axis=1).duplicated()] #remove duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec1 = pd.read_csv('/resources/data/sec1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>supid</th>\n",
       "      <th>clusterno</th>\n",
       "      <th>comname</th>\n",
       "      <th>cs1q1</th>\n",
       "      <th>cs1q2</th>\n",
       "      <th>cs1q3a</th>\n",
       "      <th>cs1q3ao</th>\n",
       "      <th>cs1q3b</th>\n",
       "      <th>cs1q3bo</th>\n",
       "      <th>cs1q3c</th>\n",
       "      <th>...</th>\n",
       "      <th>cs1q3do</th>\n",
       "      <th>cs1q4a</th>\n",
       "      <th>cs1q4ao</th>\n",
       "      <th>cs1q4b</th>\n",
       "      <th>cs1q4bo</th>\n",
       "      <th>cs1q4c</th>\n",
       "      <th>cs1q4co</th>\n",
       "      <th>cs1q4d</th>\n",
       "      <th>cs1q4do</th>\n",
       "      <th>cs1q5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>50 and above</td>\n",
       "      <td>Akan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ewe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mole-Dagbani</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Protestant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Islam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More departures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>2</td>\n",
       "      <td>ELUBO</td>\n",
       "      <td>15420.0</td>\n",
       "      <td>50 and above</td>\n",
       "      <td>Akan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>NIGER</td>\n",
       "      <td>Ewe</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Catholic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Protestant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Christians</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More arrivals</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           supid  clusterno     comname    cs1q1         cs1q2 cs1q3a cs1q3ao  \\\n",
       "0  John Botchway         42  JEWI WHARF   5000.0  50 and above   Akan     NaN   \n",
       "1  John Botchway          2       ELUBO  15420.0  50 and above   Akan     NaN   \n",
       "\n",
       "  cs1q3b cs1q3bo        cs1q3c  ... cs1q3do                   cs1q4a cs1q4ao  \\\n",
       "0    Ewe     NaN  Mole-Dagbani  ...     NaN  Charismatic/Pentecostal     NaN   \n",
       "1  Other   NIGER           Ewe  ...     NaN                 Catholic     NaN   \n",
       "\n",
       "                    cs1q4b cs1q4bo      cs1q4c  cs1q4co            cs1q4d  \\\n",
       "0                 Catholic     NaN  Protestant      NaN             Islam   \n",
       "1  Charismatic/Pentecostal     NaN  Protestant      NaN  Other Christians   \n",
       "\n",
       "  cs1q4do            cs1q5  \n",
       "0     NaN  More departures  \n",
       "1     NaN    More arrivals  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename Neighborhood_X\n",
    "sec1.rename(columns={'cs1q1':'population','cs1q4a':'popular_religion'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec11 = sec1[['population', 'popular_religion','comname']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data\n",
    "nb1 = nb.merge(sec11, left_on='Neighborhood', right_on='comname', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>population</th>\n",
       "      <th>popular_religion</th>\n",
       "      <th>comname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUBUASHIE</td>\n",
       "      <td>Accra</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>BUBUASHIE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DARKUMAN</td>\n",
       "      <td>Accra</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>DARKUMAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Borough  population         popular_religion    comname\n",
       "0    BUBUASHIE   Accra     70000.0  Charismatic/Pentecostal  BUBUASHIE\n",
       "1     DARKUMAN   Accra     31000.0  Charismatic/Pentecostal   DARKUMAN"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb1.popular_religion = nb1.popular_religion.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb11 = nb1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb11.popular_religion = nb11.popular_religion.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb11 =nb11.groupby('Neighborhood')['popular_religion'].agg([('popular_religion', ', '.join)]).reset_index() #GROUPING DATA BY Neighborhood\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nb11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>popular_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Charismatic/Pentecostal, Islam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABREPO KUMA</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADENTA WEST</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHINSAN</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALAJO</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AMAKOM</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ASHAIMAN</td>\n",
       "      <td>Charismatic/Pentecostal, Charismatic/Pentecost...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASHONGMAN</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AYIGYA</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BANTAMA</td>\n",
       "      <td>Catholic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BOHYEN</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BREMANG</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BUBUASHIE</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CHORKOR</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DARKUMAN</td>\n",
       "      <td>Charismatic/Pentecostal, Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>DZORWULU</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>JAMES TOWN</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>KANDA</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>KISSEMAN</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KOKOMLEMLE</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KORLE BU</td>\n",
       "      <td>Catholic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>KRONUM</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>LA</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LASHIBI</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MAMPROBI</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NEW SUAME</td>\n",
       "      <td>Catholic, Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NEW TAFO (KROFROM)</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NEW TAFO (MANHYIA EXTENSION)</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NIMA</td>\n",
       "      <td>Islam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NUNGUA</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>OFANKOR</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>OFORIKROM</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>OSU</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>PANKRONO</td>\n",
       "      <td>Catholic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RIDGE</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SAKUMONO</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>TAIFA</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>TANOSO</td>\n",
       "      <td>Protestant, Protestant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>TESHIE</td>\n",
       "      <td>Protestant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighborhood  \\\n",
       "0                         ABOABO   \n",
       "1                   ABOSSEY OKAI   \n",
       "2                    ABREPO KUMA   \n",
       "3                    ADENTA WEST   \n",
       "4                        AHINSAN   \n",
       "5                          ALAJO   \n",
       "6                         AMAKOM   \n",
       "7                       ASHAIMAN   \n",
       "8                      ASHONGMAN   \n",
       "9                         AYIGYA   \n",
       "10                       BANTAMA   \n",
       "11                        BOHYEN   \n",
       "12                       BREMANG   \n",
       "13                     BUBUASHIE   \n",
       "14                       CHORKOR   \n",
       "15                      DARKUMAN   \n",
       "16                      DZORWULU   \n",
       "17                    JAMES TOWN   \n",
       "18                         KANDA   \n",
       "19                      KISSEMAN   \n",
       "20                    KOKOMLEMLE   \n",
       "21                      KORLE BU   \n",
       "22                        KRONUM   \n",
       "23                            LA   \n",
       "24                       LASHIBI   \n",
       "25                      MAMPROBI   \n",
       "26                     NEW SUAME   \n",
       "27            NEW TAFO (KROFROM)   \n",
       "28  NEW TAFO (MANHYIA EXTENSION)   \n",
       "29                          NIMA   \n",
       "30                        NUNGUA   \n",
       "31                       OFANKOR   \n",
       "32                     OFORIKROM   \n",
       "33                           OSU   \n",
       "34                      PANKRONO   \n",
       "35                         RIDGE   \n",
       "36                      SAKUMONO   \n",
       "37                         TAIFA   \n",
       "38                        TANOSO   \n",
       "39                        TESHIE   \n",
       "\n",
       "                                     popular_religion  \n",
       "0                      Charismatic/Pentecostal, Islam  \n",
       "1                             Charismatic/Pentecostal  \n",
       "2                             Charismatic/Pentecostal  \n",
       "3                             Charismatic/Pentecostal  \n",
       "4                                          Protestant  \n",
       "5                                          Protestant  \n",
       "6                                          Protestant  \n",
       "7   Charismatic/Pentecostal, Charismatic/Pentecost...  \n",
       "8                                          Protestant  \n",
       "9                                          Protestant  \n",
       "10                                           Catholic  \n",
       "11                                         Protestant  \n",
       "12                            Charismatic/Pentecostal  \n",
       "13                            Charismatic/Pentecostal  \n",
       "14                            Charismatic/Pentecostal  \n",
       "15   Charismatic/Pentecostal, Charismatic/Pentecostal  \n",
       "16                            Charismatic/Pentecostal  \n",
       "17                            Charismatic/Pentecostal  \n",
       "18                            Charismatic/Pentecostal  \n",
       "19                            Charismatic/Pentecostal  \n",
       "20                            Charismatic/Pentecostal  \n",
       "21                                           Catholic  \n",
       "22                            Charismatic/Pentecostal  \n",
       "23                            Charismatic/Pentecostal  \n",
       "24                            Charismatic/Pentecostal  \n",
       "25                            Charismatic/Pentecostal  \n",
       "26                               Catholic, Protestant  \n",
       "27                                         Protestant  \n",
       "28                                         Protestant  \n",
       "29                                              Islam  \n",
       "30                                         Protestant  \n",
       "31                            Charismatic/Pentecostal  \n",
       "32                                         Protestant  \n",
       "33                                         Protestant  \n",
       "34                                           Catholic  \n",
       "35                                         Protestant  \n",
       "36                            Charismatic/Pentecostal  \n",
       "37                            Charismatic/Pentecostal  \n",
       "38                             Protestant, Protestant  \n",
       "39                                         Protestant  "
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BUBUASHIE</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DARKUMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KISSEMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TAIFA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DZORWULU</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOKOMLEMLE</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ADENTA WEST</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OFANKOR</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASHONGMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHORKOR</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>MAMPROBI</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>KORLE BU</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>JAMES TOWN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ALAJO</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RIDGE</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KANDA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NIMA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TESHIE</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>LA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OSU</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SAKUMONO</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>LASHIBI</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NUNGUA</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ASHAIMAN</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TANOSO</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BANTAMA</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ABREPO KUMA</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AMAKOM</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BREMANG</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>BOHYEN</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NEW TAFO (KROFROM)</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>KRONUM</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>PANKRONO</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NEW SUAME</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>NEW TAFO (MANHYIA EXTENSION)</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OFORIKROM</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>AHINSAN</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>AYIGYA</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Neighborhood Borough\n",
       "0                      BUBUASHIE   Accra\n",
       "1                       DARKUMAN   Accra\n",
       "2                       KISSEMAN   Accra\n",
       "3                          TAIFA   Accra\n",
       "4                       DZORWULU   Accra\n",
       "5                     KOKOMLEMLE   Accra\n",
       "6                    ADENTA WEST   Accra\n",
       "7                        OFANKOR   Accra\n",
       "8                      ASHONGMAN   Accra\n",
       "9                        CHORKOR   Accra\n",
       "10                      MAMPROBI   Accra\n",
       "11                      KORLE BU   Accra\n",
       "12                  ABOSSEY OKAI   Accra\n",
       "13                    JAMES TOWN   Accra\n",
       "14                         ALAJO   Accra\n",
       "15                         RIDGE   Accra\n",
       "16                         KANDA   Accra\n",
       "17                          NIMA   Accra\n",
       "18                        TESHIE   Accra\n",
       "19                            LA   Accra\n",
       "20                           OSU   Accra\n",
       "21                      SAKUMONO   Accra\n",
       "22                       LASHIBI   Accra\n",
       "23                        NUNGUA   Accra\n",
       "24                      ASHAIMAN   Accra\n",
       "25                        TANOSO  Kumasi\n",
       "26                       BANTAMA  Kumasi\n",
       "27                   ABREPO KUMA  Kumasi\n",
       "28                        AMAKOM  Kumasi\n",
       "29                       BREMANG  Kumasi\n",
       "30                        BOHYEN  Kumasi\n",
       "31            NEW TAFO (KROFROM)  Kumasi\n",
       "32                        KRONUM  Kumasi\n",
       "33                      PANKRONO  Kumasi\n",
       "34                     NEW SUAME  Kumasi\n",
       "36  NEW TAFO (MANHYIA EXTENSION)  Kumasi\n",
       "37                     OFORIKROM  Kumasi\n",
       "38                       AHINSAN  Kumasi\n",
       "39                        AYIGYA  Kumasi\n",
       "40                        ABOABO  Kumasi"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data\n",
    "nb2 = nb11.merge(nb1, left_on='Neighborhood', right_on='Neighborhood', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Neighborhood</th>\n",
       "      <th>popular_religion_x</th>\n",
       "      <th>Borough</th>\n",
       "      <th>population</th>\n",
       "      <th>popular_religion_y</th>\n",
       "      <th>comname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Charismatic/Pentecostal, Islam</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ABOABO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Charismatic/Pentecostal, Islam</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Islam</td>\n",
       "      <td>ABOABO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>Accra</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABREPO KUMA</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ABREPO KUMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADENTA WEST</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>Accra</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ADENTA WEST</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood              popular_religion_x Borough  population  \\\n",
       "0        ABOABO  Charismatic/Pentecostal, Islam  Kumasi       500.0   \n",
       "1        ABOABO  Charismatic/Pentecostal, Islam  Kumasi     35000.0   \n",
       "2  ABOSSEY OKAI         Charismatic/Pentecostal   Accra     12000.0   \n",
       "3   ABREPO KUMA         Charismatic/Pentecostal  Kumasi     20000.0   \n",
       "4   ADENTA WEST         Charismatic/Pentecostal   Accra    126000.0   \n",
       "\n",
       "        popular_religion_y       comname  \n",
       "0  Charismatic/Pentecostal        ABOABO  \n",
       "1                    Islam        ABOABO  \n",
       "2  Charismatic/Pentecostal  ABOSSEY OKAI  \n",
       "3  Charismatic/Pentecostal   ABREPO KUMA  \n",
       "4  Charismatic/Pentecostal   ADENTA WEST  "
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove or drop Neighborhood_y column\n",
    "nb2 = nb2.drop(columns=['popular_religion_x', 'comname'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>population</th>\n",
       "      <th>popular_religion_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Islam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Borough  population       popular_religion_y\n",
       "0       ABOABO  Kumasi       500.0  Charismatic/Pentecostal\n",
       "1       ABOABO  Kumasi     35000.0                    Islam"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename Neighborhood_X\n",
    "nb2.rename(columns={'popular_religion_y':'popular_religion'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>population</th>\n",
       "      <th>popular_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>Islam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Borough  population         popular_religion\n",
       "0       ABOABO  Kumasi       500.0  Charismatic/Pentecostal\n",
       "1       ABOABO  Kumasi     35000.0                    Islam"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec2 = pd.read_csv('/resources/data/sec2.csv', sep=',', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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>supid</th>\n",
       "      <th>clusterno</th>\n",
       "      <th>comname</th>\n",
       "      <th>cs2q1a</th>\n",
       "      <th>cs2q1ao</th>\n",
       "      <th>cs2q1b</th>\n",
       "      <th>cs2q1bo</th>\n",
       "      <th>cs2q1c</th>\n",
       "      <th>cs2q1co</th>\n",
       "      <th>cs2q1d</th>\n",
       "      <th>...</th>\n",
       "      <th>cs2q37</th>\n",
       "      <th>cs2q38</th>\n",
       "      <th>cs2q39a</th>\n",
       "      <th>cs2q39ao</th>\n",
       "      <th>cs2q39b</th>\n",
       "      <th>cs2q39bo</th>\n",
       "      <th>cs2q39c</th>\n",
       "      <th>csq29co</th>\n",
       "      <th>cs2q31both</th>\n",
       "      <th>cs2q36both</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>Trading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>BOAT TRANSPORT BUSINESS</td>\n",
       "      <td>NO OTHER</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>More difficult</td>\n",
       "      <td>Construction/Maintenance of school building</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO OTHER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>2</td>\n",
       "      <td>ELUBO</td>\n",
       "      <td>Trading</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO OTHER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>25-29 years</td>\n",
       "      <td>More difficult</td>\n",
       "      <td>Construction of Roads/Bridges</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NO OTHER</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           supid  clusterno     comname   cs2q1a cs2q1ao   cs2q1b cs2q1bo  \\\n",
       "0  John Botchway         42  JEWI WHARF  Trading     NaN  Fishing     NaN   \n",
       "1  John Botchway          2       ELUBO  Trading     NaN  Fishing     NaN   \n",
       "\n",
       "     cs2q1c                  cs2q1co    cs2q1d  ...       cs2q37  \\\n",
       "0     Other  BOAT TRANSPORT BUSINESS  NO OTHER  ...          NaN   \n",
       "1  NO OTHER                      NaN       NaN  ...  25-29 years   \n",
       "\n",
       "           cs2q38                                      cs2q39a cs2q39ao  \\\n",
       "0  More difficult  Construction/Maintenance of school building      NaN   \n",
       "1  More difficult                Construction of Roads/Bridges      NaN   \n",
       "\n",
       "    cs2q39b cs2q39bo cs2q39c csq29co cs2q31both cs2q36both  \n",
       "0  NO OTHER      NaN     NaN     NaN        NaN        NaN  \n",
       "1  NO OTHER      NaN     NaN     NaN        NaN        NaN  \n",
       "\n",
       "[2 rows x 79 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename columns\n",
    "sec2.rename(columns={'cs2q1a':'major_economic_act','cs2q6':'occassional_road_impasses','cs2q14':'post_office','cs2q19':'nearest_bank', \n",
    "                    'cs2q20':'permanent_daily_mkt'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec22 = sec2[['comname','major_economic_act','occassional_road_impasses','post_office','permanent_daily_mkt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comname</th>\n",
       "      <th>major_economic_act</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>Trading</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ELUBO</td>\n",
       "      <td>Trading</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EFASU</td>\n",
       "      <td>Farming</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MBEM</td>\n",
       "      <td>Trading</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ELOYN</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comname major_economic_act occassional_road_impasses post_office  \\\n",
       "0  JEWI WHARF            Trading                       Yes          No   \n",
       "1       ELUBO            Trading                        No         Yes   \n",
       "2       EFASU            Farming                       Yes          No   \n",
       "3        MBEM            Trading                        No          No   \n",
       "4       ELOYN            Fishing                        No          No   \n",
       "\n",
       "  permanent_daily_mkt  \n",
       "0                 Yes  \n",
       "1                 Yes  \n",
       "2                  No  \n",
       "3                  No  \n",
       "4                  No  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec22.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data\n",
    "#nb1 = nb3\n",
    "#nb = nb2\n",
    "nb3 = nb2.merge(sec22, left_on='Neighborhood', right_on='comname', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>Neighborhood</th>\n",
       "      <th>Borough</th>\n",
       "      <th>population</th>\n",
       "      <th>popular_religion</th>\n",
       "      <th>comname</th>\n",
       "      <th>major_economic_act</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Farming</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Kumasi</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Charismatic/Pentecostal</td>\n",
       "      <td>ABOABO</td>\n",
       "      <td>Trading</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Borough  population         popular_religion comname  \\\n",
       "0       ABOABO  Kumasi       500.0  Charismatic/Pentecostal  ABOABO   \n",
       "1       ABOABO  Kumasi       500.0  Charismatic/Pentecostal  ABOABO   \n",
       "\n",
       "  major_economic_act occassional_road_impasses post_office permanent_daily_mkt  \n",
       "0            Farming                       Yes          No                  No  \n",
       "1            Trading                        No         Yes                 Yes  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb3 = nb3.replace(['Yes'], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb3 = nb3.replace(['No'], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb33 = nb3.groupby('Neighborhood').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  \n",
       "0                  0.5  \n",
       "1                  0.0  "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb33.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec3 = pd.read_csv('/resources/data/sec3_prim.csv', sep=',', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supid</th>\n",
       "      <th>clusterno</th>\n",
       "      <th>comname</th>\n",
       "      <th>pmsc</th>\n",
       "      <th>cs3q5</th>\n",
       "      <th>cs3q6</th>\n",
       "      <th>cs3q7</th>\n",
       "      <th>cs3q8</th>\n",
       "      <th>cs3q9</th>\n",
       "      <th>cs3q10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>1</td>\n",
       "      <td>JEWI WHARF D/C PRIMARY</td>\n",
       "      <td>Public</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>2</td>\n",
       "      <td>JEWI WHARF SCHOOL</td>\n",
       "      <td>Public</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           supid  clusterno     comname  pmsc                   cs3q5   cs3q6  \\\n",
       "0  John Botchway         42  JEWI WHARF     1  JEWI WHARF D/C PRIMARY  Public   \n",
       "1  John Botchway         42  JEWI WHARF     2       JEWI WHARF SCHOOL  Public   \n",
       "\n",
       "   cs3q7 cs3q8  cs3q9 cs3q10  \n",
       "0  Mixed    No    NaN   1985  \n",
       "1  Mixed    No    NaN   1988  "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec33 = sec3[['comname','pmsc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note. there are more participants and that is why we need to take the mean. The mean here is the mean of the answers by the respondents\n",
    "sec333 = sec33.groupby('comname').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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>pmsc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comname</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABAASA</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABEPOTIA</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          pmsc\n",
       "comname       \n",
       "ABAASA     2.0\n",
       "ABEPOTIA   1.5"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec333.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data\n",
    "#nb1 = nb3\n",
    "#nb = nb2\n",
    "nb5 = nb33.merge(sec333, left_on='Neighborhood', right_on='comname', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pmsc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pmsc  \n",
       "0                  0.5  1.75  \n",
       "1                  0.0  2.00  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb5.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nb5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec4 = pd.read_csv('/resources/data/sec4.csv', sep=',', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "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>supid</th>\n",
       "      <th>clusterno</th>\n",
       "      <th>comname</th>\n",
       "      <th>jhsc</th>\n",
       "      <th>cs3q15</th>\n",
       "      <th>cs3q16</th>\n",
       "      <th>cs3q17</th>\n",
       "      <th>cs3q18</th>\n",
       "      <th>cs3q19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>1</td>\n",
       "      <td>JEWI WHARF DA JHS</td>\n",
       "      <td>Public</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Botchway</td>\n",
       "      <td>42</td>\n",
       "      <td>JEWI WHARF</td>\n",
       "      <td>2</td>\n",
       "      <td>JEWI WHARF ISLAMIC SCHOOL</td>\n",
       "      <td>Public</td>\n",
       "      <td>Mixed</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           supid  clusterno     comname  jhsc                     cs3q15  \\\n",
       "0  John Botchway         42  JEWI WHARF     1          JEWI WHARF DA JHS   \n",
       "1  John Botchway         42  JEWI WHARF     2  JEWI WHARF ISLAMIC SCHOOL   \n",
       "\n",
       "   cs3q16 cs3q17  cs3q18 cs3q19  \n",
       "0  Public  Mixed     3.0   1985  \n",
       "1  Public  Mixed     3.0   1988  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec4.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "sec44 = sec4[['comname','jhsc']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note. there are more participants and that is why we need to take the mean. The mean here is the mean of the answers by the respondents\n",
    "sec444 = sec44.groupby('comname').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jhsc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comname</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABAASA</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABEPOTIA</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          jhsc\n",
       "comname       \n",
       "ABAASA     2.0\n",
       "ABEPOTIA   1.0"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec444.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge data\n",
    "#nb1 = nb3\n",
    "#nb = nb2\n",
    "nb6 = nb5.merge(sec444, left_on='Neighborhood', right_on='comname', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "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>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pmsc</th>\n",
       "      <th>jhsc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pmsc  jhsc  \n",
       "0                  0.5  1.75  1.75  \n",
       "1                  0.0  2.00  2.00  "
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb6.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pmsc and jhsc seems the same, let's take the average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb6['pm_jh_mean'] = (nb6.pmsc + nb6.jhsc)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pmsc</th>\n",
       "      <th>jhsc</th>\n",
       "      <th>pm_jh_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pmsc  jhsc  pm_jh_mean  \n",
       "0                  0.5  1.75  1.75        1.75  \n",
       "1                  0.0  2.00  2.00        2.00  "
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb6.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove or drop Neighborhood_y column\n",
    "nb6 = nb6.drop(columns=['jhsc', 'pmsc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pm_jh_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pm_jh_mean  \n",
       "0                  0.5        1.75  \n",
       "1                  0.0        2.00  "
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb6.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "nb7 = nb6.merge(nb, left_on='Neighborhood', right_on='Neighborhood', how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pm_jh_mean</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABREPO KUMA</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADENTA WEST</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AHINSAN</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "2   ABREPO KUMA     20000.0                        0.0          0.0   \n",
       "3   ADENTA WEST    126000.0                        0.0          0.0   \n",
       "4       AHINSAN     25000.0                        0.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pm_jh_mean Borough  \n",
       "0                  0.5        1.75  Kumasi  \n",
       "1                  0.0        2.00   Accra  \n",
       "2                  1.0        2.00  Kumasi  \n",
       "3                  0.0        2.00   Accra  \n",
       "4                  0.0        2.00  Kumasi  "
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb7.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 7)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb7.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['ADENTA WEST'], 'ADENTA')\n",
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['ABREPO KUMA'], 'ABREPO')\n",
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['NEW TAFO (KROFROM)'], 'TAFO')\n",
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['NEW TAFO (MANHYIA EXTENSION)'], 'MANHYIA')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4285af1b38>"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb7.sort_values('population', ascending=True).plot.barh(x='Neighborhood', y='population', title = 'Population by Neighborhood', rot=0, figsize=(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "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>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pm_jh_mean</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABOABO</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABOSSEY OKAI</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "0        ABOABO     17750.0                        0.5          0.5   \n",
       "1  ABOSSEY OKAI     12000.0                        1.0          1.0   \n",
       "\n",
       "   permanent_daily_mkt  pm_jh_mean Borough  \n",
       "0                  0.5        1.75  Kumasi  \n",
       "1                  0.0        2.00   Accra  "
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb7.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb7.Neighborhood = nb7['Neighborhood'].str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['New suame'], 'Suame')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb7.Neighborhood = nb7['Neighborhood'].replace(['Bremang'], 'Breman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "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>Neighborhood</th>\n",
       "      <th>population</th>\n",
       "      <th>occassional_road_impasses</th>\n",
       "      <th>post_office</th>\n",
       "      <th>permanent_daily_mkt</th>\n",
       "      <th>pm_jh_mean</th>\n",
       "      <th>Borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Kisseman</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Taifa</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Sakumono</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Osu</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Ofankor</td>\n",
       "      <td>999999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Nungua</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Nima</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mamprobi</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Lashibi</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>La</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Korle bu</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Kokomlemle</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kanda</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>James town</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Dzorwulu</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Teshie</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Chorkor</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bubuashie</td>\n",
       "      <td>70000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abossey okai</td>\n",
       "      <td>12000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adenta</td>\n",
       "      <td>126000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ashongman</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alajo</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Darkuman</td>\n",
       "      <td>515499.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ashaiman</td>\n",
       "      <td>341085.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>Accra</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abrepo</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Pankrono</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahinsan</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Oforikrom</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Amakom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Manhyia</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Suame</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ayigya</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bantama</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Kronum</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bohyen</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Breman</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Tanoso</td>\n",
       "      <td>10750.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.221591</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Tafo</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aboabo</td>\n",
       "      <td>17750.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>Kumasi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighborhood  population  occassional_road_impasses  post_office  \\\n",
       "19      Kisseman    300000.0                        0.0     1.000000   \n",
       "37         Taifa     50000.0                        1.0     1.000000   \n",
       "36      Sakumono     66000.0                        0.0     1.000000   \n",
       "35         Ridge    120000.0                        0.0     0.000000   \n",
       "33           Osu     25000.0                        0.0     1.000000   \n",
       "31       Ofankor    999999.0                        0.0     1.000000   \n",
       "30        Nungua     10000.0                        0.0     1.000000   \n",
       "29          Nima     10000.0                        0.0     1.000000   \n",
       "25      Mamprobi     11000.0                        0.0     1.000000   \n",
       "24       Lashibi     22000.0                        0.0     1.000000   \n",
       "23            La     20000.0                        0.0     1.000000   \n",
       "21      Korle bu    200000.0                        0.0     1.000000   \n",
       "20    Kokomlemle     10000.0                        1.0     0.000000   \n",
       "18         Kanda     70000.0                        0.0     1.000000   \n",
       "17    James town    100000.0                        0.0     1.000000   \n",
       "16      Dzorwulu     16000.0                        0.0     0.000000   \n",
       "39        Teshie     45000.0                        0.0     1.000000   \n",
       "14       Chorkor     60000.0                        0.0     0.000000   \n",
       "13     Bubuashie     70000.0                        0.0     1.000000   \n",
       "1   Abossey okai     12000.0                        1.0     1.000000   \n",
       "3         Adenta    126000.0                        0.0     0.000000   \n",
       "8      Ashongman     10000.0                        0.0     1.000000   \n",
       "5          Alajo     15000.0                        0.0     1.000000   \n",
       "15      Darkuman    515499.5                        0.0     1.000000   \n",
       "7       Ashaiman    341085.0                        0.0     0.333333   \n",
       "2         Abrepo     20000.0                        0.0     0.000000   \n",
       "34      Pankrono     15000.0                        0.0     0.000000   \n",
       "4        Ahinsan     25000.0                        0.0     1.000000   \n",
       "32     Oforikrom     60000.0                        0.0     0.000000   \n",
       "6         Amakom     40000.0                        0.0     1.000000   \n",
       "28       Manhyia      2500.0                        0.0     0.000000   \n",
       "26         Suame     29000.0                        0.0     1.000000   \n",
       "9         Ayigya     20000.0                        0.0     1.000000   \n",
       "10       Bantama    120000.0                        0.0     1.000000   \n",
       "22        Kronum     15000.0                        0.0     0.000000   \n",
       "11        Bohyen     10500.0                        0.0     1.000000   \n",
       "12        Breman     15000.0                        0.0     0.000000   \n",
       "38        Tanoso     10750.0                        0.0     0.000000   \n",
       "27          Tafo     20000.0                        0.0     1.000000   \n",
       "0         Aboabo     17750.0                        0.5     0.500000   \n",
       "\n",
       "    permanent_daily_mkt  pm_jh_mean Borough  \n",
       "19             1.000000    3.500000   Accra  \n",
       "37             1.000000    2.000000   Accra  \n",
       "36             1.000000    2.000000   Accra  \n",
       "35             1.000000    2.500000   Accra  \n",
       "33             1.000000    2.000000   Accra  \n",
       "31             1.000000    1.500000   Accra  \n",
       "30             1.000000    3.000000   Accra  \n",
       "29             1.000000    1.500000   Accra  \n",
       "25             0.000000    2.000000   Accra  \n",
       "24             1.000000    2.000000   Accra  \n",
       "23             1.000000    2.000000   Accra  \n",
       "21             0.000000    1.750000   Accra  \n",
       "20             0.000000    2.250000   Accra  \n",
       "18             0.000000    2.500000   Accra  \n",
       "17             1.000000    1.500000   Accra  \n",
       "16             0.000000    2.000000   Accra  \n",
       "39             0.000000    2.000000   Accra  \n",
       "14             0.000000    2.000000   Accra  \n",
       "13             0.000000    2.000000   Accra  \n",
       "1              0.000000    2.000000   Accra  \n",
       "3              0.000000    2.000000   Accra  \n",
       "8              0.000000    2.000000   Accra  \n",
       "5              1.000000    2.000000   Accra  \n",
       "15             1.000000    2.000000   Accra  \n",
       "7              0.666667    1.875000   Accra  \n",
       "2              1.000000    2.000000  Kumasi  \n",
       "34             1.000000    2.000000  Kumasi  \n",
       "4              0.000000    2.000000  Kumasi  \n",
       "32             1.000000    2.000000  Kumasi  \n",
       "6              1.000000    2.000000  Kumasi  \n",
       "28             0.000000    1.500000  Kumasi  \n",
       "26             1.000000    2.000000  Kumasi  \n",
       "9              1.000000    2.000000  Kumasi  \n",
       "10             1.000000    2.000000  Kumasi  \n",
       "22             1.000000    2.000000  Kumasi  \n",
       "11             1.000000    2.000000  Kumasi  \n",
       "12             1.000000    2.000000  Kumasi  \n",
       "38             1.000000    3.221591  Kumasi  \n",
       "27             1.000000    2.000000  Kumasi  \n",
       "0              0.500000    1.750000  Kumasi  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb7.sort_values('Borough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb7.to_csv('/resources/data/nb7.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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