Data preprocessing v2.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pyreadstat import pyreadstat\n",
    "from collections import defaultdict, OrderedDict\n",
    "import os\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import json\n",
    "import re\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, LineString\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('D:/bachelors/datasets/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "communes_2018 = gpd.read_file('../qgis/base/Netherlands_communes.gpkg')\n",
    "total_communes_centoids = gpd.read_file('GeementenShapes/Centroids/total_centroids.gpkg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>statcode</th>\n",
       "      <th>statnaam</th>\n",
       "      <th>jrstatcode</th>\n",
       "      <th>rubriek</th>\n",
       "      <th>layer</th>\n",
       "      <th>path</th>\n",
       "      <th>geometry</th>\n",
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       "  <tbody>\n",
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       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
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       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98301 52.95103)</td>\n",
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       "      <td>Heerenveen</td>\n",
       "      <td>2009GM0074</td>\n",
       "      <td>gemeente</td>\n",
       "      <td>centroid_2009</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98296 52.95106)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2785</td>\n",
       "      <td>cbs_gemeente_2010_gegeneraliseerd.112858</td>\n",
       "      <td>GM0074</td>\n",
       "      <td>Heerenveen</td>\n",
       "      <td>2010GM0074</td>\n",
       "      <td>gemeente</td>\n",
       "      <td>centroid_2010</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98300 52.95104)</td>\n",
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       "      <td>GM0074</td>\n",
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       "      <td>gemeente</td>\n",
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       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98296 52.95106)</td>\n",
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       "      <td>gemeente</td>\n",
       "      <td>centroid_2012</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98297 52.95105)</td>\n",
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       "      <td>GM0074</td>\n",
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       "      <td>gemeente</td>\n",
       "      <td>centroid_2013</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98296 52.95106)</td>\n",
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       "      <td>gemeente</td>\n",
       "      <td>centroid_2015</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98296 52.95106)</td>\n",
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       "      <td>GM0074</td>\n",
       "      <td>Heerenveen</td>\n",
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       "      <td>gemeente</td>\n",
       "      <td>centroid_2016</td>\n",
       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98297 52.95105)</td>\n",
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       "      <td>5619</td>\n",
       "      <td>cbs_gemeente_2017_gegeneraliseerd.227632</td>\n",
       "      <td>GM0074</td>\n",
       "      <td>Heerenveen</td>\n",
       "      <td>2017GM0074</td>\n",
       "      <td>gemeente</td>\n",
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       "      <td>D:\\bachelors\\datasets\\GeementenShapes\\Centroid...</td>\n",
       "      <td>POINT (5.98296 52.95106)</td>\n",
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      "text/plain": [
       "                                            id statcode    statnaam  \\\n",
       "40     cbs_gemeente_2004_gegeneraliseerd.23106   GM0074  Heerenveen   \n",
       "555    cbs_gemeente_2005_gegeneraliseerd.52227   GM0074  Heerenveen   \n",
       "989    cbs_gemeente_2006_gegeneraliseerd.52694   GM0074  Heerenveen   \n",
       "1471   cbs_gemeente_2007_gegeneraliseerd.82129   GM0074  Heerenveen   \n",
       "1893   cbs_gemeente_2008_gegeneraliseerd.82572   GM0074  Heerenveen   \n",
       "2349  cbs_gemeente_2009_gegeneraliseerd.112419   GM0074  Heerenveen   \n",
       "2785  cbs_gemeente_2010_gegeneraliseerd.112858   GM0074  Heerenveen   \n",
       "3209  cbs_gemeente_2011_gegeneraliseerd.143179   GM0074  Heerenveen   \n",
       "3625  cbs_gemeente_2012_gegeneraliseerd.143597   GM0074  Heerenveen   \n",
       "4069  cbs_gemeente_2013_gegeneraliseerd.174393   GM0074  Heerenveen   \n",
       "4448  cbs_gemeente_2014_gegeneraliseerd.174799   GM0074  Heerenveen   \n",
       "4874  cbs_gemeente_2015_gegeneraliseerd.206709   GM0074  Heerenveen   \n",
       "5229  cbs_gemeente_2016_gegeneraliseerd.207102   GM0074  Heerenveen   \n",
       "5619  cbs_gemeente_2017_gegeneraliseerd.227632   GM0074  Heerenveen   \n",
       "\n",
       "      jrstatcode   rubriek          layer  \\\n",
       "40    2004GM0074  gemeente  centroid_2004   \n",
       "555   2005GM0074  gemeente  centroid_2005   \n",
       "989   2006GM0074  gemeente  centroid_2006   \n",
       "1471  2007GM0074  gemeente  centroid_2007   \n",
       "1893  2008GM0074  gemeente  centroid_2008   \n",
       "2349  2009GM0074  gemeente  centroid_2009   \n",
       "2785  2010GM0074  gemeente  centroid_2010   \n",
       "3209  2011GM0074  gemeente  centroid_2011   \n",
       "3625  2012GM0074  gemeente  centroid_2012   \n",
       "4069  2013GM0074  gemeente  centroid_2013   \n",
       "4448  2014GM0074  gemeente  centroid_2014   \n",
       "4874  2015GM0074  gemeente  centroid_2015   \n",
       "5229  2016GM0074  gemeente  centroid_2016   \n",
       "5619  2017GM0074  gemeente  centroid_2017   \n",
       "\n",
       "                                                   path  \\\n",
       "40    D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "555   D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "989   D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "1471  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "1893  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "2349  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "2785  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "3209  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "3625  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "4069  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "4448  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "4874  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "5229  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "5619  D:\\bachelors\\datasets\\GeementenShapes\\Centroid...   \n",
       "\n",
       "                      geometry  \n",
       "40    POINT (5.98301 52.95102)  \n",
       "555   POINT (5.98301 52.95103)  \n",
       "989   POINT (5.98301 52.95103)  \n",
       "1471  POINT (5.98301 52.95103)  \n",
       "1893  POINT (5.98301 52.95103)  \n",
       "2349  POINT (5.98296 52.95106)  \n",
       "2785  POINT (5.98300 52.95104)  \n",
       "3209  POINT (5.98296 52.95106)  \n",
       "3625  POINT (5.98297 52.95105)  \n",
       "4069  POINT (5.98296 52.95106)  \n",
       "4448  POINT (5.98296 52.95106)  \n",
       "4874  POINT (5.98296 52.95106)  \n",
       "5229  POINT (5.98297 52.95105)  \n",
       "5619  POINT (5.98296 52.95106)  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_communes_centoids[total_communes_centoids.statnaam.str.contains('Heeren')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "communes_mapper = gpd.sjoin(\n",
    "    total_communes_centoids,\n",
    "    communes_2018, op = 'intersects',\n",
    "    how = 'left'\n",
    ")[\n",
    "    ['statnaam', 'NAME']\n",
    "].drop_duplicates(subset = ['statnaam']).set_index('statnaam').to_dict()['NAME']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "communes = set()\n",
    "\n",
    "for data in iterator:\n",
    "    valuesMap = values_map[data]\n",
    "    l_dir = iterator[data]['list_dir']\n",
    "    root_path = iterator[data]['path']\n",
    "    for l in tqdm(l_dir):\n",
    "        df, meta = pyreadstat.read_sav(root_path + l)\n",
    "        df.columns = [i.lower() for i in df.columns]\n",
    "\n",
    "        new_labels = {k.lower() : v for k, v in meta.variable_value_labels.items()}\n",
    "        \n",
    "        df = df[\n",
    "            set(colnames_map.keys()).intersection(set(df.columns))\n",
    "        ].dropna(subset = ['vertgem', 'aankgem'])\n",
    "\n",
    "        \n",
    "        df = df.apply(\n",
    "            lambda column: column.map(new_labels['wogem']) if column.name in ['wogem', 'vertgem', 'aankgem'] else column\n",
    "        )\n",
    "        communes = communes.union(set(df['wogem']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(pd.Series(communes_mapper).rename('communes_from_geo')).sort_values('communes_from_geo').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.merge(\n",
    "    pd.DataFrame(pd.Series(list(communes)).rename('communes_from_data')),\n",
    "    left_on = 'index',\n",
    "    right_on = 'communes_from_data',\n",
    "    how = 'outer',\n",
    "    left_index = True\n",
    ").to_excel('FINAL COMMUNES MERGE_try.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "communes_mapper = pd.read_excel('FINAL COMMUNES MERGE_EDITED.xlsx').set_index('index')['communes_from_geo'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_MON = 'D:/bachelors/datasets/MobNed_dat/'\n",
    "path_OViN = 'D:/bachelors/datasets/OViN_dat/'\n",
    "# path_OViN = 'D:/bachelors/datasets/ODiN_dat/'\n",
    "\n",
    "l_dir_MON = os.listdir(path_MON)\n",
    "l_dir_OViN = os.listdir(path_OViN)\n",
    "\n",
    "\n",
    "iterator = {\n",
    "    'MON' : {'path' : path_MON,\n",
    "             'list_dir' : l_dir_MON},\n",
    "    'OViN' : {'path' : path_OViN,\n",
    "              'list_dir' : l_dir_OViN}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('Variables/mappers/values_ru_map_v2.json') as infile:\n",
    "    values_map = json.load(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames_map = {    \n",
    "    \"wogem\": \"geo_commune\",\n",
    "    \"vertgem\": \"geo_departure\",\n",
    "    \"aankgem\": \"geo_arrival\",\n",
    "    \"motief\": \"motiv_motivation\",\n",
    "    \"motief\": \"motiv_motivation\",\n",
    "    \"motiefv\": \"motiv_motivation\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "motivation_map = {   \n",
    "    'OViN': {\n",
    "        1 : 'Работа',\n",
    "        2 : 'Рабочая встреча',\n",
    "        3 : 'Транспортная сфера работы',\n",
    "        4 : 'Перевозка людей',\n",
    "        5 : 'Перевозка товаров',\n",
    "        6 : 'Образование',\n",
    "        7 : 'Покупки',\n",
    "        8 : 'Посещение друзей и родственников',\n",
    "        9 : 'Туризм и рекреация',\n",
    "        10 : 'Спорт',\n",
    "        11 : 'Другие виды досуга',\n",
    "        12 : 'Услуги',\n",
    "        13 : 'Другое'\n",
    "    },\n",
    "    'MON' : {\n",
    "        0 : 'Не указан',\n",
    "        2 : 'Работа',\n",
    "        3 : 'Рабочая встреча',\n",
    "        4 : 'Транспортная сфера работы',\n",
    "        5 : 'Перевозка людей',\n",
    "        6 : 'Перевозка товаров',\n",
    "        7 : 'Образование',\n",
    "        8 : 'Покупки',\n",
    "        9 : 'Посещение друзей и родственников',\n",
    "        10 : 'Туризм и рекреация',\n",
    "        11 : 'Спорт',\n",
    "        12 : 'Другие виды досуга',\n",
    "        13 : 'Услуги',\n",
    "        14 : 'Совместно с другим человеком',\n",
    "        15 : 'Другое'\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "model_id": "54559ecb0c5c4fb0a5d9e6b7bb0a6ed7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7919e6b8b5244b2d86374c09f79e2e62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=8), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for data in iterator:\n",
    "    valuesMap = values_map[data]\n",
    "    l_dir = iterator[data]['list_dir']\n",
    "    root_path = iterator[data]['path']\n",
    "    for l in tqdm(l_dir):\n",
    "        df, meta = pyreadstat.read_sav(root_path + l)\n",
    "        df.columns = [i.lower() for i in df.columns]\n",
    "        \n",
    "        new_labels = {k.lower() : v for k, v in meta.variable_value_labels.items()}\n",
    "        \n",
    "\n",
    "        df = df[\n",
    "            set(colnames_map.keys()).intersection(set(df.columns))\n",
    "        ].dropna(subset = ['vertgem', 'aankgem'])\n",
    "\n",
    "            \n",
    "        \n",
    "        df = df.apply(\n",
    "            lambda column: column.map(new_labels['wogem']) if column.name in ['wogem', 'vertgem', 'aankgem'] else column\n",
    "        ).apply(\n",
    "            lambda column: column.map(communes_mapper) if column.name in ['wogem', 'vertgem', 'aankgem'] else column\n",
    "        )\n",
    "        df.dropna(subset = ['vertgem', 'aankgem'], inplace = True)\n",
    "        \n",
    "        df.rename(columns = colnames_map, inplace = True)\n",
    "        df['motiv_motivation'] = df['motiv_motivation'].astype(int).map(motivation_map[data])\n",
    "        df['uid_person'] = pd.Series([i for i in range(len(df))])        \n",
    "        \n",
    "        \n",
    "        year = re.findall('\\d+', l)[0]\n",
    "        df.to_csv(f'translated_data_v4/data_{year}.csv', index = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:/bachelors/datasets/OViN_dat/'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df, meta = pyreadstat.read_sav('D:/bachelors/datasets/MobNed_dat/Databestand MON 2009.sav')\n",
    "df.columns = [i.lower() for i in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_labels = {k.lower() : v for k, v in meta.variable_value_labels.items()}\n",
    "        \n",
    "\n",
    "df = df[\n",
    "    set(colnames_map.keys()).intersection(set(df.columns))\n",
    "].dropna(subset = ['vertgem', 'aankgem'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['wogem']).difference(df['aankgem'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "(\"'<' not supported between instances of 'float' and 'str'\", 'occurred at index 132')",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-109-8ea42a4a6d92>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'uid_person'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'geo_departure'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'geo_arrival'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minplace\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_corrMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocal_1\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlocal_2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'flow'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'flow'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'flow'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-88-f2578e1cdd6f>\u001b[0m in \u001b[0;36mget_corrMatrix\u001b[1;34m(df_current)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_corrMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_current\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m\"\"\"Get correspondence matrix from datatable\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdf_current\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_current\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_departure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_arrival\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_current\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'uid_person'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, result_type, args, **kwds)\u001b[0m\n\u001b[0;32m   6911\u001b[0m             \u001b[0mkwds\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6912\u001b[0m         )\n\u001b[1;32m-> 6913\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6915\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapplymap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mget_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    184\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_empty_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    291\u001b[0m         \u001b[1;31m# compute the result using the series generator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 292\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    293\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;31m# wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    319\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    320\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 321\u001b[1;33m                     \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    322\u001b[0m                     \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    323\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-88-f2578e1cdd6f>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_corrMatrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_current\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;34m\"\"\"Get correspondence matrix from datatable\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdf_current\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_current\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_departure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_arrival\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_current\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'uid_person'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: (\"'<' not supported between instances of 'float' and 'str'\", 'occurred at index 132')"
     ]
    }
   ],
   "source": [
    "for year in range(2010, 2018):\n",
    "    try:\n",
    "        df = pd.read_spss(f'OViN_dat/OViN{year}_Databestand.sav')\n",
    "    except:\n",
    "        df = pd.read_spss(f'OViN_dat/OViN{year}_Databestand_revisie.sav')\n",
    "    df.columns = [i.lower() for i in df.columns]\n",
    "    \n",
    "    df.rename(columns = {'vertgem' : 'geo_departure', 'aankgem' : 'geo_arrival'}, inplace = True)\n",
    "    df['uid_person'] = pd.Series([i for i in range(len(df))])\n",
    "    df.dropna(subset = ['geo_departure', 'geo_arrival'], inplace = True)\n",
    "    matrix = get_corrMatrix(df)\n",
    "    matrix = matrix[matrix.local_1 != matrix.local_2]\n",
    "    matrix['flow'] = matrix['flow'] / matrix['flow'].sum()\n",
    "    \n",
    "    coords = gpd.read_file(f'GeementenShapes/communes_{year}.shp').to_crs({'init' : 'epsg:4326'})\n",
    "    coords.set_index('statnaam', inplace = True)\n",
    "    coords['geometry'] = coords['geometry'].centroid\n",
    "    coords['coords'] = coords['geometry'].apply(lambda x: [x.x, x.y]) \n",
    "    dict_coords = coords['coords'].to_dict()\n",
    "    \n",
    "    matrix['l1_coords'] = matrix['local_1'].map(dict_coords)\n",
    "    matrix['l2_coords'] = matrix['local_2'].map(dict_coords)\n",
    "\n",
    "    matrix.dropna(subset = ['l1_coords', 'l2_coords'], inplace = True)\n",
    "    \n",
    "    gdf_matrix = gpd.GeoDataFrame(\n",
    "        matrix,\n",
    "        crs = {'init' : 'epsg:4326'},\n",
    "        geometry = [LineString([c1, c2]) for c1, c2  in zip(matrix.l1_coords, matrix.l2_coords)]\n",
    "    )\n",
    "    gdf_matrix.drop(columns = ['l1_coords', 'l2_coords'], inplace = True)\n",
    "    \n",
    "    gdf_matrix.to_file(f'REMAKE/graphs/{year}_graph.gpkg', driver = 'GPKG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_spss(f'OViN_dat/OViN2011_Databestand_revisie.sav')\n",
    "df.columns = [i.lower() for i in df.columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'vertgem' : 'geo_departure', 'aankgem' : 'geo_arrival'}, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              Hilversum\n",
       "1              Hilversum\n",
       "2              Hilversum\n",
       "3                  Ommen\n",
       "4                 Zwolle\n",
       "               ...      \n",
       "134195    Smallingerland\n",
       "134196               NaN\n",
       "134197       Menterwolde\n",
       "134198       Menterwolde\n",
       "134199               NaN\n",
       "Name: geo_departure, Length: 134200, dtype: category\n",
       "Categories (419, object): ['s-Gravenhage, 's-Hertogenbosch, Aa en Hunze, Aalburg, ..., Zwartewaterland, Zwijndrecht, Zwolle, het Bildt]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['geo_departure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              0\n",
       "1              1\n",
       "2              2\n",
       "3              3\n",
       "4              4\n",
       "           ...  \n",
       "221981    221981\n",
       "221982    221982\n",
       "221983    221983\n",
       "221984    221984\n",
       "221985    221985\n",
       "Name: uid_person, Length: 221986, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['uid_person']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>jaar</th>\n",
       "      <th>maand</th>\n",
       "      <th>dag</th>\n",
       "      <th>huishoud</th>\n",
       "      <th>persoon</th>\n",
       "      <th>verpl</th>\n",
       "      <th>rit</th>\n",
       "      <th>hhid</th>\n",
       "      <th>persid</th>\n",
       "      <th>verplid</th>\n",
       "      <th>...</th>\n",
       "      <th>rdoel</th>\n",
       "      <th>rwachten</th>\n",
       "      <th>freqgebr</th>\n",
       "      <th>groepgr</th>\n",
       "      <th>rpzoek</th>\n",
       "      <th>rpark</th>\n",
       "      <th>rstation</th>\n",
       "      <th>kkrtsoor</th>\n",
       "      <th>uid_person</th>\n",
       "      <th>route</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>3e dag van de maand</td>\n",
       "      <td>Nieuw huishouden</td>\n",
       "      <td>Nieuwe persoon</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>4142114.0</td>\n",
       "      <td>414211401.0</td>\n",
       "      <td>4.142114e+10</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>0</td>\n",
       "      <td>(Valkenswaard, Valkenswaard)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>3e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>4142114.0</td>\n",
       "      <td>414211401.0</td>\n",
       "      <td>4.142114e+10</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>1</td>\n",
       "      <td>(Valkenswaard, Valkenswaard)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>3e dag van de maand</td>\n",
       "      <td>Nieuw huishouden</td>\n",
       "      <td>Nieuwe persoon</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>4144011.0</td>\n",
       "      <td>414401101.0</td>\n",
       "      <td>4.144011e+10</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>2</td>\n",
       "      <td>(Rotterdam, Rotterdam)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>3e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>4144011.0</td>\n",
       "      <td>414401101.0</td>\n",
       "      <td>4.144011e+10</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>3</td>\n",
       "      <td>(Rotterdam, Rotterdam)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>3e dag van de maand</td>\n",
       "      <td>Nieuw huishouden</td>\n",
       "      <td>Nieuwe persoon</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>4144034.0</td>\n",
       "      <td>414403401.0</td>\n",
       "      <td>4.144034e+10</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>4</td>\n",
       "      <td>(Amersfoort, Zeewolde)</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221981</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>2e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>5753175.0</td>\n",
       "      <td>575317502.0</td>\n",
       "      <td>5.753175e+10</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>221981</td>\n",
       "      <td>(Dongeradeel, Steenwijkerland)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221982</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>9e dag van de maand</td>\n",
       "      <td>Nieuw huishouden</td>\n",
       "      <td>Nieuwe persoon</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>5753177.0</td>\n",
       "      <td>575317701.0</td>\n",
       "      <td>5.753177e+10</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>221982</td>\n",
       "      <td>(Zederik, Zederik)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221983</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>9e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>5753177.0</td>\n",
       "      <td>575317701.0</td>\n",
       "      <td>5.753177e+10</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>221983</td>\n",
       "      <td>(Zederik, Zederik)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221984</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>9e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe persoon</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>5753177.0</td>\n",
       "      <td>575317702.0</td>\n",
       "      <td>5.753177e+10</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>221984</td>\n",
       "      <td>(Zederik, Zederik)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221985</td>\n",
       "      <td>2005</td>\n",
       "      <td>Januari</td>\n",
       "      <td>9e dag van de maand</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nvt</td>\n",
       "      <td>Nieuwe verplaatsing</td>\n",
       "      <td>Nieuwe rit</td>\n",
       "      <td>5753177.0</td>\n",
       "      <td>575317702.0</td>\n",
       "      <td>5.753177e+10</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>221985</td>\n",
       "      <td>(Zederik, Zederik)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221986 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        jaar    maand                  dag          huishoud         persoon  \\\n",
       "0       2005  Januari  3e dag van de maand  Nieuw huishouden  Nieuwe persoon   \n",
       "1       2005  Januari  3e dag van de maand               Nvt             Nvt   \n",
       "2       2005  Januari  3e dag van de maand  Nieuw huishouden  Nieuwe persoon   \n",
       "3       2005  Januari  3e dag van de maand               Nvt             Nvt   \n",
       "4       2005  Januari  3e dag van de maand  Nieuw huishouden  Nieuwe persoon   \n",
       "...      ...      ...                  ...               ...             ...   \n",
       "221981  2005  Januari  2e dag van de maand               Nvt             Nvt   \n",
       "221982  2005  Januari  9e dag van de maand  Nieuw huishouden  Nieuwe persoon   \n",
       "221983  2005  Januari  9e dag van de maand               Nvt             Nvt   \n",
       "221984  2005  Januari  9e dag van de maand               Nvt  Nieuwe persoon   \n",
       "221985  2005  Januari  9e dag van de maand               Nvt             Nvt   \n",
       "\n",
       "                      verpl         rit       hhid       persid       verplid  \\\n",
       "0       Nieuwe verplaatsing  Nieuwe rit  4142114.0  414211401.0  4.142114e+10   \n",
       "1       Nieuwe verplaatsing  Nieuwe rit  4142114.0  414211401.0  4.142114e+10   \n",
       "2       Nieuwe verplaatsing  Nieuwe rit  4144011.0  414401101.0  4.144011e+10   \n",
       "3       Nieuwe verplaatsing  Nieuwe rit  4144011.0  414401101.0  4.144011e+10   \n",
       "4       Nieuwe verplaatsing  Nieuwe rit  4144034.0  414403401.0  4.144034e+10   \n",
       "...                     ...         ...        ...          ...           ...   \n",
       "221981  Nieuwe verplaatsing  Nieuwe rit  5753175.0  575317502.0  5.753175e+10   \n",
       "221982  Nieuwe verplaatsing  Nieuwe rit  5753177.0  575317701.0  5.753177e+10   \n",
       "221983  Nieuwe verplaatsing  Nieuwe rit  5753177.0  575317701.0  5.753177e+10   \n",
       "221984  Nieuwe verplaatsing  Nieuwe rit  5753177.0  575317702.0  5.753177e+10   \n",
       "221985  Nieuwe verplaatsing  Nieuwe rit  5753177.0  575317702.0  5.753177e+10   \n",
       "\n",
       "        ...  rdoel  rwachten  freqgebr  groepgr rpzoek rpark rstation  \\\n",
       "0       ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "1       ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "2       ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "3       ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "4       ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "...     ...    ...       ...       ...      ...    ...   ...      ...   \n",
       "221981  ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "221982  ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "221983  ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "221984  ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "221985  ...    NaN       NaN       NaN      NaN    NaN   NaN      NaN   \n",
       "\n",
       "       kkrtsoor uid_person                           route  \n",
       "0           NaN          0    (Valkenswaard, Valkenswaard)  \n",
       "1           NaN          1    (Valkenswaard, Valkenswaard)  \n",
       "2           NaN          2          (Rotterdam, Rotterdam)  \n",
       "3           NaN          3          (Rotterdam, Rotterdam)  \n",
       "4           NaN          4          (Amersfoort, Zeewolde)  \n",
       "...         ...        ...                             ...  \n",
       "221981      NaN     221981  (Dongeradeel, Steenwijkerland)  \n",
       "221982      NaN     221982              (Zederik, Zederik)  \n",
       "221983      NaN     221983              (Zederik, Zederik)  \n",
       "221984      NaN     221984              (Zederik, Zederik)  \n",
       "221985      NaN     221985              (Zederik, Zederik)  \n",
       "\n",
       "[221986 rows x 131 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['route'] = df.apply(lambda row: tuple(sorted([row.geo_departure, row.geo_arrival])), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['uid']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'float' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msafe_sort\u001b[1;34m(values, labels, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m    465\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m             \u001b[0msorter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m             \u001b[0mordered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-100-e027d4ab9b6f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'uid_person'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36mnunique\u001b[1;34m(self, dropna)\u001b[0m\n\u001b[0;32m   1127\u001b[0m             \u001b[0mNumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0munique\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0mwithin\u001b[0m \u001b[0meach\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m         \"\"\"\n\u001b[1;32m-> 1129\u001b[1;33m         \u001b[0mids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_internal_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36mgroup_info\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    294\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgroup_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[0mcomp_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobs_group_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m         \u001b[0mngroups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs_group_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m_get_compressed_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mlabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    395\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 397\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    398\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m_make_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    419\u001b[0m                 \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m                 \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m                 \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36mfactorize\u001b[1;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m         uniques, labels = safe_sort(\n\u001b[1;32m--> 679\u001b[1;33m             \u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0massume_unique\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    680\u001b[0m         )\n\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msafe_sort\u001b[1;34m(values, labels, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    469\u001b[0m             \u001b[1;31m# try this anyway\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 470\u001b[1;33m             \u001b[0mordered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msort_mixed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    471\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    472\u001b[0m     \u001b[1;31m# labels:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msort_mixed\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[1;31m# order ints before strings, safe in py3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m         \u001b[0mstr_pos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mnums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mstr_pos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m         \u001b[0mstrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr_pos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m    932\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'"
     ]
    }
   ],
   "source": [
    "matrix = df.dropna(subset = ['route']).groupby('route')['uid_person'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "route\n",
       "('s-Gravendeel, Oostflakkee)         6\n",
       "('s-Gravendeel, Oud-Beijerland)      3\n",
       "('s-Gravendeel, Papendrecht)         4\n",
       "('s-Gravendeel, Tilburg)             6\n",
       "('s-Gravenhage, 's-Gravenhage)     382\n",
       "                                  ... \n",
       "(Zevenaar, Zevenaar)                 6\n",
       "(Zoetermeer, Zoetermeer)             3\n",
       "(Zutphen, Zwolle)                    3\n",
       "(Zwartewaterland, Zwolle)           21\n",
       "(Zwolle, Zwolle)                    19\n",
       "Name: uid_person, Length: 1096, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_current['route'] = df_current.apply(lambda row: tuple(sorted([row.geo_departure, row.geo_arrival])), axis = 1)\n",
    "matrix = df_current.groupby('route')['uid_person'].nunique()\n",
    "matrix.index = pd.MultiIndex.from_tuples(matrix.index)\n",
    "matrix.index.set_names(['local_1', 'local_2'], inplace = True)\n",
    "matrix.rename('flow', inplace = True)\n",
    "#     matrix.reset_index(inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'float' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msafe_sort\u001b[1;34m(values, labels, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m    465\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 466\u001b[1;33m             \u001b[0msorter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margsort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    467\u001b[0m             \u001b[0mordered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-83-f17dd27e96be>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'route'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'uid_person'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36mnunique\u001b[1;34m(self, dropna)\u001b[0m\n\u001b[0;32m   1127\u001b[0m             \u001b[0mNumber\u001b[0m \u001b[0mof\u001b[0m \u001b[0munique\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0mwithin\u001b[0m \u001b[0meach\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1128\u001b[0m         \"\"\"\n\u001b[1;32m-> 1129\u001b[1;33m         \u001b[0mids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_internal_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36mgroup_info\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    294\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgroup_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m         \u001b[0mcomp_ids\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobs_group_ids\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m         \u001b[0mngroups\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobs_group_ids\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m_get_compressed_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\ops.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_compressed_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 312\u001b[1;33m         \u001b[0mall_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mping\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mping\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupings\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    313\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    314\u001b[0m             \u001b[0mgroup_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_group_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxnull\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mlabels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    395\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    396\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 397\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    398\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    399\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36m_make_labels\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    419\u001b[0m                 \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m                 \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfactorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m                 \u001b[0muniques\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    206\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\algorithms.py\u001b[0m in \u001b[0;36mfactorize\u001b[1;34m(values, sort, order, na_sentinel, size_hint)\u001b[0m\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m         uniques, labels = safe_sort(\n\u001b[1;32m--> 679\u001b[1;33m             \u001b[0muniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mna_sentinel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mna_sentinel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0massume_unique\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    680\u001b[0m         )\n\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msafe_sort\u001b[1;34m(values, labels, na_sentinel, assume_unique, verify)\u001b[0m\n\u001b[0;32m    468\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    469\u001b[0m             \u001b[1;31m# try this anyway\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 470\u001b[1;33m             \u001b[0mordered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msort_mixed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    471\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    472\u001b[0m     \u001b[1;31m# labels:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\sorting.py\u001b[0m in \u001b[0;36msort_mixed\u001b[1;34m(values)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[1;31m# order ints before strings, safe in py3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m         \u001b[0mstr_pos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mnums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[0mstr_pos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m         \u001b[0mstrs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr_pos\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnums\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m    932\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 934\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    935\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'float' and 'str'"
     ]
    }
   ],
   "source": [
    "df.dropna(subset = ['route']).groupby('route')['uid_person'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           (Valkenswaard, Valkenswaard)\n",
       "1           (Valkenswaard, Valkenswaard)\n",
       "2                 (Rotterdam, Rotterdam)\n",
       "3                 (Rotterdam, Rotterdam)\n",
       "4                 (Amersfoort, Zeewolde)\n",
       "                       ...              \n",
       "221981    (Dongeradeel, Steenwijkerland)\n",
       "221982                (Zederik, Zederik)\n",
       "221983                (Zederik, Zederik)\n",
       "221984                (Zederik, Zederik)\n",
       "221985                (Zederik, Zederik)\n",
       "Name: route, Length: 221986, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.route.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20d974b7908>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf_matrix.to_crs({'init' : 'epsg:3857'}).plot(column = 'flow', figsize = (5,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = get_corrMatrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = matrix[matrix.local_1 != matrix.local_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mtopn\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>local_1</th>\n",
       "      <th>local_2</th>\n",
       "      <th>flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Aa en Hunze</td>\n",
       "      <td>Amersfoort</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Aa en Hunze</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Aa en Hunze</td>\n",
       "      <td>Appingedam</td>\n",
       "      <td>0.000090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Aa en Hunze</td>\n",
       "      <td>Assen</td>\n",
       "      <td>0.001841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Aa en Hunze</td>\n",
       "      <td>Barneveld</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9260</td>\n",
       "      <td>Zwijndrecht</td>\n",
       "      <td>s-Gravendeel</td>\n",
       "      <td>0.000077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9261</td>\n",
       "      <td>Zwijndrecht</td>\n",
       "      <td>s-Gravenhage</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9263</td>\n",
       "      <td>Zwolle</td>\n",
       "      <td>s-Gravenhage</td>\n",
       "      <td>0.000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9266</td>\n",
       "      <td>s-Gravendeel</td>\n",
       "      <td>s-Gravenhage</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9268</td>\n",
       "      <td>s-Gravenhage</td>\n",
       "      <td>s-Hertogenbosch</td>\n",
       "      <td>0.000116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8787 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           local_1          local_2      flow\n",
       "1      Aa en Hunze       Amersfoort  0.000013\n",
       "2      Aa en Hunze        Amsterdam  0.000039\n",
       "3      Aa en Hunze       Appingedam  0.000090\n",
       "4      Aa en Hunze            Assen  0.001841\n",
       "5      Aa en Hunze        Barneveld  0.000064\n",
       "...            ...              ...       ...\n",
       "9260   Zwijndrecht     s-Gravendeel  0.000077\n",
       "9261   Zwijndrecht     s-Gravenhage  0.000116\n",
       "9263        Zwolle     s-Gravenhage  0.000026\n",
       "9266  s-Gravendeel     s-Gravenhage  0.000103\n",
       "9268  s-Gravenhage  s-Hertogenbosch  0.000116\n",
       "\n",
       "[8787 rows x 3 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "coords = gpd.read_file('GeementenShapes/communes_2004.shp').to_crs({'init' : 'epsg:4326'})\n",
    "coords.set_index('statnaam', inplace = True)\n",
    "coords['geometry'] = coords['geometry'].centroid\n",
    "coords['coords'] = coords['geometry'].apply(lambda x: [x.x, x.y]) \n",
    "dict_coords = coords['coords'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mtopn\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\mtopn\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\mtopn\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "matrix['l1_coords'] = matrix['local_1'].map(dict_coords)\n",
    "matrix['l2_coords'] = matrix['local_2'].map(dict_coords)\n",
    "\n",
    "matrix.dropna(subset = ['l1_coords', 'l2_coords'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf_matrix = gpd.GeoDataFrame(\n",
    "    matrix,\n",
    "    crs = {'init' : 'epsg:4326'},\n",
    "    geometry = [LineString([c1, c2]) for c1, c2  in zip(matrix.l1_coords, matrix.l2_coords)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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