{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting boto3\n",
      "  Downloading boto3-1.12.29-py2.py3-none-any.whl (128 kB)\n",
      "\u001b[K     |████████████████████████████████| 128 kB 6.4 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting google-cloud-storage\n",
      "  Using cached google_cloud_storage-1.26.0-py2.py3-none-any.whl (75 kB)\n",
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      "\u001b[K     |████████████████████████████████| 6.0 MB 9.4 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n",
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      "Processing /home/jupyter/.cache/pip/wheels/4c/a1/71/5e427276ceeff277fd76878d1b19fbf4587a2845015d86864b/googleapis_common_protos-1.51.0-py3-none-any.whl\n",
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      "Installing collected packages: xgboost, jmespath, docutils, botocore, s3transfer, boto3, google-resumable-media, rsa, google-auth, googleapis-common-protos, google-api-core, google-cloud-core, google-cloud-storage, smart-open, gensim, future, funcy, pyLDAvis\n",
      "Successfully installed boto3-1.12.29 botocore-1.15.29 docutils-0.15.2 funcy-1.14 future-0.18.2 gensim-3.8.1 google-api-core-1.16.0 google-auth-1.11.3 google-cloud-core-1.3.0 google-cloud-storage-1.26.0 google-resumable-media-0.5.0 googleapis-common-protos-1.51.0 jmespath-0.9.5 pyLDAvis-2.1.2 rsa-4.0 s3transfer-0.3.3 smart-open-1.10.0 xgboost-1.0.2\n"
     ]
    }
   ],
   "source": [
    "! pip install qeds fiona geopandas xgboost gensim folium pyLDAvis descartes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from shapely.geometry import Point\n",
    "import plotly.express as px\n",
    "\n",
    "%matplotlib inline\n",
    "# activate plot theme\n",
    "import qeds\n",
    "qeds.themes.mpl_style();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I start by uploading my thesis data. For my thesis, I am interested in understanding how increasing the intensity of the measure of fatalities affects support for Duterte's party, PDP Laban, in the most recent mayoral elections. Since Duterte was elected in 2016, the Philippines has become a hotbed for extra judicial killings of drug suspects. There's a lot of literature in economics about the effect of conflict on political and economic outcomes. One really important paper for my thesis is done by Luisa Blanco in 2012, who finds that increased perceptions of insecurity(resulting from exposure to violent crime) leads to distrust in the government and also increased palatability of authoritarianism(partially because of its perceived efficiency in dealing with crime). The Philippines offers an interesting context: On the one hand, the majority of the extra-judicial killings have been comitted or encouraged by the state towards mostly low income Filipinos. This could lead to  Filipinos feeling less safe and distrusting the government. On the other hand, the Philippines has a long history of dealing with inefficient courts, and so the drug war could be welcomed as an efficient way of dealing with the drug trade. The purpose of my thesis is to see which effect dominates in the Philippine setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>province</th>\n",
       "      <th>muni</th>\n",
       "      <th>PDPpercent</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>province_code</th>\n",
       "      <th>municipality_city_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>location</th>\n",
       "      <th>...</th>\n",
       "      <th>lon_a_5</th>\n",
       "      <th>lat_a_5</th>\n",
       "      <th>d_a_5</th>\n",
       "      <th>PDPran</th>\n",
       "      <th>FPDP</th>\n",
       "      <th>pi_g</th>\n",
       "      <th>fatalities_pc</th>\n",
       "      <th>fatalities_p_c</th>\n",
       "      <th>pi_a</th>\n",
       "      <th>Coordinates</th>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140103000</td>\n",
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       "      <td>POINT(120.719846281 17.523537522)</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>330.886511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>POINT (120.7198486328125 17.52353668212891)</td>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
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       "      <td>POINT (120.8480606079102 17.43698883056641)</td>\n",
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       "    <tr>\n",
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       "      <td>ABRA</td>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.331700</td>\n",
       "      <td>POINT (120.952278137207 17.4498291015625)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  province       muni  PDPpercent                                  region  \\\n",
       "0     ABRA    BANGUED    0.101855  Cordillera Administrative Region (CAR)   \n",
       "1     ABRA    BOLINEY    0.000000  Cordillera Administrative Region (CAR)   \n",
       "2     ABRA      BUCAY    0.053107  Cordillera Administrative Region (CAR)   \n",
       "3     ABRA     BUCLOC    0.047902  Cordillera Administrative Region (CAR)   \n",
       "4     ABRA  DAGUIOMAN    0.007278  Cordillera Administrative Region (CAR)   \n",
       "\n",
       "   region_code province_code municipality_city_code   longitude   latitude  \\\n",
       "0  PH140000000   PH140100000            PH140101000  120.600914  17.611204   \n",
       "1  PH140000000   PH140100000            PH140102000  120.864738  17.378698   \n",
       "2  PH140000000   PH140100000            PH140103000  120.719849  17.523537   \n",
       "3  PH140000000   PH140100000            PH140104000  120.848061  17.436989   \n",
       "4  PH140000000   PH140100000            PH140105000  120.952278  17.449829   \n",
       "\n",
       "                             location  ...     lon_a_5  lat_a_5       d_a_5  \\\n",
       "0  POINT(120.600912102 17.6112035614)  ...  120.991997   14.545  341.874815   \n",
       "1  POINT(120.864738788 17.3786990212)  ...  120.991997   14.545  313.868504   \n",
       "2   POINT(120.719846281 17.523537522)  ...  120.991997   14.545  330.886511   \n",
       "3  POINT(120.848063524 17.4369884989)  ...  120.991997   14.545  320.394546   \n",
       "4  POINT(120.952276395 17.4498298787)  ...  120.991997   14.545  321.473186   \n",
       "\n",
       "   PDPran FPDP      pi_g fatalities_pc fatalities_p_c      pi_a  \\\n",
       "0     1.0  6.0 -0.386866      0.000125       1.245770  0.126267   \n",
       "1     0.0  0.0 -0.627300      0.000000       0.000000  0.451233   \n",
       "2     1.0  1.0 -0.562465      0.000058       0.584283  0.298767   \n",
       "3     1.0  0.0 -0.727650      0.000000       0.000000  0.467700   \n",
       "4     1.0  0.0 -0.056885      0.000000       0.000000  0.331700   \n",
       "\n",
       "                                   Coordinates  \n",
       "0  POINT (120.6009140014648 17.61120414733887)  \n",
       "1  POINT (120.8647384643555 17.37869834899902)  \n",
       "2  POINT (120.7198486328125 17.52353668212891)  \n",
       "3  POINT (120.8480606079102 17.43698883056641)  \n",
       "4    POINT (120.952278137207 17.4498291015625)  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#read thesis data here\n",
    "\n",
    "PH = pd.read_stata(\"Collapsed merged with controls and dist!(unmatched munis dropped) 2.dta\")\n",
    "\n",
    "#generating the coordinates\n",
    "PH[\"Coordinates\"] = list(zip(PH.longitude, PH.latitude))\n",
    "PH.head()\n",
    "PH[\"Coordinates\"] = PH[\"Coordinates\"].apply(Point)\n",
    "PH.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PDP percent is my variable for the vote share of Duterte's party's candidates.\n",
    "\n",
    "Next, I upload the stanford shape file that contains the polygon data at the municipality level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "PHshape2 = gpd.read_file(\"PHL_stanford.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff67eca37d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PHshape2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the world map:\n",
    "\n",
    "world = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\"))\n",
    "world = world.set_index(\"iso_a3\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
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      ],
      "text/plain": [
       "<shapely.geometry.multipolygon.MultiPolygon at 0x7ff67eaaad10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.loc[\"PHL\",'geometry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1615 entries, 0 to 1614\n",
      "Data columns (total 63 columns):\n",
      " #   Column                  Non-Null Count  Dtype   \n",
      "---  ------                  --------------  -----   \n",
      " 0   province                1615 non-null   object  \n",
      " 1   muni                    1615 non-null   object  \n",
      " 2   PDPpercent              1615 non-null   float32 \n",
      " 3   region                  1615 non-null   object  \n",
      " 4   region_code             1615 non-null   object  \n",
      " 5   province_code           1615 non-null   object  \n",
      " 6   municipality_city_code  1615 non-null   object  \n",
      " 7   longitude               1615 non-null   float32 \n",
      " 8   latitude                1615 non-null   float32 \n",
      " 9   location                1615 non-null   object  \n",
      " 10  muni_code_numeric       1615 non-null   int32   \n",
      " 11  p_2009                  1615 non-null   float32 \n",
      " 12  p_2012                  1615 non-null   float32 \n",
      " 13  p_2015                  1615 non-null   float32 \n",
      " 14  var26                   1615 non-null   object  \n",
      " 15  municipality_code       1615 non-null   object  \n",
      " 16  Male                    1615 non-null   object  \n",
      " 17  Female                  1615 non-null   object  \n",
      " 18  population              1615 non-null   int32   \n",
      " 19  portname                1615 non-null   object  \n",
      " 20  prttype                 1615 non-null   object  \n",
      " 21  lat_s                   1615 non-null   float32 \n",
      " 22  lon_s                   1615 non-null   float32 \n",
      " 23  iso3                    1615 non-null   object  \n",
      " 24  iso3_op                 1615 non-null   object  \n",
      " 25  remarks                 1615 non-null   object  \n",
      " 26  geonameid               1615 non-null   int32   \n",
      " 27  gdb_geomattr_data       0 non-null      float64 \n",
      " 28  d_s                     1615 non-null   float64 \n",
      " 29  muni1                   1615 non-null   object  \n",
      " 30  muni2                   1615 non-null   object  \n",
      " 31  muni11                  1615 non-null   object  \n",
      " 32  muni12                  1615 non-null   object  \n",
      " 33  fatalities              1615 non-null   float64 \n",
      " 34  regioncode              1615 non-null   object  \n",
      " 35  provincecode            1615 non-null   object  \n",
      " 36  city_municipality       1615 non-null   object  \n",
      " 37  city_muncode            1615 non-null   object  \n",
      " 38  airport                 1615 non-null   object  \n",
      " 39  classification          1615 non-null   object  \n",
      " 40  description             1615 non-null   object  \n",
      " 41  address                 1615 non-null   object  \n",
      " 42  lon_a                   1615 non-null   float32 \n",
      " 43  lat_a                   1615 non-null   float32 \n",
      " 44  d_a                     1615 non-null   float64 \n",
      " 45  a_typhoon               1615 non-null   float64 \n",
      " 46  a_earthquake            1615 non-null   float64 \n",
      " 47  _merge1                 1615 non-null   category\n",
      " 48  votePDP                 1615 non-null   float32 \n",
      " 49  fatality_b              1615 non-null   float32 \n",
      " 50  lat_s_5                 1615 non-null   float32 \n",
      " 51  lon_s_5                 1615 non-null   float32 \n",
      " 52  d_s_5                   1615 non-null   float64 \n",
      " 53  lon_a_5                 1615 non-null   float32 \n",
      " 54  lat_a_5                 1615 non-null   float32 \n",
      " 55  d_a_5                   1615 non-null   float64 \n",
      " 56  PDPran                  1615 non-null   float32 \n",
      " 57  FPDP                    1615 non-null   float32 \n",
      " 58  pi_g                    1615 non-null   float32 \n",
      " 59  fatalities_pc           1615 non-null   float32 \n",
      " 60  fatalities_p_c          1615 non-null   float32 \n",
      " 61  pi_a                    1615 non-null   float32 \n",
      " 62  Coordinates             1615 non-null   object  \n",
      "dtypes: category(1), float32(22), float64(8), int32(3), object(29)\n",
      "memory usage: 638.8+ KB\n"
     ]
    }
   ],
   "source": [
    "PH.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I now convert my thesis data to a geopanda data frame so that I can merge using geometric locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>province</th>\n",
       "      <th>muni</th>\n",
       "      <th>PDPpercent</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>province_code</th>\n",
       "      <th>municipality_city_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>location</th>\n",
       "      <th>...</th>\n",
       "      <th>lon_a_5</th>\n",
       "      <th>lat_a_5</th>\n",
       "      <th>d_a_5</th>\n",
       "      <th>PDPran</th>\n",
       "      <th>FPDP</th>\n",
       "      <th>pi_g</th>\n",
       "      <th>fatalities_pc</th>\n",
       "      <th>fatalities_p_c</th>\n",
       "      <th>pi_a</th>\n",
       "      <th>Coordinates</th>\n",
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       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>341.874815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.386866</td>\n",
       "      <td>0.000125</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABRA</td>\n",
       "      <td>BOLINEY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
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       "      <td>120.864738</td>\n",
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       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>313.868504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.627300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>2</th>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCAY</td>\n",
       "      <td>0.053107</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140103000</td>\n",
       "      <td>120.719849</td>\n",
       "      <td>17.523537</td>\n",
       "      <td>POINT(120.719846281 17.523537522)</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>330.886511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.562465</td>\n",
       "      <td>0.000058</td>\n",
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       "    <tr>\n",
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       "      <td>ABRA</td>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
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       "      <td>120.848061</td>\n",
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       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>320.394546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.727650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>POINT (120.84806 17.43699)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABRA</td>\n",
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       "      <td>Cordillera Administrative Region (CAR)</td>\n",
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       "      <td>PH140100000</td>\n",
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       "      <td>17.449829</td>\n",
       "      <td>POINT(120.952276395 17.4498298787)</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>321.473186</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.056885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>POINT (120.95228 17.44983)</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1610</th>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
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       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098312000</td>\n",
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       "      <td>POINT(122.430683899 7.7020220193)</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>242.523021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.112825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.489067</td>\n",
       "      <td>POINT (122.43069 7.70202)</td>\n",
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       "    <tr>\n",
       "      <th>1611</th>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>SIAY</td>\n",
       "      <td>0.260228</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098313000</td>\n",
       "      <td>122.876854</td>\n",
       "      <td>7.734716</td>\n",
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       "      <td>8.592</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.042893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1612</th>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TALUSAN</td>\n",
       "      <td>0.178658</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098314000</td>\n",
       "      <td>122.884216</td>\n",
       "      <td>7.334711</td>\n",
       "      <td>POINT(122.884218322 7.3347104573)</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>220.979871</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.158900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606933</td>\n",
       "      <td>POINT (122.88422 7.33471)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613</th>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TITAY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098315000</td>\n",
       "      <td>122.517639</td>\n",
       "      <td>7.845210</td>\n",
       "      <td>POINT(122.517641 7.84521002867)</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>227.549967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.414100</td>\n",
       "      <td>POINT (122.51764 7.84521)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TUNGAWAN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098316000</td>\n",
       "      <td>122.370041</td>\n",
       "      <td>7.568430</td>\n",
       "      <td>POINT(122.370040541 7.56843016062)</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>254.889034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.121783</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.475851</td>\n",
       "      <td>0.559233</td>\n",
       "      <td>POINT (122.37004 7.56843)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1615 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               province          muni  PDPpercent  \\\n",
       "0                  ABRA       BANGUED    0.101855   \n",
       "1                  ABRA       BOLINEY    0.000000   \n",
       "2                  ABRA         BUCAY    0.053107   \n",
       "3                  ABRA        BUCLOC    0.047902   \n",
       "4                  ABRA     DAGUIOMAN    0.007278   \n",
       "...                 ...           ...         ...   \n",
       "1610  ZAMBOANGA SIBUGAY  ROSELLER LIM    0.197443   \n",
       "1611  ZAMBOANGA SIBUGAY          SIAY    0.260228   \n",
       "1612  ZAMBOANGA SIBUGAY       TALUSAN    0.178658   \n",
       "1613  ZAMBOANGA SIBUGAY         TITAY    0.000000   \n",
       "1614  ZAMBOANGA SIBUGAY      TUNGAWAN    0.000000   \n",
       "\n",
       "                                      region  region_code province_code  \\\n",
       "0     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "1     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "2     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "3     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "4     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "...                                      ...          ...           ...   \n",
       "1610         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1611         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1612         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1613         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1614         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "\n",
       "     municipality_city_code   longitude   latitude  \\\n",
       "0               PH140101000  120.600914  17.611204   \n",
       "1               PH140102000  120.864738  17.378698   \n",
       "2               PH140103000  120.719849  17.523537   \n",
       "3               PH140104000  120.848061  17.436989   \n",
       "4               PH140105000  120.952278  17.449829   \n",
       "...                     ...         ...        ...   \n",
       "1610            PH098312000  122.430687   7.702022   \n",
       "1611            PH098313000  122.876854   7.734716   \n",
       "1612            PH098314000  122.884216   7.334711   \n",
       "1613            PH098315000  122.517639   7.845210   \n",
       "1614            PH098316000  122.370041   7.568430   \n",
       "\n",
       "                                location  ...     lon_a_5  lat_a_5  \\\n",
       "0     POINT(120.600912102 17.6112035614)  ...  120.991997   14.545   \n",
       "1     POINT(120.864738788 17.3786990212)  ...  120.991997   14.545   \n",
       "2      POINT(120.719846281 17.523537522)  ...  120.991997   14.545   \n",
       "3     POINT(120.848063524 17.4369884989)  ...  120.991997   14.545   \n",
       "4     POINT(120.952276395 17.4498298787)  ...  120.991997   14.545   \n",
       "...                                  ...  ...         ...      ...   \n",
       "1610   POINT(122.430683899 7.7020220193)  ...  124.442001    8.592   \n",
       "1611  POINT(122.876851077 7.73471573608)  ...  124.442001    8.592   \n",
       "1612   POINT(122.884218322 7.3347104573)  ...  124.442001    8.592   \n",
       "1613     POINT(122.517641 7.84521002867)  ...  124.442001    8.592   \n",
       "1614  POINT(122.370040541 7.56843016062)  ...  124.442001    8.592   \n",
       "\n",
       "           d_a_5  PDPran FPDP      pi_g fatalities_pc fatalities_p_c  \\\n",
       "0     341.874815     1.0  6.0 -0.386866      0.000125       1.245770   \n",
       "1     313.868504     0.0  0.0 -0.627300      0.000000       0.000000   \n",
       "2     330.886511     1.0  1.0 -0.562465      0.000058       0.584283   \n",
       "3     320.394546     1.0  0.0 -0.727650      0.000000       0.000000   \n",
       "4     321.473186     1.0  0.0 -0.056885      0.000000       0.000000   \n",
       "...          ...     ...  ...       ...           ...            ...   \n",
       "1610  242.523021     1.0  0.0 -0.112825      0.000000       0.000000   \n",
       "1611  196.818101     1.0  0.0 -0.042893      0.000000       0.000000   \n",
       "1612  220.979871     1.0  0.0 -0.158900      0.000000       0.000000   \n",
       "1613  227.549967     0.0  0.0  0.017960      0.000000       0.000000   \n",
       "1614  254.889034     0.0  0.0 -0.121783      0.000048       0.475851   \n",
       "\n",
       "          pi_a                 Coordinates  \n",
       "0     0.126267  POINT (120.60091 17.61120)  \n",
       "1     0.451233  POINT (120.86474 17.37870)  \n",
       "2     0.298767  POINT (120.71985 17.52354)  \n",
       "3     0.467700  POINT (120.84806 17.43699)  \n",
       "4     0.331700  POINT (120.95228 17.44983)  \n",
       "...        ...                         ...  \n",
       "1610  0.489067   POINT (122.43069 7.70202)  \n",
       "1611  0.440700   POINT (122.87685 7.73472)  \n",
       "1612  0.606933   POINT (122.88422 7.33471)  \n",
       "1613  0.414100   POINT (122.51764 7.84521)  \n",
       "1614  0.559233   POINT (122.37004 7.56843)  \n",
       "\n",
       "[1615 rows x 63 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PHvote = gpd.GeoDataFrame(PH, geometry=\"Coordinates\")\n",
    "\n",
    "PHvote\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cells below, I merge my thesis data with the shape file I need to plot the municipalities and the cities. I tried using all 3 versions of \"op\" ; the other 2 are \"contains\" and \"within\". \"Intersects\" gives me the best merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_left</th>\n",
       "      <th>province</th>\n",
       "      <th>muni</th>\n",
       "      <th>PDPpercent</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>province_code</th>\n",
       "      <th>municipality_city_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>lon_a_5</th>\n",
       "      <th>lat_a_5</th>\n",
       "      <th>d_a_5</th>\n",
       "      <th>PDPran</th>\n",
       "      <th>FPDP</th>\n",
       "      <th>pi_g</th>\n",
       "      <th>fatalities_pc</th>\n",
       "      <th>fatalities_p_c</th>\n",
       "      <th>pi_a</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BANGUED</td>\n",
       "      <td>0.101855</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140101000</td>\n",
       "      <td>120.600914</td>\n",
       "      <td>17.611204</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>341.874815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.386866</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>1.245770</td>\n",
       "      <td>0.126267</td>\n",
       "      <td>POLYGON ((120.61553 17.62360, 120.61870 17.623...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BOLINEY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140102000</td>\n",
       "      <td>120.864738</td>\n",
       "      <td>17.378698</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>313.868504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.451233</td>\n",
       "      <td>POLYGON ((120.90749 17.41933, 120.92464 17.413...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCAY</td>\n",
       "      <td>0.053107</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140103000</td>\n",
       "      <td>120.719849</td>\n",
       "      <td>17.523537</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>330.886511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.562465</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.584283</td>\n",
       "      <td>0.298767</td>\n",
       "      <td>POLYGON ((120.74548 17.57671, 120.75101 17.575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCLOC</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140104000</td>\n",
       "      <td>120.848061</td>\n",
       "      <td>17.436989</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>320.394546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>POLYGON ((120.82477 17.41721, 120.78956 17.416...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>DAGUIOMAN</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140105000</td>\n",
       "      <td>120.952278</td>\n",
       "      <td>17.449829</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>321.473186</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>1579</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((120.23167 14.85611, 120.23222 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((123.39980 8.44444, 123.40269 8.44411...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((123.54957 8.21537, 123.54903 8.20592...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((123.18555 7.84652, 123.18510 7.84630...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((122.99306 7.82587, 123.00117 7.81298...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1701 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index_left province       muni  PDPpercent  \\\n",
       "0            0.0     ABRA    BANGUED    0.101855   \n",
       "1            1.0     ABRA    BOLINEY    0.000000   \n",
       "2            2.0     ABRA      BUCAY    0.053107   \n",
       "3            3.0     ABRA     BUCLOC    0.047902   \n",
       "4            4.0     ABRA  DAGUIOMAN    0.007278   \n",
       "...          ...      ...        ...         ...   \n",
       "1579         NaN      NaN        NaN         NaN   \n",
       "1595         NaN      NaN        NaN         NaN   \n",
       "1614         NaN      NaN        NaN         NaN   \n",
       "1617         NaN      NaN        NaN         NaN   \n",
       "1638         NaN      NaN        NaN         NaN   \n",
       "\n",
       "                                      region  region_code province_code  \\\n",
       "0     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "1     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "2     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "3     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "4     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "...                                      ...          ...           ...   \n",
       "1579                                     NaN          NaN           NaN   \n",
       "1595                                     NaN          NaN           NaN   \n",
       "1614                                     NaN          NaN           NaN   \n",
       "1617                                     NaN          NaN           NaN   \n",
       "1638                                     NaN          NaN           NaN   \n",
       "\n",
       "     municipality_city_code   longitude   latitude  ...     lon_a_5  lat_a_5  \\\n",
       "0               PH140101000  120.600914  17.611204  ...  120.991997   14.545   \n",
       "1               PH140102000  120.864738  17.378698  ...  120.991997   14.545   \n",
       "2               PH140103000  120.719849  17.523537  ...  120.991997   14.545   \n",
       "3               PH140104000  120.848061  17.436989  ...  120.991997   14.545   \n",
       "4               PH140105000  120.952278  17.449829  ...  120.991997   14.545   \n",
       "...                     ...         ...        ...  ...         ...      ...   \n",
       "1579                    NaN         NaN        NaN  ...         NaN      NaN   \n",
       "1595                    NaN         NaN        NaN  ...         NaN      NaN   \n",
       "1614                    NaN         NaN        NaN  ...         NaN      NaN   \n",
       "1617                    NaN         NaN        NaN  ...         NaN      NaN   \n",
       "1638                    NaN         NaN        NaN  ...         NaN      NaN   \n",
       "\n",
       "           d_a_5  PDPran  FPDP      pi_g fatalities_pc fatalities_p_c  \\\n",
       "0     341.874815     1.0   6.0 -0.386866      0.000125       1.245770   \n",
       "1     313.868504     0.0   0.0 -0.627300      0.000000       0.000000   \n",
       "2     330.886511     1.0   1.0 -0.562465      0.000058       0.584283   \n",
       "3     320.394546     1.0   0.0 -0.727650      0.000000       0.000000   \n",
       "4     321.473186     1.0   0.0 -0.056885      0.000000       0.000000   \n",
       "...          ...     ...   ...       ...           ...            ...   \n",
       "1579         NaN     NaN   NaN       NaN           NaN            NaN   \n",
       "1595         NaN     NaN   NaN       NaN           NaN            NaN   \n",
       "1614         NaN     NaN   NaN       NaN           NaN            NaN   \n",
       "1617         NaN     NaN   NaN       NaN           NaN            NaN   \n",
       "1638         NaN     NaN   NaN       NaN           NaN            NaN   \n",
       "\n",
       "          pi_a                                           geometry  \n",
       "0     0.126267  POLYGON ((120.61553 17.62360, 120.61870 17.623...  \n",
       "1     0.451233  POLYGON ((120.90749 17.41933, 120.92464 17.413...  \n",
       "2     0.298767  POLYGON ((120.74548 17.57671, 120.75101 17.575...  \n",
       "3     0.467700  POLYGON ((120.82477 17.41721, 120.78956 17.416...  \n",
       "4     0.331700  POLYGON ((120.98141 17.50534, 120.98367 17.504...  \n",
       "...        ...                                                ...  \n",
       "1579       NaN  MULTIPOLYGON (((120.23167 14.85611, 120.23222 ...  \n",
       "1595       NaN  POLYGON ((123.39980 8.44444, 123.40269 8.44411...  \n",
       "1614       NaN  POLYGON ((123.54957 8.21537, 123.54903 8.20592...  \n",
       "1617       NaN  POLYGON ((123.18555 7.84652, 123.18510 7.84630...  \n",
       "1638       NaN  POLYGON ((122.99306 7.82587, 123.00117 7.81298...  \n",
       "\n",
       "[1701 rows x 64 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##merging the spacial data with the thesis data\n",
    "\n",
    "PHmerge = gpd.sjoin(PHvote, PHshape2, how = \"right\", op = 'intersects')\n",
    "PHmerge\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I clean the merged data set a little bit to only retain matched observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_left</th>\n",
       "      <th>province</th>\n",
       "      <th>muni</th>\n",
       "      <th>PDPpercent</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>province_code</th>\n",
       "      <th>municipality_city_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>lon_a_5</th>\n",
       "      <th>lat_a_5</th>\n",
       "      <th>d_a_5</th>\n",
       "      <th>PDPran</th>\n",
       "      <th>FPDP</th>\n",
       "      <th>pi_g</th>\n",
       "      <th>fatalities_pc</th>\n",
       "      <th>fatalities_p_c</th>\n",
       "      <th>pi_a</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BANGUED</td>\n",
       "      <td>0.101855</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140101000</td>\n",
       "      <td>120.600914</td>\n",
       "      <td>17.611204</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>341.874815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.386866</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>1.245770</td>\n",
       "      <td>0.126267</td>\n",
       "      <td>POLYGON ((120.61553 17.62360, 120.61870 17.623...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BOLINEY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140102000</td>\n",
       "      <td>120.864738</td>\n",
       "      <td>17.378698</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>313.868504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.627300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.451233</td>\n",
       "      <td>POLYGON ((120.90749 17.41933, 120.92464 17.413...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCAY</td>\n",
       "      <td>0.053107</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140103000</td>\n",
       "      <td>120.719849</td>\n",
       "      <td>17.523537</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>330.886511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.562465</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.584283</td>\n",
       "      <td>0.298767</td>\n",
       "      <td>POLYGON ((120.74548 17.57671, 120.75101 17.575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCLOC</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140104000</td>\n",
       "      <td>120.848061</td>\n",
       "      <td>17.436989</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>320.394546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.727650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.467700</td>\n",
       "      <td>POLYGON ((120.82477 17.41721, 120.78956 17.416...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>DAGUIOMAN</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140105000</td>\n",
       "      <td>120.952278</td>\n",
       "      <td>17.449829</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>321.473186</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.056885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.331700</td>\n",
       "      <td>POLYGON ((120.98141 17.50534, 120.98367 17.504...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>1610.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>ROSELLER LIM</td>\n",
       "      <td>0.197443</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098312000</td>\n",
       "      <td>122.430687</td>\n",
       "      <td>7.702022</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>242.523021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.112825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.489067</td>\n",
       "      <td>MULTIPOLYGON (((122.54519 7.68240, 122.54472 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>1611.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>SIAY</td>\n",
       "      <td>0.260228</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098313000</td>\n",
       "      <td>122.876854</td>\n",
       "      <td>7.734716</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>196.818101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.042893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440700</td>\n",
       "      <td>MULTIPOLYGON (((122.80055 7.73917, 122.80083 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>1612.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TALUSAN</td>\n",
       "      <td>0.178658</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098314000</td>\n",
       "      <td>122.884216</td>\n",
       "      <td>7.334711</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>220.979871</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.158900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606933</td>\n",
       "      <td>MULTIPOLYGON (((122.91917 7.34111, 122.91861 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>1613.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TITAY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098315000</td>\n",
       "      <td>122.517639</td>\n",
       "      <td>7.845210</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>227.549967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.414100</td>\n",
       "      <td>POLYGON ((122.68086 7.88831, 122.68091 7.88682...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>1614.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TUNGAWAN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098316000</td>\n",
       "      <td>122.370041</td>\n",
       "      <td>7.568430</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>254.889034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.121783</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.475851</td>\n",
       "      <td>0.559233</td>\n",
       "      <td>MULTIPOLYGON (((122.41989 7.49679, 122.42042 7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1613 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index_left           province          muni  PDPpercent  \\\n",
       "0            0.0               ABRA       BANGUED    0.101855   \n",
       "1            1.0               ABRA       BOLINEY    0.000000   \n",
       "2            2.0               ABRA         BUCAY    0.053107   \n",
       "3            3.0               ABRA        BUCLOC    0.047902   \n",
       "4            4.0               ABRA     DAGUIOMAN    0.007278   \n",
       "...          ...                ...           ...         ...   \n",
       "1647      1610.0  ZAMBOANGA SIBUGAY  ROSELLER LIM    0.197443   \n",
       "1648      1611.0  ZAMBOANGA SIBUGAY          SIAY    0.260228   \n",
       "1649      1612.0  ZAMBOANGA SIBUGAY       TALUSAN    0.178658   \n",
       "1650      1613.0  ZAMBOANGA SIBUGAY         TITAY    0.000000   \n",
       "1651      1614.0  ZAMBOANGA SIBUGAY      TUNGAWAN    0.000000   \n",
       "\n",
       "                                      region  region_code province_code  \\\n",
       "0     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "1     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "2     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "3     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "4     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "...                                      ...          ...           ...   \n",
       "1647         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1648         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1649         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1650         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1651         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "\n",
       "     municipality_city_code   longitude   latitude  ...     lon_a_5  lat_a_5  \\\n",
       "0               PH140101000  120.600914  17.611204  ...  120.991997   14.545   \n",
       "1               PH140102000  120.864738  17.378698  ...  120.991997   14.545   \n",
       "2               PH140103000  120.719849  17.523537  ...  120.991997   14.545   \n",
       "3               PH140104000  120.848061  17.436989  ...  120.991997   14.545   \n",
       "4               PH140105000  120.952278  17.449829  ...  120.991997   14.545   \n",
       "...                     ...         ...        ...  ...         ...      ...   \n",
       "1647            PH098312000  122.430687   7.702022  ...  124.442001    8.592   \n",
       "1648            PH098313000  122.876854   7.734716  ...  124.442001    8.592   \n",
       "1649            PH098314000  122.884216   7.334711  ...  124.442001    8.592   \n",
       "1650            PH098315000  122.517639   7.845210  ...  124.442001    8.592   \n",
       "1651            PH098316000  122.370041   7.568430  ...  124.442001    8.592   \n",
       "\n",
       "           d_a_5  PDPran  FPDP      pi_g fatalities_pc fatalities_p_c  \\\n",
       "0     341.874815     1.0   6.0 -0.386866      0.000125       1.245770   \n",
       "1     313.868504     0.0   0.0 -0.627300      0.000000       0.000000   \n",
       "2     330.886511     1.0   1.0 -0.562465      0.000058       0.584283   \n",
       "3     320.394546     1.0   0.0 -0.727650      0.000000       0.000000   \n",
       "4     321.473186     1.0   0.0 -0.056885      0.000000       0.000000   \n",
       "...          ...     ...   ...       ...           ...            ...   \n",
       "1647  242.523021     1.0   0.0 -0.112825      0.000000       0.000000   \n",
       "1648  196.818101     1.0   0.0 -0.042893      0.000000       0.000000   \n",
       "1649  220.979871     1.0   0.0 -0.158900      0.000000       0.000000   \n",
       "1650  227.549967     0.0   0.0  0.017960      0.000000       0.000000   \n",
       "1651  254.889034     0.0   0.0 -0.121783      0.000048       0.475851   \n",
       "\n",
       "          pi_a                                           geometry  \n",
       "0     0.126267  POLYGON ((120.61553 17.62360, 120.61870 17.623...  \n",
       "1     0.451233  POLYGON ((120.90749 17.41933, 120.92464 17.413...  \n",
       "2     0.298767  POLYGON ((120.74548 17.57671, 120.75101 17.575...  \n",
       "3     0.467700  POLYGON ((120.82477 17.41721, 120.78956 17.416...  \n",
       "4     0.331700  POLYGON ((120.98141 17.50534, 120.98367 17.504...  \n",
       "...        ...                                                ...  \n",
       "1647  0.489067  MULTIPOLYGON (((122.54519 7.68240, 122.54472 7...  \n",
       "1648  0.440700  MULTIPOLYGON (((122.80055 7.73917, 122.80083 7...  \n",
       "1649  0.606933  MULTIPOLYGON (((122.91917 7.34111, 122.91861 7...  \n",
       "1650  0.414100  POLYGON ((122.68086 7.88831, 122.68091 7.88682...  \n",
       "1651  0.559233  MULTIPOLYGON (((122.41989 7.49679, 122.42042 7...  \n",
       "\n",
       "[1613 rows x 64 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## retaining only the matched municipalities\n",
    "\n",
    "PHmerge = PHmerge[PHmerge.classification == 'Community']\n",
    "PHmerge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before I can make the map, I need to convert my merged data set into a geopanda data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index_left</th>\n",
       "      <th>province</th>\n",
       "      <th>muni</th>\n",
       "      <th>PDPpercent</th>\n",
       "      <th>region</th>\n",
       "      <th>region_code</th>\n",
       "      <th>province_code</th>\n",
       "      <th>municipality_city_code</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>lon_a_5</th>\n",
       "      <th>lat_a_5</th>\n",
       "      <th>d_a_5</th>\n",
       "      <th>PDPran</th>\n",
       "      <th>FPDP</th>\n",
       "      <th>pi_g</th>\n",
       "      <th>fatalities_pc</th>\n",
       "      <th>fatalities_p_c</th>\n",
       "      <th>pi_a</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BANGUED</td>\n",
       "      <td>0.101855</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140101000</td>\n",
       "      <td>120.600914</td>\n",
       "      <td>17.611204</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>341.874815</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-0.386866</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>1.245770</td>\n",
       "      <td>0.126267</td>\n",
       "      <td>POLYGON ((120.61553 17.62360, 120.61870 17.623...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BOLINEY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140102000</td>\n",
       "      <td>120.864738</td>\n",
       "      <td>17.378698</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>313.868504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.627300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.451233</td>\n",
       "      <td>POLYGON ((120.90749 17.41933, 120.92464 17.413...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCAY</td>\n",
       "      <td>0.053107</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140103000</td>\n",
       "      <td>120.719849</td>\n",
       "      <td>17.523537</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>330.886511</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.562465</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.584283</td>\n",
       "      <td>0.298767</td>\n",
       "      <td>POLYGON ((120.74548 17.57671, 120.75101 17.575...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>BUCLOC</td>\n",
       "      <td>0.047902</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140104000</td>\n",
       "      <td>120.848061</td>\n",
       "      <td>17.436989</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>320.394546</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.727650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.467700</td>\n",
       "      <td>POLYGON ((120.82477 17.41721, 120.78956 17.416...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>ABRA</td>\n",
       "      <td>DAGUIOMAN</td>\n",
       "      <td>0.007278</td>\n",
       "      <td>Cordillera Administrative Region (CAR)</td>\n",
       "      <td>PH140000000</td>\n",
       "      <td>PH140100000</td>\n",
       "      <td>PH140105000</td>\n",
       "      <td>120.952278</td>\n",
       "      <td>17.449829</td>\n",
       "      <td>...</td>\n",
       "      <td>120.991997</td>\n",
       "      <td>14.545</td>\n",
       "      <td>321.473186</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.056885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.331700</td>\n",
       "      <td>POLYGON ((120.98141 17.50534, 120.98367 17.504...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>1610.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>ROSELLER LIM</td>\n",
       "      <td>0.197443</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098312000</td>\n",
       "      <td>122.430687</td>\n",
       "      <td>7.702022</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>242.523021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.112825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.489067</td>\n",
       "      <td>MULTIPOLYGON (((122.54519 7.68240, 122.54472 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1648</th>\n",
       "      <td>1611.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>SIAY</td>\n",
       "      <td>0.260228</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098313000</td>\n",
       "      <td>122.876854</td>\n",
       "      <td>7.734716</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>196.818101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.042893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.440700</td>\n",
       "      <td>MULTIPOLYGON (((122.80055 7.73917, 122.80083 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>1612.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TALUSAN</td>\n",
       "      <td>0.178658</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098314000</td>\n",
       "      <td>122.884216</td>\n",
       "      <td>7.334711</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>220.979871</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.158900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606933</td>\n",
       "      <td>MULTIPOLYGON (((122.91917 7.34111, 122.91861 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>1613.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TITAY</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098315000</td>\n",
       "      <td>122.517639</td>\n",
       "      <td>7.845210</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>227.549967</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.414100</td>\n",
       "      <td>POLYGON ((122.68086 7.88831, 122.68091 7.88682...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1651</th>\n",
       "      <td>1614.0</td>\n",
       "      <td>ZAMBOANGA SIBUGAY</td>\n",
       "      <td>TUNGAWAN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Region IX (Zamboanga Peninsula)</td>\n",
       "      <td>PH090000000</td>\n",
       "      <td>PH098300000</td>\n",
       "      <td>PH098316000</td>\n",
       "      <td>122.370041</td>\n",
       "      <td>7.568430</td>\n",
       "      <td>...</td>\n",
       "      <td>124.442001</td>\n",
       "      <td>8.592</td>\n",
       "      <td>254.889034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.121783</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.475851</td>\n",
       "      <td>0.559233</td>\n",
       "      <td>MULTIPOLYGON (((122.41989 7.49679, 122.42042 7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1613 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index_left           province          muni  PDPpercent  \\\n",
       "0            0.0               ABRA       BANGUED    0.101855   \n",
       "1            1.0               ABRA       BOLINEY    0.000000   \n",
       "2            2.0               ABRA         BUCAY    0.053107   \n",
       "3            3.0               ABRA        BUCLOC    0.047902   \n",
       "4            4.0               ABRA     DAGUIOMAN    0.007278   \n",
       "...          ...                ...           ...         ...   \n",
       "1647      1610.0  ZAMBOANGA SIBUGAY  ROSELLER LIM    0.197443   \n",
       "1648      1611.0  ZAMBOANGA SIBUGAY          SIAY    0.260228   \n",
       "1649      1612.0  ZAMBOANGA SIBUGAY       TALUSAN    0.178658   \n",
       "1650      1613.0  ZAMBOANGA SIBUGAY         TITAY    0.000000   \n",
       "1651      1614.0  ZAMBOANGA SIBUGAY      TUNGAWAN    0.000000   \n",
       "\n",
       "                                      region  region_code province_code  \\\n",
       "0     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "1     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "2     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "3     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "4     Cordillera Administrative Region (CAR)  PH140000000   PH140100000   \n",
       "...                                      ...          ...           ...   \n",
       "1647         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1648         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1649         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1650         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "1651         Region IX (Zamboanga Peninsula)  PH090000000   PH098300000   \n",
       "\n",
       "     municipality_city_code   longitude   latitude  ...     lon_a_5  lat_a_5  \\\n",
       "0               PH140101000  120.600914  17.611204  ...  120.991997   14.545   \n",
       "1               PH140102000  120.864738  17.378698  ...  120.991997   14.545   \n",
       "2               PH140103000  120.719849  17.523537  ...  120.991997   14.545   \n",
       "3               PH140104000  120.848061  17.436989  ...  120.991997   14.545   \n",
       "4               PH140105000  120.952278  17.449829  ...  120.991997   14.545   \n",
       "...                     ...         ...        ...  ...         ...      ...   \n",
       "1647            PH098312000  122.430687   7.702022  ...  124.442001    8.592   \n",
       "1648            PH098313000  122.876854   7.734716  ...  124.442001    8.592   \n",
       "1649            PH098314000  122.884216   7.334711  ...  124.442001    8.592   \n",
       "1650            PH098315000  122.517639   7.845210  ...  124.442001    8.592   \n",
       "1651            PH098316000  122.370041   7.568430  ...  124.442001    8.592   \n",
       "\n",
       "           d_a_5  PDPran  FPDP      pi_g fatalities_pc fatalities_p_c  \\\n",
       "0     341.874815     1.0   6.0 -0.386866      0.000125       1.245770   \n",
       "1     313.868504     0.0   0.0 -0.627300      0.000000       0.000000   \n",
       "2     330.886511     1.0   1.0 -0.562465      0.000058       0.584283   \n",
       "3     320.394546     1.0   0.0 -0.727650      0.000000       0.000000   \n",
       "4     321.473186     1.0   0.0 -0.056885      0.000000       0.000000   \n",
       "...          ...     ...   ...       ...           ...            ...   \n",
       "1647  242.523021     1.0   0.0 -0.112825      0.000000       0.000000   \n",
       "1648  196.818101     1.0   0.0 -0.042893      0.000000       0.000000   \n",
       "1649  220.979871     1.0   0.0 -0.158900      0.000000       0.000000   \n",
       "1650  227.549967     0.0   0.0  0.017960      0.000000       0.000000   \n",
       "1651  254.889034     0.0   0.0 -0.121783      0.000048       0.475851   \n",
       "\n",
       "          pi_a                                           geometry  \n",
       "0     0.126267  POLYGON ((120.61553 17.62360, 120.61870 17.623...  \n",
       "1     0.451233  POLYGON ((120.90749 17.41933, 120.92464 17.413...  \n",
       "2     0.298767  POLYGON ((120.74548 17.57671, 120.75101 17.575...  \n",
       "3     0.467700  POLYGON ((120.82477 17.41721, 120.78956 17.416...  \n",
       "4     0.331700  POLYGON ((120.98141 17.50534, 120.98367 17.504...  \n",
       "...        ...                                                ...  \n",
       "1647  0.489067  MULTIPOLYGON (((122.54519 7.68240, 122.54472 7...  \n",
       "1648  0.440700  MULTIPOLYGON (((122.80055 7.73917, 122.80083 7...  \n",
       "1649  0.606933  MULTIPOLYGON (((122.91917 7.34111, 122.91861 7...  \n",
       "1650  0.414100  POLYGON ((122.68086 7.88831, 122.68091 7.88682...  \n",
       "1651  0.559233  MULTIPOLYGON (((122.41989 7.49679, 122.42042 7...  \n",
       "\n",
       "[1613 rows x 64 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PHmerge = gpd.GeoDataFrame(PHmerge, geometry=\"geometry\")\n",
    "PHmerge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, I graph the vote share in the Philippines by municipality in the 2019 Mayoral elections. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff67c586910>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##map for PH\n",
    "\n",
    "fig, gax = plt.subplots(figsize = (10,8))\n",
    "#plot the Philippines!\n",
    "world.query(\"name=='Philippines'\").plot(ax=gax, edgecolor='black',color='white')\n",
    "\n",
    "\n",
    "#plot the muicipalities\n",
    "PHmerge.plot(ax=gax, column=\"PDPpercent\", legend=True,cmap='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above results show that most municipalities don't have majority support for Duterte's party's mayoral candidates. The important thing though is that there is lots of variation in the municipality's vote shares for Duterte, which is a good thing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I also try to graph the fatalities for the Philippines. I should note that most municipalities that observed at least one fatality have a relatively small count of fatalities(the mean of the fatalities variable in my data set is around 3). To scale the fatalities better on the map, I decide to use the log of fatalities instead of the count. Since the range in the count of fatalities is quite large(it goes from 0 to 428), some municipalities look as though they had no fatalities because of the way cmap scales the colouring. In my thesis, I include specifications where I use the count of fataltiies, and others where I include the log of fatalities.\n",
    "\n",
    "Note: I chose to use a heat map for this visualisation since this is the same type of colour scheme Blanco(2012) uses for their homecide rate visualisations in their appendix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/pandas/core/series.py:679: RuntimeWarning:\n",
      "\n",
      "divide by zero encountered in log\n",
      "\n"
     ]
    }
   ],
   "source": [
    "PHmerge[\"log_fatalities\"]=np.log(PHmerge[\"fatalities\"]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1.791759\n",
       "1       0.000000\n",
       "2       0.000000\n",
       "3       0.000000\n",
       "4       0.000000\n",
       "          ...   \n",
       "1647    0.000000\n",
       "1648    0.000000\n",
       "1649    0.000000\n",
       "1650    0.000000\n",
       "1651    0.693147\n",
       "Name: log_fatalities, Length: 1613, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cleaning the log fatalities variable\n",
    "PHmerge[\"log_fatalities\"] = np.nan_to_num(PHmerge[\"log_fatalities\"], copy=True, nan=0.0, posinf=None, neginf=0)\n",
    "PHmerge[\"log_fatalities\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff608181550>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize = (10,8))\n",
    "#plot the Philippines!\n",
    "world.query(\"name=='Philippines'\").plot(ax=gax, edgecolor='black',color='white')\n",
    "\n",
    "\n",
    "#plot the muicipalities\n",
    "PHmerge.plot(ax=gax, column=\"log_fatalities\", legend=True,cmap='hot')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map is reassuring since there is a lot of geographic variation in the fatalities. If the fatalities were concerntrated in one specific region that would mean that I might have to limit my analysis to that region, which would severly limit my observations. Since the fatalities are relatively spread out throughout the country, I am able to make use of all the municipalities in my data set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, here is the map where I use the count of fatalities instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff6083d7490>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gax = plt.subplots(figsize = (10,8))\n",
    "#plot the Philippines!\n",
    "world.query(\"name=='Philippines'\").plot(ax=gax, edgecolor='black',color='white')\n",
    "\n",
    "\n",
    "#plot the muicipalities\n",
    "PHmerge.plot(ax=gax, column=\"fatalities\", legend=True,cmap='hot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map is less assuring, but again, the mean of the fatalities variable is right around 3, which means most municipalities won't show up in this colour scaling. Only the outliers show up in this colour scaling, and even then the outliers are still spread throughout the country, which makes me confident that there isn't one regional fixed effect driving the fatalities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I make an interactive graph!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error() {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < css_urls.length; i++) {\n",
       "      var url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error;\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
       "  var css_urls = [];\n",
       "  \n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"a00daad6-c257-4b25-ad89-8b45e3d29340\" data-root-id=\"1006\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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