Notebooks/Analysis_maps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "noble-biography",
   "metadata": {},
   "source": [
    "# **Place of birth analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dependent-employer",
   "metadata": {},
   "source": [
    "In this notebook, we will analyze the places of birth and sites of study by the realization of maps. It is another way to represent the data. We will present two ways to make it: one with the geographic coordinates and one with polygons. \n",
    "\n",
    "Documentation: \n",
    "* [Geographic information system](https://en.wikipedia.org/wiki/Geographic_information_system) (Wikipedia - en)\n",
    "* [Geoid](https://en.wikipedia.org/wiki/Geoid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "extraordinary-diversity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "# geopandas is a library used to work with geospatial data.\n",
    "# cf. https://geopandas.org/\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import mapclassify\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "satisfactory-following",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "import csv\n",
    "import ast\n",
    "import re\n",
    "\n",
    "from collections import Counter\n",
    "from operator import itemgetter\n",
    "\n",
    "import sqlite3 as sql\n",
    "import time\n",
    "\n",
    "from importlib import reload\n",
    "from shutil import copyfile\n",
    "\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "downtown-celebrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This library can make interactions like range sliders. \n",
    "# cf. https://ipywidgets.readthedocs.io/en/latest/index.html\n",
    "from ipywidgets import (\n",
    "    interact, IntSlider, Checkbox,\n",
    "    interactive, SelectMultiple,\n",
    "    SelectionRangeSlider, Layout,\n",
    "    jslink, fixed,\n",
    "    HTML, HBox)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "tough-album",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this library is used to achieve interactive maps b\n",
    "# documentation: https://ipyleaflet.readthedocs.io/en/latest/\n",
    "from ipyleaflet import (Map,\n",
    "                        Polygon,\n",
    "                        GeoData,\n",
    "                        Marker,\n",
    "                        basemap_to_tiles,\n",
    "                        basemaps,\n",
    "                        CircleMarker,\n",
    "                        MarkerCluster,\n",
    "                        LayerGroup, \n",
    "                        LayersControl,\n",
    "                        WidgetControl,\n",
    "                        Popup,\n",
    "                        ScaleControl\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "improved-silence",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sparql_functions as spqf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "prescription-panel",
   "metadata": {},
   "outputs": [],
   "source": [
    "### It's define the database ligne to use\n",
    "pk_query = 44\n",
    "\n",
    "# Connexion to the database\n",
    "original_db = 'data/sparql_queries.db'\n",
    "conn = sql.connect(original_db)\n",
    "\n",
    "c = conn.cursor()\n",
    "\n",
    "### It runs the query on the SQLite database to get the row values \n",
    "c.execute('SELECT * FROM query WHERE pk_query = ?', [pk_query]) ### a list around argument is needed for a string longer then one\n",
    "#c.execute('SELECT * FROM query WHERE pk_query = 10')\n",
    "\n",
    "rc = c.fetchone()\n",
    "\n",
    "# close connexion\n",
    "conn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "smaller-drama",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDescription:\u001b[0m\n",
      "\n",
      "SELECT: In this query, we will analyze the place of birth of the individuals and we will realise some maps.\n",
      "-----\n",
      "\u001b[1mDatabase:\u001b[0m\n",
      "\n",
      "Wikidata\n",
      "----\n",
      "\u001b[1mNotes:\u001b[0m\n",
      "\n",
      " \n",
      "----\n",
      "\u001b[1mTime stamp:\u001b[0m\n",
      "\n",
      "2021-07-05 09:41:53\n",
      "--------------\n",
      "\u001b[1mSparl endpoint:\u001b[0m\n",
      "\n",
      "http://localhost:7200/repositories/IMPORT\n",
      "-----------\n",
      "\u001b[1mQuery:\u001b[0m\n",
      "\n",
      "PREFIX  wikibase: <http://wikiba.se/ontology#>\n",
      "PREFIX  wd:   \t  <http://www.wikidata.org/entity/>\n",
      "PREFIX  wdt:  \t  <http://www.wikidata.org/prop/direct/>\n",
      "PREFIX  rdfs: \t  <http://www.w3.org/2000/01/rdf-schema#>\n",
      "PREFIX  owl:      <http://www.w3.org/2002/07/owl#>\n",
      "PREFIX  rdf:      <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
      "PREFIX  xsd:      <http://www.w3.org/2001/XMLSchema#>\n",
      "\n",
      "SELECT  ?person ?name  ?occupation ?year ?birthPlace  ?type ?long ?lat\n",
      "WHERE{ {\n",
      "SELECT  ?person ?name ?occupation (ROUND(AVG(?birthDate_avg)) AS ?year) ?birthPlace  ?long ?lat ?type\n",
      "    WHERE { {\n",
      "    SELECT Distinct ?person ?nameLabel (GROUP_CONCAT(DISTINCT(?occLabel); SEPARATOR = \"|\") AS ?occupation1) ?gender  ?nationality (YEAR(?bD) AS ?birthDate_avg)  (MIN(?birthPlaceLabel) AS ?birthPlace) ?long ?lat ?type\n",
      "   WHERE\n",
      "      { {?person wdt:P569 ?bD ;\n",
      "      rdfs:label ?nameLabel .\n",
      "       ?person wdt:P21 ?gen .\n",
      "       ?gen rdfs:label ?gender .\n",
      "       ?person wdt:P106 ?occ .\n",
      "       ?occ  rdfs:label ?occLabel .\n",
      "       ?person wdt:P27 ?na .\n",
      "       ?na rdfs:label ?nationality .\n",
      "       ?person wdt:P19 ?pB .\n",
      "       ?pB rdfs:label ?birthPlaceLabel.\n",
      "       ?pB wikibase:geoLatitude  ?lat .\n",
      "       ?pB wikibase:geoLongitude ?long .\n",
      "       FILTER ( regex(?occLabel, \"^economist$\", \"i\") || regex(?occLabel, \"^jurist$\", \"i\")  || regex(?occLabel, \"^lawyer$\", \"i\") || regex(?occLabel, \"^judge$\", \"i\") || regex(?occLabel, \"^attorney at law$\", \"i\") || regex(?occLabel, \"^advocate$\", \"i\") || regex(?occLabel, \"^magistrate$\", \"i\") || regex(?occLabel, \"^legal advocate in France$\", \"i\"))\n",
      "     FILTER (lang(?nameLabel) = 'en')\n",
      "                }\n",
      "                OPTIONAL\n",
      "                {?pB wdt:P31 ?typeBP .\n",
      "       \t\t\t?typeBP rdfs:label ?type.\n",
      "                } }\n",
      "           GROUP BY ?person ?nationality ?gender ?bD  ?long ?lat ?nameLabel ?type\n",
      "}\n",
      "                BIND(REPLACE(?occupation1, \"attorney at law\", \"jurist\", \"i\") AS ?occupation2)\n",
      "                BIND(REPLACE(?occupation2, \"lawyer\", \"jurist\", \"i\") AS ?occupation3)\n",
      "                BIND(REPLACE(?occupation3, \"judge\", \"jurist\", \"i\") AS ?occupation4)\n",
      "                BIND(REPLACE(?occupation4, \"advocate\", \"jurist\", \"i\") AS ?occupation5)\n",
      "        \t\tBIND(REPLACE(?occupation5, \"magistrate\", \"jurist\", \"i\") AS ?occupation6)\n",
      "                BIND(REPLACE(?occupation6, \"^legal advocate in France$\", \"jurist\", \"i\") AS ?occupation)\n",
      "                BIND(str(?nameLabel) AS ?name)\n",
      "    }\n",
      "       GROUP BY ?person ?nationality ?occupation ?gender ?birthPlace  ?long ?lat ?name ?type\n",
      "\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(\"\\033[1mDescription:\\033[0m\\n\\n\"+rc[3]  + \"\\n-----\\n\" +\"\\033[1mDatabase:\\033[0m\\n\\n\"+ rc[2]+  \"\\n----\\n\"+\"\\033[1mNotes:\\033[0m\\n\\n\" +rc[7] + \"\\n----\\n\"+\"\\033[1mTime stamp:\\033[0m\\n\\n\"+rc[8]+  \"\\n--------------\\n\" +\"\\033[1mSparl endpoint:\\033[0m\\n\\n\"+   rc[5] + \"\\n-----------\\n\" +\"\\033[1mQuery:\\033[0m\\n\\n\"+ rc[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "mineral-element",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "### Execute the SPARQL query wrapped in the function in the library _sparql_functions.py_\n",
    "# The first setting correspond to SPARQL Endpoint, the seconde to the query\n",
    "q = spqf.get_json_sparql_result(rc[5],rc[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "unlike-packet",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99810\n"
     ]
    }
   ],
   "source": [
    "##### Transform the result into a list with a fonction of the library #####\n",
    "#### Result of the query\n",
    "r_query = [l for l in spqf.sparql_result_to_list(q)]\n",
    "print(len(r_query))\n",
    "#r_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "skilled-resource",
   "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>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>occupation</th>\n",
       "      <th>year</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b</td>\n",
       "      <td>Stephen Harper</td>\n",
       "      <td>economist</td>\n",
       "      <td>1959</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>city</td>\n",
       "      <td>-79.386666666667</td>\n",
       "      <td>43.670277777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:5c7f3133-a15b-4f35-8e17-162cf63207f7</td>\n",
       "      <td>Abraham Lincoln</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1809</td>\n",
       "      <td>Hodgenville</td>\n",
       "      <td></td>\n",
       "      <td>-85.738611111111</td>\n",
       "      <td>37.571111111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:92ebbd17-f4d1-4cdd-816c-5fcf4ba34dce</td>\n",
       "      <td>Wiltraut Rupp-von Brünneck</td>\n",
       "      <td>jurist|jurist</td>\n",
       "      <td>1912</td>\n",
       "      <td>Lankwitz</td>\n",
       "      <td></td>\n",
       "      <td>13.35</td>\n",
       "      <td>52.4333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c</td>\n",
       "      <td>Joseph C. O'Mahoney</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1884</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td></td>\n",
       "      <td>-71.033333333333</td>\n",
       "      <td>42.391666666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:2178758f-d8df-447d-b64b-bd78dfe045b7</td>\n",
       "      <td>Valentyn Symonenko</td>\n",
       "      <td>economist</td>\n",
       "      <td>1940</td>\n",
       "      <td>Odessa</td>\n",
       "      <td></td>\n",
       "      <td>30.73262</td>\n",
       "      <td>46.47747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62</td>\n",
       "      <td>Hugh Meade Alcorn, Jr.</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1907</td>\n",
       "      <td>United States of America</td>\n",
       "      <td></td>\n",
       "      <td>-98.5795</td>\n",
       "      <td>39.828175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>urn:uuid:8b81cd5a-63af-4905-817e-9dcea33ae252</td>\n",
       "      <td>Wolfgang Poppelbaum</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1939</td>\n",
       "      <td>Vienna</td>\n",
       "      <td></td>\n",
       "      <td>16.373064</td>\n",
       "      <td>48.20833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>urn:uuid:0a98b4ed-2ecf-4a4f-8250-e405279d4f64</td>\n",
       "      <td>Alexander Eisvogel</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1965</td>\n",
       "      <td>Mechernich</td>\n",
       "      <td></td>\n",
       "      <td>6.65</td>\n",
       "      <td>50.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>urn:uuid:13dbca6a-92fc-4582-8d38-796577b4c0d0</td>\n",
       "      <td>Gejza Ferdinandy</td>\n",
       "      <td>jurist|jurist</td>\n",
       "      <td>1864</td>\n",
       "      <td>Košice</td>\n",
       "      <td>city</td>\n",
       "      <td>21.25</td>\n",
       "      <td>48.716666666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4</td>\n",
       "      <td>Bud Cullen</td>\n",
       "      <td>jurist|jurist</td>\n",
       "      <td>1927</td>\n",
       "      <td>Walden</td>\n",
       "      <td></td>\n",
       "      <td>-81.334</td>\n",
       "      <td>46.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>urn:uuid:f526bf21-41c1-4a78-b1fc-3fea7e317611</td>\n",
       "      <td>Rudolph von Holzschuher</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1777</td>\n",
       "      <td>Nuremberg</td>\n",
       "      <td></td>\n",
       "      <td>11.0775</td>\n",
       "      <td>49.453888888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>urn:uuid:08981167-0b89-414d-8568-1405661c5523</td>\n",
       "      <td>Buddha Scheidegger</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1940</td>\n",
       "      <td>Sevelen</td>\n",
       "      <td></td>\n",
       "      <td>9.4856888888889</td>\n",
       "      <td>47.120430555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>urn:uuid:9179aad4-9c44-4d9b-98f6-9d97ad36baa6</td>\n",
       "      <td>Stephen A. Northway</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1833</td>\n",
       "      <td>New York</td>\n",
       "      <td></td>\n",
       "      <td>-75.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>urn:uuid:7a6b66d4-4d7e-4039-ac03-06777914d86d</td>\n",
       "      <td>Imre Fest</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1817</td>\n",
       "      <td>Spišské Podhradie</td>\n",
       "      <td>town</td>\n",
       "      <td>20.75305556</td>\n",
       "      <td>48.99888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962</td>\n",
       "      <td>Mihály Ficsor</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1938</td>\n",
       "      <td>Heves</td>\n",
       "      <td></td>\n",
       "      <td>20.283333333333</td>\n",
       "      <td>47.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id_entity                        name  \\\n",
       "0   urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b              Stephen Harper   \n",
       "1   urn:uuid:5c7f3133-a15b-4f35-8e17-162cf63207f7             Abraham Lincoln   \n",
       "2   urn:uuid:92ebbd17-f4d1-4cdd-816c-5fcf4ba34dce  Wiltraut Rupp-von Brünneck   \n",
       "3   urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c         Joseph C. O'Mahoney   \n",
       "4   urn:uuid:2178758f-d8df-447d-b64b-bd78dfe045b7          Valentyn Symonenko   \n",
       "5   urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62      Hugh Meade Alcorn, Jr.   \n",
       "6   urn:uuid:8b81cd5a-63af-4905-817e-9dcea33ae252         Wolfgang Poppelbaum   \n",
       "7   urn:uuid:0a98b4ed-2ecf-4a4f-8250-e405279d4f64          Alexander Eisvogel   \n",
       "8   urn:uuid:13dbca6a-92fc-4582-8d38-796577b4c0d0            Gejza Ferdinandy   \n",
       "9   urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4                  Bud Cullen   \n",
       "10  urn:uuid:f526bf21-41c1-4a78-b1fc-3fea7e317611     Rudolph von Holzschuher   \n",
       "11  urn:uuid:08981167-0b89-414d-8568-1405661c5523          Buddha Scheidegger   \n",
       "12  urn:uuid:9179aad4-9c44-4d9b-98f6-9d97ad36baa6         Stephen A. Northway   \n",
       "13  urn:uuid:7a6b66d4-4d7e-4039-ac03-06777914d86d                   Imre Fest   \n",
       "14  urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962               Mihály Ficsor   \n",
       "\n",
       "       occupation  year                birthPlace  type              long  \\\n",
       "0       economist  1959                   Toronto  city  -79.386666666667   \n",
       "1          jurist  1809               Hodgenville        -85.738611111111   \n",
       "2   jurist|jurist  1912                  Lankwitz                   13.35   \n",
       "3          jurist  1884                   Chelsea        -71.033333333333   \n",
       "4       economist  1940                    Odessa                30.73262   \n",
       "5          jurist  1907  United States of America                -98.5795   \n",
       "6          jurist  1939                    Vienna               16.373064   \n",
       "7          jurist  1965                Mechernich                    6.65   \n",
       "8   jurist|jurist  1864                    Košice  city             21.25   \n",
       "9   jurist|jurist  1927                    Walden                 -81.334   \n",
       "10         jurist  1777                 Nuremberg                 11.0775   \n",
       "11         jurist  1940                   Sevelen         9.4856888888889   \n",
       "12         jurist  1833                  New York                   -75.0   \n",
       "13         jurist  1817         Spišské Podhradie  town       20.75305556   \n",
       "14         jurist  1938                     Heves         20.283333333333   \n",
       "\n",
       "                lat  \n",
       "0   43.670277777778  \n",
       "1   37.571111111111  \n",
       "2           52.4333  \n",
       "3   42.391666666667  \n",
       "4          46.47747  \n",
       "5         39.828175  \n",
       "6          48.20833  \n",
       "7              50.6  \n",
       "8   48.716666666667  \n",
       "9            46.358  \n",
       "10  49.453888888889  \n",
       "11  47.120430555556  \n",
       "12             43.0  \n",
       "13      48.99888889  \n",
       "14             47.6  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create a dataframe\n",
    "df_places = pd.DataFrame(r_query)\n",
    "df_places.columns =['id_entity', \"name\", \"occupation\", \"year\", \"birthPlace\", \"type\", \"long\" ,\"lat\"]\n",
    "df_places[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "obvious-height",
   "metadata": {},
   "outputs": [],
   "source": [
    "### We encode to obtain of three types: \"jurist\", \"economist\" and \"jurist/economist\"  \n",
    "## ( In the SPARQL query, a concatenation of the type has been processed cf. SQLite: query n°44)\n",
    "df_places['occupation'].replace([\"jurist|jurist\", \"jurist|jurist|jurist\", \"jurist|jurist|jurist|jurist\", \"legal jurist in France\", \"jurist|legal jurist in France\"],'jurist',inplace=True)\n",
    "df_places['occupation'].replace([\"jurist|jurist|economist\", \"jurist|jurist|jurist|economist\", \"jurist|jurist|jurist|jurist\", \"jurist|economist|jurist\", \"economist|jurist\", \"jurist|economist\"],'jurist/economist',inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fewer-protocol",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_places.to_csv(\"spreadsheets/Maps/df_places.csv\", sep=\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "continuous-variety",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_places=pd.read_csv(\"spreadsheets/Maps/df_places.csv\", sep=\"|\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "banned-tribune",
   "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>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>20741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital</th>\n",
       "      <td>12859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>town</th>\n",
       "      <td>2072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city/town</th>\n",
       "      <td>1786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global city</th>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>megacity</th>\n",
       "      <td>798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>village</th>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suburb</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban area</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>township</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              freq\n",
       "type              \n",
       "city         20741\n",
       "capital      12859\n",
       "town          2072\n",
       "city/town     1786\n",
       "global city    995\n",
       "megacity       798\n",
       "village        782\n",
       "suburb         144\n",
       "urban area      53\n",
       "township         7"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Group data by place type and count them\n",
    "gb_places = pd.DataFrame(df_places.groupby('type').size())\n",
    "gb_places.columns = ['freq']\n",
    "gb_places.sort_values(by='freq', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "threatened-plumbing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>eff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>45993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>10093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital</th>\n",
       "      <td>5142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital,city</th>\n",
       "      <td>4726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>,</th>\n",
       "      <td>2369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>,,,</th>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>town</th>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city,city</th>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city,global city</th>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>,,</th>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    eff\n",
       "type                   \n",
       "                  45993\n",
       "city              10093\n",
       "capital            5142\n",
       "capital,city       4726\n",
       ",                  2369\n",
       ",,,                1414\n",
       "town               1296\n",
       "city,city           976\n",
       "city,global city    854\n",
       ",,                  469"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Group data by place type (can have several types for one place) and count them per group\n",
    "## Inspects types: which ones keep ? \n",
    "types_places = df_places.fillna(value='').groupby(['id_entity'])['type'].apply(','.join).reset_index()\n",
    "gb_types_places = pd.DataFrame(types_places.groupby('type').size())\n",
    "gb_types_places.columns = ['eff']\n",
    "gb_types_places.sort_values(by='eff', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "silver-package",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>occupation</th>\n",
       "      <th>year</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>place_list</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b</td>\n",
       "      <td>Stephen Harper</td>\n",
       "      <td>economist</td>\n",
       "      <td>1959</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>city</td>\n",
       "      <td>-79.386667</td>\n",
       "      <td>43.670278</td>\n",
       "      <td>[Toronto, city, -79.386666666667, 43.670277777...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:5c7f3133-a15b-4f35-8e17-162cf63207f7</td>\n",
       "      <td>Abraham Lincoln</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1809</td>\n",
       "      <td>Hodgenville</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-85.738611</td>\n",
       "      <td>37.571111</td>\n",
       "      <td>[Hodgenville, nan, -85.738611111111, 37.571111...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:92ebbd17-f4d1-4cdd-816c-5fcf4ba34dce</td>\n",
       "      <td>Wiltraut Rupp-von Brünneck</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1912</td>\n",
       "      <td>Lankwitz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.350000</td>\n",
       "      <td>52.433300</td>\n",
       "      <td>[Lankwitz, nan, 13.35, 52.4333]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c</td>\n",
       "      <td>Joseph C. O'Mahoney</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1884</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-71.033333</td>\n",
       "      <td>42.391667</td>\n",
       "      <td>[Chelsea, nan, -71.03333333333299, 42.39166666...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:2178758f-d8df-447d-b64b-bd78dfe045b7</td>\n",
       "      <td>Valentyn Symonenko</td>\n",
       "      <td>economist</td>\n",
       "      <td>1940</td>\n",
       "      <td>Odessa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.732620</td>\n",
       "      <td>46.477470</td>\n",
       "      <td>[Odessa, nan, 30.73262, 46.477470000000004]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id_entity                        name  \\\n",
       "0  urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b              Stephen Harper   \n",
       "1  urn:uuid:5c7f3133-a15b-4f35-8e17-162cf63207f7             Abraham Lincoln   \n",
       "2  urn:uuid:92ebbd17-f4d1-4cdd-816c-5fcf4ba34dce  Wiltraut Rupp-von Brünneck   \n",
       "3  urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c         Joseph C. O'Mahoney   \n",
       "4  urn:uuid:2178758f-d8df-447d-b64b-bd78dfe045b7          Valentyn Symonenko   \n",
       "\n",
       "  occupation  year   birthPlace  type       long        lat  \\\n",
       "0  economist  1959      Toronto  city -79.386667  43.670278   \n",
       "1     jurist  1809  Hodgenville   NaN -85.738611  37.571111   \n",
       "2     jurist  1912     Lankwitz   NaN  13.350000  52.433300   \n",
       "3     jurist  1884      Chelsea   NaN -71.033333  42.391667   \n",
       "4  economist  1940       Odessa   NaN  30.732620  46.477470   \n",
       "\n",
       "                                          place_list  \n",
       "0  [Toronto, city, -79.386666666667, 43.670277777...  \n",
       "1  [Hodgenville, nan, -85.738611111111, 37.571111...  \n",
       "2                    [Lankwitz, nan, 13.35, 52.4333]  \n",
       "3  [Chelsea, nan, -71.03333333333299, 42.39166666...  \n",
       "4        [Odessa, nan, 30.73262, 46.477470000000004]  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Construct a column with propertyes of the places grouped as a list (create a list for each place)\n",
    "df_places['place_list'] = df_places[['birthPlace','type','long','lat']].apply(lambda x: list(x), axis=1)\n",
    "df_places.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "crucial-lewis",
   "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>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>place_list</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11</td>\n",
       "      <td>Shōji Tanaka</td>\n",
       "      <td>[[Tokyo, capital, 139.69222222222, 35.68972222...</td>\n",
       "      <td>1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919</td>\n",
       "      <td>Andrej Ferjančič</td>\n",
       "      <td>[[Slap, nan, 13.933333, 45.839166999999996]]</td>\n",
       "      <td>1848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0</td>\n",
       "      <td>Helene White</td>\n",
       "      <td>[[Jackson Heights, nan, -73.886944, 40.751389]]</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09</td>\n",
       "      <td>Ernest Boulanger</td>\n",
       "      <td>[[Nantillois, nan, 5.1391666666667, 49.2988888...</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96</td>\n",
       "      <td>Gregory Fajt</td>\n",
       "      <td>[[Greensburg, nan, -79.54222222222201, 40.2977...</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id_entity              name  \\\n",
       "0  urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11      Shōji Tanaka   \n",
       "1  urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919  Andrej Ferjančič   \n",
       "2  urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0      Helene White   \n",
       "3  urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09  Ernest Boulanger   \n",
       "4  urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96      Gregory Fajt   \n",
       "\n",
       "                                          place_list  year  \n",
       "0  [[Tokyo, capital, 139.69222222222, 35.68972222...  1924  \n",
       "1       [[Slap, nan, 13.933333, 45.839166999999996]]  1848  \n",
       "2    [[Jackson Heights, nan, -73.886944, 40.751389]]  1954  \n",
       "3  [[Nantillois, nan, 5.1391666666667, 49.2988888...  1831  \n",
       "4  [[Greensburg, nan, -79.54222222222201, 40.2977...  1954  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group places of birth per person by a new dataframe\n",
    "## All places as a list produce above are together\n",
    "l_birthPlace = df_places.groupby(['id_entity', 'name']).agg({'place_list': lambda x: list(x), 'year': lambda x: max(x)}).reset_index()\n",
    "l_birthPlace.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "vital-contrary",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77429\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11',\n",
       "  'Shōji Tanaka',\n",
       "  'Tokyo',\n",
       "  'capital',\n",
       "  139.69222222222,\n",
       "  35.689722222222,\n",
       "  1924],\n",
       " ['urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919',\n",
       "  'Andrej Ferjančič',\n",
       "  'Slap',\n",
       "  nan,\n",
       "  13.933333,\n",
       "  45.839166999999996,\n",
       "  1848]]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Drop places multiples for a person by focusing capitals, then cities, then town, etc.\n",
    "## With 'for', this browses in the order of the important types\n",
    "# if the variable 'i_birthPlace' has a place, his lentgh is not equal to 0 and this go out the loop\n",
    "# otherwise, this choose the next place of birth, etc.\n",
    "\n",
    "unique_birthPlace = []\n",
    "for l in l_birthPlace.to_records():\n",
    "    \n",
    "    i_birthPlace = []\n",
    "    if len(i_birthPlace) == 0:\n",
    "        for i in l[3]:\n",
    "            if i[1] == 'capital':\n",
    "                i_birthPlace = i\n",
    "            elif i[1] == 'city':\n",
    "                i_birthPlace = i\n",
    "            elif i[1] == 'town':\n",
    "                i_birthPlace = i\n",
    "            elif i[1] == 'city/town':\n",
    "                i_birthPlace = i\n",
    "            elif i[1] == 'village':\n",
    "                i_birthPlace = i\n",
    "    else:\n",
    "        pass\n",
    "    if len(i_birthPlace) == 0:\n",
    "        i_birthPlace = l[3][0]\n",
    "    unique_birthPlace.append([l[1]] + [l[2]]+ i_birthPlace + [int(l[4]) if str(l[4]) > '' else 2020])\n",
    "\n",
    "\n",
    "print(len(unique_birthPlace))\n",
    "unique_birthPlace[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "hindu-finger",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77429\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11</td>\n",
       "      <td>Shōji Tanaka</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>capital</td>\n",
       "      <td>139.692222</td>\n",
       "      <td>35.689722</td>\n",
       "      <td>1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919</td>\n",
       "      <td>Andrej Ferjančič</td>\n",
       "      <td>Slap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.933333</td>\n",
       "      <td>45.839167</td>\n",
       "      <td>1848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0</td>\n",
       "      <td>Helene White</td>\n",
       "      <td>Jackson Heights</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.886944</td>\n",
       "      <td>40.751389</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09</td>\n",
       "      <td>Ernest Boulanger</td>\n",
       "      <td>Nantillois</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.139167</td>\n",
       "      <td>49.298889</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96</td>\n",
       "      <td>Gregory Fajt</td>\n",
       "      <td>Greensburg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-79.542222</td>\n",
       "      <td>40.297778</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id_entity              name  \\\n",
       "0  urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11      Shōji Tanaka   \n",
       "1  urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919  Andrej Ferjančič   \n",
       "2  urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0      Helene White   \n",
       "3  urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09  Ernest Boulanger   \n",
       "4  urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96      Gregory Fajt   \n",
       "\n",
       "        birthPlace     type        long        lat  year  \n",
       "0            Tokyo  capital  139.692222  35.689722  1924  \n",
       "1             Slap      NaN   13.933333  45.839167  1848  \n",
       "2  Jackson Heights      NaN  -73.886944  40.751389  1954  \n",
       "3       Nantillois      NaN    5.139167  49.298889  1831  \n",
       "4       Greensburg      NaN  -79.542222  40.297778  1954  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ubp = pd.DataFrame(unique_birthPlace)\n",
    "df_ubp.columns = ['id_entity','name','birthPlace','type','long','lat', 'year']\n",
    "print(len(df_ubp))\n",
    "df_ubp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "complimentary-relevance",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "More than one place : 0\n",
      "No place : 0\n",
      "Individuals with one place of birth : 77429\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11</td>\n",
       "      <td>Shōji Tanaka</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>capital</td>\n",
       "      <td>139.692222</td>\n",
       "      <td>35.689722</td>\n",
       "      <td>1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919</td>\n",
       "      <td>Andrej Ferjančič</td>\n",
       "      <td>Slap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.933333</td>\n",
       "      <td>45.839167</td>\n",
       "      <td>1848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0</td>\n",
       "      <td>Helene White</td>\n",
       "      <td>Jackson Heights</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.886944</td>\n",
       "      <td>40.751389</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09</td>\n",
       "      <td>Ernest Boulanger</td>\n",
       "      <td>Nantillois</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.139167</td>\n",
       "      <td>49.298889</td>\n",
       "      <td>1831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96</td>\n",
       "      <td>Gregory Fajt</td>\n",
       "      <td>Greensburg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-79.542222</td>\n",
       "      <td>40.297778</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id_entity              name  \\\n",
       "0  urn:uuid:0000604d-91f6-4680-9ed9-4a9cd345af11      Shōji Tanaka   \n",
       "1  urn:uuid:00033b44-dea6-4a45-bd42-c4995e5e3919  Andrej Ferjančič   \n",
       "2  urn:uuid:00034d59-7ac8-4638-a0e9-af297e6ec3c0      Helene White   \n",
       "3  urn:uuid:0003a60a-43e0-4f10-b6a2-ccec94311b09  Ernest Boulanger   \n",
       "4  urn:uuid:00051051-28de-44cf-9d2b-769dd57f5e96      Gregory Fajt   \n",
       "\n",
       "        birthPlace     type        long        lat  year  \n",
       "0            Tokyo  capital  139.692222  35.689722  1924  \n",
       "1             Slap      NaN   13.933333  45.839167  1848  \n",
       "2  Jackson Heights      NaN  -73.886944  40.751389  1954  \n",
       "3       Nantillois      NaN    5.139167  49.298889  1831  \n",
       "4       Greensburg      NaN  -79.542222  40.297778  1954  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Check if there is a place of birth per individual\n",
    "gb_df_ubp= df_ubp.groupby(['id_entity']).size()\n",
    "print(\"More than one place : \" + str(len(gb_df_ubp[gb_df_ubp > 1])))\n",
    "print(\"No place : \" + str(len(gb_df_ubp[gb_df_ubp == 0])))\n",
    "print(\"Individuals with one place of birth : \" + str(len(gb_df_ubp[gb_df_ubp == 1])))\n",
    "df_ubp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thousand-planet",
   "metadata": {},
   "source": [
    "### Inspect the birth distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "amber-earthquake",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1770, 1771, 1772, 1773, 1774]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 230 artists>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gb_years_birth = df_ubp.groupby(['year']).size()\n",
    "\n",
    "\n",
    "objects = [l for l in gb_years_birth.index]\n",
    "eff = [l for l in gb_years_birth]\n",
    "\n",
    "print(objects[:5])\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.bar(objects, eff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "heard-injection",
   "metadata": {},
   "source": [
    "## **Add activity periods** \n",
    "\n",
    "We cut by activity periods corresponding to world history.\n",
    "\n",
    "Activity periods: -1900 (before XXth century), 1901-1945 (both World Wars period), 1946- (contemporary period)\n",
    "\n",
    "In addition, we can imagine ranges for the activity period \n",
    ":\n",
    "* min: birth + 25 years\n",
    "* max: birth + 75 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "binding-rolling",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fonction to add the activity period\n",
    "\n",
    "# several activity periods are possible for the same person\n",
    "def activity_period(birth_year):    \n",
    "    period_value = []\n",
    "    if (birth_year + 25) < 1901:\n",
    "        period_value.append(\"-1900\")        \n",
    "    # The second clause excludes people probably who no longer works even if born before the indicated date.\n",
    "    if (birth_year + 75) > 1900 and (birth_year + 25) < 1946:\n",
    "        period_value.append(\"1901-1945\")\n",
    "    if (birth_year + 75) > 1945:\n",
    "        period_value.append(\"1946-\")\n",
    "    return period_value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "intended-disorder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['-1900', '1901-1945'],\n",
       " ['-1900', '1901-1945', '1946-'],\n",
       " ['1901-1945', '1946-'])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activity_period(1860), activity_period(1873), activity_period(1900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "mental-times",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the activity period to the persons with the activity_period function\n",
    "df_ubp['activity_period'] = df_ubp['year'].apply (lambda x : activity_period(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "alert-assurance",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data used after in the map\n",
    "df_ubp['concat_person'] = df_ubp['id_entity'] + '|'  + df_ubp['name']+ '|' + df_ubp['year'].apply (lambda x : ' (' + str(x) + ')' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "level-scholar",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3813\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>persons</th>\n",
       "      <th>frequency</th>\n",
       "      <th>periods</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aplao</th>\n",
       "      <th>city</th>\n",
       "      <th>-72.500000</th>\n",
       "      <th>-16.083333</th>\n",
       "      <td>urn:uuid:c934e0a6-de27-4e14-b75a-01a818c7fbea|...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1900]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appenzell</th>\n",
       "      <th>village</th>\n",
       "      <th>9.408611</th>\n",
       "      <th>47.330556</th>\n",
       "      <td>urn:uuid:918dd2b2-3bad-4b2c-a3b0-f76bcea62089|...</td>\n",
       "      <td>1</td>\n",
       "      <td>[-1900, 1901-1945]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aracaju</th>\n",
       "      <th>capital</th>\n",
       "      <th>-37.050000</th>\n",
       "      <th>-10.916700</th>\n",
       "      <td>urn:uuid:062c60e0-8f7f-4295-944d-0b2690b7da82|...</td>\n",
       "      <td>18</td>\n",
       "      <td>[1946-, 1946-, -1900, 1901-1945, 1901-1945, 19...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arachova</th>\n",
       "      <th>town</th>\n",
       "      <th>22.586667</th>\n",
       "      <th>38.478889</th>\n",
       "      <td>urn:uuid:41f2062d-201b-430c-9372-b4df959a57b5|...</td>\n",
       "      <td>1</td>\n",
       "      <td>[1901-1945, 1946-]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Araci</th>\n",
       "      <th>village</th>\n",
       "      <th>25.653618</th>\n",
       "      <th>45.806686</th>\n",
       "      <td>urn:uuid:0b9b802c-48d8-4be6-9d79-539bfcb98c67|...</td>\n",
       "      <td>1</td>\n",
       "      <td>[1946-]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                    persons  \\\n",
       "birthPlace type    long       lat                                                             \n",
       "Aplao      city    -72.500000 -16.083333  urn:uuid:c934e0a6-de27-4e14-b75a-01a818c7fbea|...   \n",
       "Appenzell  village  9.408611   47.330556  urn:uuid:918dd2b2-3bad-4b2c-a3b0-f76bcea62089|...   \n",
       "Aracaju    capital -37.050000 -10.916700  urn:uuid:062c60e0-8f7f-4295-944d-0b2690b7da82|...   \n",
       "Arachova   town     22.586667  38.478889  urn:uuid:41f2062d-201b-430c-9372-b4df959a57b5|...   \n",
       "Araci      village  25.653618  45.806686  urn:uuid:0b9b802c-48d8-4be6-9d79-539bfcb98c67|...   \n",
       "\n",
       "                                          frequency  \\\n",
       "birthPlace type    long       lat                     \n",
       "Aplao      city    -72.500000 -16.083333          1   \n",
       "Appenzell  village  9.408611   47.330556          1   \n",
       "Aracaju    capital -37.050000 -10.916700         18   \n",
       "Arachova   town     22.586667  38.478889          1   \n",
       "Araci      village  25.653618  45.806686          1   \n",
       "\n",
       "                                                                                    periods  \n",
       "birthPlace type    long       lat                                                            \n",
       "Aplao      city    -72.500000 -16.083333                                            [-1900]  \n",
       "Appenzell  village  9.408611   47.330556                                 [-1900, 1901-1945]  \n",
       "Aracaju    capital -37.050000 -10.916700  [1946-, 1946-, -1900, 1901-1945, 1901-1945, 19...  \n",
       "Arachova   town     22.586667  38.478889                                 [1901-1945, 1946-]  \n",
       "Araci      village  25.653618  45.806686                                            [1946-]  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Group per place of birth\n",
    "## Add the number of individual per place of birth and the activity periods of the individuals\n",
    "# cf. https://izziswift.com/naming-returned-columns-in-pandas-aggregate-function/\n",
    "# The 'as_index' option should be marked as false but this didn't work.\n",
    "place_count = df_ubp.groupby(['birthPlace', 'type', 'long', 'lat'], as_index=True) \\\n",
    "      .agg(persons = ('concat_person', ','.join), frequency=('id_entity', 'count'),\\\n",
    "       periods = ('activity_period', 'sum')).sort_values(by=['birthPlace'])\n",
    "print(len(place_count))\n",
    "place_count[130:135]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "beautiful-sierra",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset index \n",
    "place_count=place_count.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "global-mentor",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Aggregate and count periods\n",
    "## A person can be in more than one period\n",
    "place_count['aggregate_periods'] = place_count['periods'].apply(lambda x : dict(Counter(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "instructional-nashville",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>frequency</th>\n",
       "      <th>periods</th>\n",
       "      <th>aggregate_periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>Paris</td>\n",
       "      <td>capital</td>\n",
       "      <td>2.351389</td>\n",
       "      <td>48.856944</td>\n",
       "      <td>urn:uuid:01121c51-5aeb-41ff-b9e6-f2f16bdb0a91|...</td>\n",
       "      <td>508</td>\n",
       "      <td>[1946-, 1901-1945, 1946-, 1946-, 1901-1945, 19...</td>\n",
       "      <td>{'1946-': 289, '1901-1945': 220, '-1900': 226}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     birthPlace     type      long        lat  \\\n",
       "2516      Paris  capital  2.351389  48.856944   \n",
       "\n",
       "                                                persons  frequency  \\\n",
       "2516  urn:uuid:01121c51-5aeb-41ff-b9e6-f2f16bdb0a91|...        508   \n",
       "\n",
       "                                                periods  \\\n",
       "2516  [1946-, 1901-1945, 1946-, 1946-, 1901-1945, 19...   \n",
       "\n",
       "                                   aggregate_periods  \n",
       "2516  {'1946-': 289, '1901-1945': 220, '-1900': 226}  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example: Paris\n",
    "place_count[place_count['birthPlace'] == 'Paris']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "charitable-guitar",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save dataframe\n",
    "place_count.to_csv('spreadsheets/Maps/birth_place_years.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "residential-playlist",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>frequency</th>\n",
       "      <th>periods</th>\n",
       "      <th>aggregate_periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Ammanford</td>\n",
       "      <td>town</td>\n",
       "      <td>-3.993000</td>\n",
       "      <td>51.800000</td>\n",
       "      <td>urn:uuid:1979abdf-bf9a-4747-bff6-64512ce5bb92|...</td>\n",
       "      <td>2</td>\n",
       "      <td>['1946-', '1946-']</td>\n",
       "      <td>{'1946-': 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Amoudah</td>\n",
       "      <td>city</td>\n",
       "      <td>40.930000</td>\n",
       "      <td>37.104167</td>\n",
       "      <td>urn:uuid:fe4e4d6d-ee1c-4be9-86fe-fdc581628432|...</td>\n",
       "      <td>1</td>\n",
       "      <td>['1946-']</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Amravati</td>\n",
       "      <td>city</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>20.933333</td>\n",
       "      <td>urn:uuid:f68ced86-5315-44fc-bd67-94ddc79da5d6|...</td>\n",
       "      <td>1</td>\n",
       "      <td>['1946-']</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Amritsar</td>\n",
       "      <td>city</td>\n",
       "      <td>74.877040</td>\n",
       "      <td>31.626917</td>\n",
       "      <td>urn:uuid:0ec6e130-e67e-47e0-9b68-418529748153|...</td>\n",
       "      <td>6</td>\n",
       "      <td>['1901-1945', '1946-', '1946-', '1901-1945', '...</td>\n",
       "      <td>{'1901-1945': 4, '1946-': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>city</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>52.383333</td>\n",
       "      <td>urn:uuid:00aef876-ed16-46be-a63b-6a3bae4e06a2|...</td>\n",
       "      <td>151</td>\n",
       "      <td>['1946-', '1946-', '1946-', '1946-', '1901-194...</td>\n",
       "      <td>{'1946-': 109, '1901-1945': 62, '-1900': 44}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Anamur</td>\n",
       "      <td>town</td>\n",
       "      <td>32.826111</td>\n",
       "      <td>36.083611</td>\n",
       "      <td>urn:uuid:4b1217b1-c986-45bf-9ca7-03d319e7612a|...</td>\n",
       "      <td>1</td>\n",
       "      <td>['1946-']</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Anapa</td>\n",
       "      <td>city/town</td>\n",
       "      <td>37.316667</td>\n",
       "      <td>44.894444</td>\n",
       "      <td>urn:uuid:07deb5df-f53a-4ab4-9b79-85c9107a67f6|...</td>\n",
       "      <td>1</td>\n",
       "      <td>['1946-']</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Ancona</td>\n",
       "      <td>city</td>\n",
       "      <td>13.516667</td>\n",
       "      <td>43.616667</td>\n",
       "      <td>urn:uuid:28eb2e97-7274-44ea-9b77-c561a190b9a9|...</td>\n",
       "      <td>12</td>\n",
       "      <td>['1946-', '-1900', '1946-', '1901-1945', '1946...</td>\n",
       "      <td>{'1946-': 10, '-1900': 2, '1901-1945': 6}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Andahuaylas</td>\n",
       "      <td>city</td>\n",
       "      <td>-73.383333</td>\n",
       "      <td>-13.657500</td>\n",
       "      <td>urn:uuid:8960736b-3827-4275-8b29-6b1abfb71336|...</td>\n",
       "      <td>1</td>\n",
       "      <td>['1946-']</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Anderlecht</td>\n",
       "      <td>town</td>\n",
       "      <td>4.329722</td>\n",
       "      <td>50.839167</td>\n",
       "      <td>urn:uuid:00e21975-1314-4b19-8b2e-32fb0f2f4fd1|...</td>\n",
       "      <td>4</td>\n",
       "      <td>['1946-', '1946-', '1946-', '1901-1945', '1946-']</td>\n",
       "      <td>{'1946-': 4, '1901-1945': 1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      birthPlace       type       long        lat  \\\n",
       "100    Ammanford       town  -3.993000  51.800000   \n",
       "101      Amoudah       city  40.930000  37.104167   \n",
       "102     Amravati       city  77.750000  20.933333   \n",
       "103     Amritsar       city  74.877040  31.626917   \n",
       "104    Amsterdam       city   4.900000  52.383333   \n",
       "105       Anamur       town  32.826111  36.083611   \n",
       "106        Anapa  city/town  37.316667  44.894444   \n",
       "107       Ancona       city  13.516667  43.616667   \n",
       "108  Andahuaylas       city -73.383333 -13.657500   \n",
       "109   Anderlecht       town   4.329722  50.839167   \n",
       "\n",
       "                                               persons  frequency  \\\n",
       "100  urn:uuid:1979abdf-bf9a-4747-bff6-64512ce5bb92|...          2   \n",
       "101  urn:uuid:fe4e4d6d-ee1c-4be9-86fe-fdc581628432|...          1   \n",
       "102  urn:uuid:f68ced86-5315-44fc-bd67-94ddc79da5d6|...          1   \n",
       "103  urn:uuid:0ec6e130-e67e-47e0-9b68-418529748153|...          6   \n",
       "104  urn:uuid:00aef876-ed16-46be-a63b-6a3bae4e06a2|...        151   \n",
       "105  urn:uuid:4b1217b1-c986-45bf-9ca7-03d319e7612a|...          1   \n",
       "106  urn:uuid:07deb5df-f53a-4ab4-9b79-85c9107a67f6|...          1   \n",
       "107  urn:uuid:28eb2e97-7274-44ea-9b77-c561a190b9a9|...         12   \n",
       "108  urn:uuid:8960736b-3827-4275-8b29-6b1abfb71336|...          1   \n",
       "109  urn:uuid:00e21975-1314-4b19-8b2e-32fb0f2f4fd1|...          4   \n",
       "\n",
       "                                               periods  \\\n",
       "100                                 ['1946-', '1946-']   \n",
       "101                                          ['1946-']   \n",
       "102                                          ['1946-']   \n",
       "103  ['1901-1945', '1946-', '1946-', '1901-1945', '...   \n",
       "104  ['1946-', '1946-', '1946-', '1946-', '1901-194...   \n",
       "105                                          ['1946-']   \n",
       "106                                          ['1946-']   \n",
       "107  ['1946-', '-1900', '1946-', '1901-1945', '1946...   \n",
       "108                                          ['1946-']   \n",
       "109  ['1946-', '1946-', '1946-', '1901-1945', '1946-']   \n",
       "\n",
       "                                aggregate_periods  \n",
       "100                                  {'1946-': 2}  \n",
       "101                                  {'1946-': 1}  \n",
       "102                                  {'1946-': 1}  \n",
       "103                  {'1901-1945': 4, '1946-': 6}  \n",
       "104  {'1946-': 109, '1901-1945': 62, '-1900': 44}  \n",
       "105                                  {'1946-': 1}  \n",
       "106                                  {'1946-': 1}  \n",
       "107     {'1946-': 10, '-1900': 2, '1901-1945': 6}  \n",
       "108                                  {'1946-': 1}  \n",
       "109                  {'1946-': 4, '1901-1945': 1}  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_count = pd.read_csv('spreadsheets/Maps/birth_place_years.csv', sep=';', index_col=[0])\n",
    "place_count.iloc[100:110]\n",
    "#print(len(place_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "starting-theorem",
   "metadata": {},
   "source": [
    "## **Creation of data for the three layers corresponsing to the three activity periods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "improved-array",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'s-Hertogenbosch</td>\n",
       "      <td>city</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...</td>\n",
       "      <td>{'-1900': 10, '1946-': 8, '1901-1945': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>city</td>\n",
       "      <td>-8.383333</td>\n",
       "      <td>43.366667</td>\n",
       "      <td>urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...</td>\n",
       "      <td>{'1946-': 66, '-1900': 9, '1901-1945': 24}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aarau</td>\n",
       "      <td>capital</td>\n",
       "      <td>8.044600</td>\n",
       "      <td>47.392300</td>\n",
       "      <td>urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...</td>\n",
       "      <td>{'-1900': 8, '1901-1945': 7, '1946-': 4}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abelvær</td>\n",
       "      <td>village</td>\n",
       "      <td>11.178333</td>\n",
       "      <td>64.729444</td>\n",
       "      <td>urn:uuid:42436a00-6ee7-4589-880a-03cfe8dfd8a4|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1, '1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Aberaeron</td>\n",
       "      <td>town</td>\n",
       "      <td>-4.259390</td>\n",
       "      <td>52.242450</td>\n",
       "      <td>urn:uuid:0fd29200-79a1-4500-908c-eeae9f57cfae|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          birthPlace     type       long        lat  \\\n",
       "1   's-Hertogenbosch     city   5.316667  51.700000   \n",
       "2           A Coruña     city  -8.383333  43.366667   \n",
       "4              Aarau  capital   8.044600  47.392300   \n",
       "8            Abelvær  village  11.178333  64.729444   \n",
       "10         Aberaeron     town  -4.259390  52.242450   \n",
       "\n",
       "                                              persons  \\\n",
       "1   urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...   \n",
       "2   urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...   \n",
       "4   urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...   \n",
       "8   urn:uuid:42436a00-6ee7-4589-880a-03cfe8dfd8a4|...   \n",
       "10  urn:uuid:0fd29200-79a1-4500-908c-eeae9f57cfae|...   \n",
       "\n",
       "                             aggregate_periods  \n",
       "1    {'-1900': 10, '1946-': 8, '1901-1945': 7}  \n",
       "2   {'1946-': 66, '-1900': 9, '1901-1945': 24}  \n",
       "4     {'-1900': 8, '1901-1945': 7, '1946-': 4}  \n",
       "8     {'-1900': 1, '1901-1945': 1, '1946-': 1}  \n",
       "10                {'-1900': 1, '1901-1945': 1}  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only people where their activity period is before 1900\n",
    "_1900 = place_count[['birthPlace', 'type','long','lat','persons','aggregate_periods']][place_count['periods'].str.contains('-1900', regex=True)]\n",
    "_1900.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "sexual-section",
   "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>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>aggregate_periods</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>city</td>\n",
       "      <td>-8.383333</td>\n",
       "      <td>43.366667</td>\n",
       "      <td>urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...</td>\n",
       "      <td>{'1946-': 66, '-1900': 9, '1901-1945': 24}</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aarau</td>\n",
       "      <td>capital</td>\n",
       "      <td>8.044600</td>\n",
       "      <td>47.392300</td>\n",
       "      <td>urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...</td>\n",
       "      <td>{'-1900': 8, '1901-1945': 7, '1946-': 4}</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abelvær</td>\n",
       "      <td>village</td>\n",
       "      <td>11.178333</td>\n",
       "      <td>64.729444</td>\n",
       "      <td>urn:uuid:42436a00-6ee7-4589-880a-03cfe8dfd8a4|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Aberaeron</td>\n",
       "      <td>town</td>\n",
       "      <td>-4.259390</td>\n",
       "      <td>52.242450</td>\n",
       "      <td>urn:uuid:0fd29200-79a1-4500-908c-eeae9f57cfae|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Aberdare</td>\n",
       "      <td>town</td>\n",
       "      <td>-3.445000</td>\n",
       "      <td>51.713056</td>\n",
       "      <td>urn:uuid:56022fa7-7a6e-429f-b880-e3a77c1b1422|...</td>\n",
       "      <td>{'-1900': 2, '1901-1945': 1}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>city</td>\n",
       "      <td>-2.100000</td>\n",
       "      <td>57.150000</td>\n",
       "      <td>urn:uuid:27fc3b63-acd1-43a2-923c-1961eff2f549|...</td>\n",
       "      <td>{'1946-': 5, '-1900': 5, '1901-1945': 2}</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Acomayo</td>\n",
       "      <td>city</td>\n",
       "      <td>-71.681111</td>\n",
       "      <td>-13.916667</td>\n",
       "      <td>urn:uuid:406e8d62-cbb6-4bbe-b210-4bd6bc504996|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 2, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Acuña</td>\n",
       "      <td>city</td>\n",
       "      <td>-100.308611</td>\n",
       "      <td>25.671389</td>\n",
       "      <td>urn:uuid:0e557647-b2e1-40b6-ae72-ce013933781b|...</td>\n",
       "      <td>{'1946-': 38, '1901-1945': 18, '-1900': 8}</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Adelaide</td>\n",
       "      <td>city</td>\n",
       "      <td>138.583333</td>\n",
       "      <td>-34.933333</td>\n",
       "      <td>urn:uuid:12fe7957-767c-44e0-a713-740d8c034e71|...</td>\n",
       "      <td>{'1946-': 35, '-1900': 8, '1901-1945': 11}</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   birthPlace     type        long        lat  \\\n",
       "2    A Coruña     city   -8.383333  43.366667   \n",
       "4       Aarau  capital    8.044600  47.392300   \n",
       "8     Abelvær  village   11.178333  64.729444   \n",
       "10  Aberaeron     town   -4.259390  52.242450   \n",
       "11   Aberdare     town   -3.445000  51.713056   \n",
       "13   Aberdeen     city   -2.100000  57.150000   \n",
       "18    Acomayo     city  -71.681111 -13.916667   \n",
       "19      Acuña     city -100.308611  25.671389   \n",
       "22   Adelaide     city  138.583333 -34.933333   \n",
       "\n",
       "                                              persons  \\\n",
       "2   urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...   \n",
       "4   urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...   \n",
       "8   urn:uuid:42436a00-6ee7-4589-880a-03cfe8dfd8a4|...   \n",
       "10  urn:uuid:0fd29200-79a1-4500-908c-eeae9f57cfae|...   \n",
       "11  urn:uuid:56022fa7-7a6e-429f-b880-e3a77c1b1422|...   \n",
       "13  urn:uuid:27fc3b63-acd1-43a2-923c-1961eff2f549|...   \n",
       "18  urn:uuid:406e8d62-cbb6-4bbe-b210-4bd6bc504996|...   \n",
       "19  urn:uuid:0e557647-b2e1-40b6-ae72-ce013933781b|...   \n",
       "22  urn:uuid:12fe7957-767c-44e0-a713-740d8c034e71|...   \n",
       "\n",
       "                             aggregate_periods  freq  \n",
       "2   {'1946-': 66, '-1900': 9, '1901-1945': 24}     9  \n",
       "4     {'-1900': 8, '1901-1945': 7, '1946-': 4}     8  \n",
       "8     {'-1900': 1, '1901-1945': 1, '1946-': 1}     1  \n",
       "10                {'-1900': 1, '1901-1945': 1}     1  \n",
       "11                {'-1900': 2, '1901-1945': 1}     2  \n",
       "13    {'1946-': 5, '-1900': 5, '1901-1945': 2}     5  \n",
       "18    {'-1900': 1, '1901-1945': 2, '1946-': 1}     1  \n",
       "19  {'1946-': 38, '1901-1945': 18, '-1900': 8}     8  \n",
       "22  {'1946-': 35, '-1900': 8, '1901-1945': 11}     8  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### creat another colums, count the number of people in the same activity period and place of birth\n",
    "# 'ast.literal_eval' convert string to dictionary\n",
    "_1900['freq'] = _1900['aggregate_periods'].apply( lambda x : ast.literal_eval(x)['-1900'])\n",
    "_1900.iloc[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "muslim-antenna",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>aggregate_periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'s-Graveland</td>\n",
       "      <td>village</td>\n",
       "      <td>5.121100</td>\n",
       "      <td>52.244200</td>\n",
       "      <td>urn:uuid:4be4e2a8-b1ac-43b5-aca6-cc82bac2690a|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'s-Hertogenbosch</td>\n",
       "      <td>city</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...</td>\n",
       "      <td>{'-1900': 10, '1946-': 8, '1901-1945': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>city</td>\n",
       "      <td>-8.383333</td>\n",
       "      <td>43.366667</td>\n",
       "      <td>urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...</td>\n",
       "      <td>{'1946-': 66, '-1900': 9, '1901-1945': 24}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aalten</td>\n",
       "      <td>village</td>\n",
       "      <td>6.581111</td>\n",
       "      <td>51.915833</td>\n",
       "      <td>urn:uuid:5576a10b-a575-4e18-8dde-5fb5594b0dda|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aarau</td>\n",
       "      <td>capital</td>\n",
       "      <td>8.044600</td>\n",
       "      <td>47.392300</td>\n",
       "      <td>urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...</td>\n",
       "      <td>{'-1900': 8, '1901-1945': 7, '1946-': 4}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthPlace     type      long        lat  \\\n",
       "0      's-Graveland  village  5.121100  52.244200   \n",
       "1  's-Hertogenbosch     city  5.316667  51.700000   \n",
       "2          A Coruña     city -8.383333  43.366667   \n",
       "3            Aalten  village  6.581111  51.915833   \n",
       "4             Aarau  capital  8.044600  47.392300   \n",
       "\n",
       "                                             persons  \\\n",
       "0  urn:uuid:4be4e2a8-b1ac-43b5-aca6-cc82bac2690a|...   \n",
       "1  urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...   \n",
       "2  urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...   \n",
       "3  urn:uuid:5576a10b-a575-4e18-8dde-5fb5594b0dda|...   \n",
       "4  urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...   \n",
       "\n",
       "                            aggregate_periods  \n",
       "0                {'1901-1945': 1, '1946-': 1}  \n",
       "1   {'-1900': 10, '1946-': 8, '1901-1945': 7}  \n",
       "2  {'1946-': 66, '-1900': 9, '1901-1945': 24}  \n",
       "3                {'1901-1945': 1, '1946-': 1}  \n",
       "4    {'-1900': 8, '1901-1945': 7, '1946-': 4}  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only people where their activity period is between 1900 and 1945\n",
    "\n",
    "_1901_1945 = place_count[['birthPlace', 'type','long','lat','persons','aggregate_periods']][place_count['periods'].str.contains('1901-1945', regex=True)]\n",
    "_1901_1945.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "north-retention",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>aggregate_periods</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>Bahía Blanca</td>\n",
       "      <td>city</td>\n",
       "      <td>-62.266667</td>\n",
       "      <td>-38.716667</td>\n",
       "      <td>urn:uuid:1ba9b517-35d8-4b8c-b75b-13abcb86128f|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 6}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Bahía Honda</td>\n",
       "      <td>city</td>\n",
       "      <td>-83.163889</td>\n",
       "      <td>22.906389</td>\n",
       "      <td>urn:uuid:9fa29122-d4fa-4515-8b37-c6b6f7fcc5b7|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>Bakhchysarai</td>\n",
       "      <td>city/town</td>\n",
       "      <td>33.860833</td>\n",
       "      <td>44.752778</td>\n",
       "      <td>urn:uuid:83048c2f-dd22-49a2-aa5e-6d2c009b1a79|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Baku</td>\n",
       "      <td>capital</td>\n",
       "      <td>49.835183</td>\n",
       "      <td>40.366656</td>\n",
       "      <td>urn:uuid:016ab311-9a9f-4a50-988c-20a45fc1b7c1|...</td>\n",
       "      <td>{'1946-': 36, '1901-1945': 5}</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>Ballarat</td>\n",
       "      <td>city</td>\n",
       "      <td>143.847500</td>\n",
       "      <td>-37.560833</td>\n",
       "      <td>urn:uuid:135f32d8-22dc-4951-b1e2-fcd0aa9c6500|...</td>\n",
       "      <td>{'1946-': 3, '1901-1945': 2}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>Ballymena</td>\n",
       "      <td>town</td>\n",
       "      <td>-6.268000</td>\n",
       "      <td>54.862000</td>\n",
       "      <td>urn:uuid:5d294eb9-1562-4260-b5d1-c1be5c9ff69d|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>Baltiysk</td>\n",
       "      <td>city/town</td>\n",
       "      <td>19.916667</td>\n",
       "      <td>54.650000</td>\n",
       "      <td>urn:uuid:40b1e9bf-2db9-472c-9c5c-a5cf7db28613|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>Bamarni</td>\n",
       "      <td>city</td>\n",
       "      <td>43.266667</td>\n",
       "      <td>37.116667</td>\n",
       "      <td>urn:uuid:c59db6e8-d164-440c-ae99-f1a18b067258|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Band</td>\n",
       "      <td>village</td>\n",
       "      <td>24.383333</td>\n",
       "      <td>46.583333</td>\n",
       "      <td>urn:uuid:83b7599e-0dee-4b2b-8b67-a8605685f5ed|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       birthPlace       type        long        lat  \\\n",
       "223  Bahía Blanca       city  -62.266667 -38.716667   \n",
       "224   Bahía Honda       city  -83.163889  22.906389   \n",
       "228  Bakhchysarai  city/town   33.860833  44.752778   \n",
       "229          Baku    capital   49.835183  40.366656   \n",
       "231      Ballarat       city  143.847500 -37.560833   \n",
       "233     Ballymena       town   -6.268000  54.862000   \n",
       "234      Baltiysk  city/town   19.916667  54.650000   \n",
       "236       Bamarni       city   43.266667  37.116667   \n",
       "240          Band    village   24.383333  46.583333   \n",
       "\n",
       "                                               persons  \\\n",
       "223  urn:uuid:1ba9b517-35d8-4b8c-b75b-13abcb86128f|...   \n",
       "224  urn:uuid:9fa29122-d4fa-4515-8b37-c6b6f7fcc5b7|...   \n",
       "228  urn:uuid:83048c2f-dd22-49a2-aa5e-6d2c009b1a79|...   \n",
       "229  urn:uuid:016ab311-9a9f-4a50-988c-20a45fc1b7c1|...   \n",
       "231  urn:uuid:135f32d8-22dc-4951-b1e2-fcd0aa9c6500|...   \n",
       "233  urn:uuid:5d294eb9-1562-4260-b5d1-c1be5c9ff69d|...   \n",
       "234  urn:uuid:40b1e9bf-2db9-472c-9c5c-a5cf7db28613|...   \n",
       "236  urn:uuid:c59db6e8-d164-440c-ae99-f1a18b067258|...   \n",
       "240  urn:uuid:83b7599e-0dee-4b2b-8b67-a8605685f5ed|...   \n",
       "\n",
       "                 aggregate_periods  freq  \n",
       "223   {'1901-1945': 1, '1946-': 6}     1  \n",
       "224   {'1901-1945': 1, '1946-': 1}     1  \n",
       "228   {'1901-1945': 1, '1946-': 1}     1  \n",
       "229  {'1946-': 36, '1901-1945': 5}     5  \n",
       "231   {'1946-': 3, '1901-1945': 2}     2  \n",
       "233   {'1901-1945': 1, '1946-': 1}     1  \n",
       "234   {'1901-1945': 1, '1946-': 1}     1  \n",
       "236   {'1901-1945': 1, '1946-': 1}     1  \n",
       "240   {'-1900': 1, '1901-1945': 1}     1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_1901_1945['freq'] = _1901_1945['aggregate_periods'].apply( lambda x : ast.literal_eval(x)['1901-1945'])\n",
    "_1901_1945.iloc[131:140]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "prepared-adaptation",
   "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>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>aggregate_periods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'s-Graveland</td>\n",
       "      <td>village</td>\n",
       "      <td>5.121100</td>\n",
       "      <td>52.244200</td>\n",
       "      <td>urn:uuid:4be4e2a8-b1ac-43b5-aca6-cc82bac2690a|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'s-Hertogenbosch</td>\n",
       "      <td>city</td>\n",
       "      <td>5.316667</td>\n",
       "      <td>51.700000</td>\n",
       "      <td>urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...</td>\n",
       "      <td>{'-1900': 10, '1946-': 8, '1901-1945': 7}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Coruña</td>\n",
       "      <td>city</td>\n",
       "      <td>-8.383333</td>\n",
       "      <td>43.366667</td>\n",
       "      <td>urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...</td>\n",
       "      <td>{'1946-': 66, '-1900': 9, '1901-1945': 24}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aalten</td>\n",
       "      <td>village</td>\n",
       "      <td>6.581111</td>\n",
       "      <td>51.915833</td>\n",
       "      <td>urn:uuid:5576a10b-a575-4e18-8dde-5fb5594b0dda|...</td>\n",
       "      <td>{'1901-1945': 1, '1946-': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aarau</td>\n",
       "      <td>capital</td>\n",
       "      <td>8.044600</td>\n",
       "      <td>47.392300</td>\n",
       "      <td>urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...</td>\n",
       "      <td>{'-1900': 8, '1901-1945': 7, '1946-': 4}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthPlace     type      long        lat  \\\n",
       "0      's-Graveland  village  5.121100  52.244200   \n",
       "1  's-Hertogenbosch     city  5.316667  51.700000   \n",
       "2          A Coruña     city -8.383333  43.366667   \n",
       "3            Aalten  village  6.581111  51.915833   \n",
       "4             Aarau  capital  8.044600  47.392300   \n",
       "\n",
       "                                             persons  \\\n",
       "0  urn:uuid:4be4e2a8-b1ac-43b5-aca6-cc82bac2690a|...   \n",
       "1  urn:uuid:049184f7-a9b3-4e4f-9478-1e8f4a9d6ba3|...   \n",
       "2  urn:uuid:01250cae-6b19-4916-b786-42c045acae96|...   \n",
       "3  urn:uuid:5576a10b-a575-4e18-8dde-5fb5594b0dda|...   \n",
       "4  urn:uuid:17760017-eca7-472d-a622-c0382c05d674|...   \n",
       "\n",
       "                            aggregate_periods  \n",
       "0                {'1901-1945': 1, '1946-': 1}  \n",
       "1   {'-1900': 10, '1946-': 8, '1901-1945': 7}  \n",
       "2  {'1946-': 66, '-1900': 9, '1901-1945': 24}  \n",
       "3                {'1901-1945': 1, '1946-': 1}  \n",
       "4    {'-1900': 8, '1901-1945': 7, '1946-': 4}  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep only people where their activity period is before 1945\n",
    "\n",
    "_1946_ = place_count[['birthPlace', 'type','long','lat','persons','aggregate_periods']][place_count['periods'].str.contains('1946-', regex=True)]\n",
    "_1946_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "checked-oriental",
   "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>birthPlace</th>\n",
       "      <th>type</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>persons</th>\n",
       "      <th>aggregate_periods</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Barrow-in-Furness</td>\n",
       "      <td>town</td>\n",
       "      <td>-3.216667</td>\n",
       "      <td>54.116667</td>\n",
       "      <td>urn:uuid:158fde76-a7fc-45ee-9a44-cb4db4fd705e|...</td>\n",
       "      <td>{'-1900': 1, '1901-1945': 1, '1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Barry</td>\n",
       "      <td>town</td>\n",
       "      <td>-3.266700</td>\n",
       "      <td>51.406400</td>\n",
       "      <td>urn:uuid:50884ecc-4f27-4627-be34-1b40ab3aa95d|...</td>\n",
       "      <td>{'1946-': 2, '1901-1945': 1}</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>Bartın</td>\n",
       "      <td>city</td>\n",
       "      <td>32.337500</td>\n",
       "      <td>41.634444</td>\n",
       "      <td>urn:uuid:a038ef55-2433-43ab-8082-43a86c9d44e1|...</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Bassar</td>\n",
       "      <td>city</td>\n",
       "      <td>0.783333</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>urn:uuid:ac7f3cf6-727f-4708-a9e8-ac78438cef7c|...</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Basseterre</td>\n",
       "      <td>city</td>\n",
       "      <td>-62.734167</td>\n",
       "      <td>17.298333</td>\n",
       "      <td>urn:uuid:1f052482-54e2-4d1d-b1c9-320bae1339be|...</td>\n",
       "      <td>{'1946-': 1}</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            birthPlace  type       long        lat  \\\n",
       "282  Barrow-in-Furness  town  -3.216667  54.116667   \n",
       "283              Barry  town  -3.266700  51.406400   \n",
       "285             Bartın  city  32.337500  41.634444   \n",
       "287             Bassar  city   0.783333   9.250000   \n",
       "288         Basseterre  city -62.734167  17.298333   \n",
       "\n",
       "                                               persons  \\\n",
       "282  urn:uuid:158fde76-a7fc-45ee-9a44-cb4db4fd705e|...   \n",
       "283  urn:uuid:50884ecc-4f27-4627-be34-1b40ab3aa95d|...   \n",
       "285  urn:uuid:a038ef55-2433-43ab-8082-43a86c9d44e1|...   \n",
       "287  urn:uuid:ac7f3cf6-727f-4708-a9e8-ac78438cef7c|...   \n",
       "288  urn:uuid:1f052482-54e2-4d1d-b1c9-320bae1339be|...   \n",
       "\n",
       "                            aggregate_periods  freq  \n",
       "282  {'-1900': 1, '1901-1945': 1, '1946-': 1}     1  \n",
       "283              {'1946-': 2, '1901-1945': 1}     2  \n",
       "285                              {'1946-': 1}     1  \n",
       "287                              {'1946-': 1}     1  \n",
       "288                              {'1946-': 1}     1  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_1946_['freq'] = _1946_['aggregate_periods'].apply( lambda x : ast.literal_eval(x)['1946-'])\n",
    "_1946_.iloc[245:250]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medical-quarter",
   "metadata": {},
   "source": [
    "To display the data on the map, it needs to be done by step. First, it needs to create the map with the base map. Then add layers of the different periods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "physical-health",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa8c21bbfd594b39aa7c309fa35f4416",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[35, 5], controls=(AttributionControl(options=['position', 'prefix'], position='bottomright'), Widg…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose basemap:\n",
    "open_street_map = basemap_to_tiles(basemaps.OpenStreetMap.Mapnik)\n",
    "watercolor = basemap_to_tiles(basemaps.Stamen.Watercolor)\n",
    "\n",
    "## https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Styling.html\n",
    "m2 = Map(layers=(watercolor,open_street_map), center=(35, 5), layout=Layout(width='100%', height='600px'),\\\n",
    "        zoom_control=False, scroll_wheel_zoom= True)\n",
    "\n",
    "zoom_slider = IntSlider(description='Zoom level:', min=0, max=15, value=2)\n",
    "jslink((zoom_slider, 'value'), (m2, 'zoom'))\n",
    "widget_control1 = WidgetControl(widget=zoom_slider, position='bottomright')\n",
    "m2.add_control(widget_control1)\n",
    "\n",
    "control = LayersControl(name='layCont', position='topright')\n",
    "m2.add_control(control)\n",
    "\n",
    "m2.add_control(ScaleControl(position='topleft'))\n",
    "\n",
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assured-springfield",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First layer to display to the map\n",
    "markers = []\n",
    "popups = []\n",
    "\n",
    "\n",
    "## Extract dataframe rows\n",
    "for i,j in _1900.iterrows():\n",
    "    message = HTML()\n",
    "    p_elements = ''\n",
    "\n",
    "    prs = j['persons'].split(',')\n",
    "    \n",
    "    for z in prs:\n",
    "\n",
    "        if '|' in z :\n",
    "            zs = z.split('|')\n",
    "            p_elements += f\"\"\"<a href=\"{zs[0]}\" target=\"\">{zs[1]}</a><br/>\"\"\"\n",
    "        else:\n",
    "            p_elements = ''\n",
    "\n",
    "    message.value = f\"<h4>{j['birthPlace']} ({len(prs)})</h4>\" + p_elements\n",
    "    a = HBox([message], layout=Layout(width='200px', height='200px', overflow_y='auto',overflow=\"auto\"))\n",
    "\n",
    "\n",
    "    circle = CircleMarker(location=(j['lat'], j['long']), color=\"black\", name=f\"{j['birthPlace']}\", \\\n",
    "                          fill_color='Crimson', radius = int(math.log((j['freq'] + 2) * 2,  2)), weight=1)\n",
    "\n",
    "    circle.popup = a\n",
    "\n",
    "    markers.append(circle)\n",
    "\n",
    "\n",
    "print(\"Markers' number \" + str(len(markers)))\n",
    "\n",
    "#name_part = str(begin_end[0]) + '-' + str(begin_end[1]) + ' (' +  color + ')'\n",
    "\n",
    "l_1900 = LayerGroup(name=\"Place of birth of the workers until 1900\", layers=(markers))\n",
    "#lg2 = MarkerCluster(name='Clusters' + name_part, markers=markers)\n",
    "\n",
    "m2.add_layer(l_1900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "informational-replacement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Markers' number 2119\n"
     ]
    }
   ],
   "source": [
    "markers = []\n",
    "popups = []\n",
    "\n",
    "\n",
    "## Extract dataframe rows\n",
    "for i,j in _1901_1945.iterrows():\n",
    "    message = HTML()\n",
    "    p_elements = ''\n",
    "\n",
    "    for z in j['persons'].split(','):\n",
    "\n",
    "        if '|' in z :\n",
    "            zs = z.split('|')\n",
    "            p_elements += f\"\"\"<a href=\"{zs[0]}\" target=\"_blank\">{zs[1]}</a><br/>\"\"\"\n",
    "        else:\n",
    "            p_elements = ''\n",
    "\n",
    "    message.value = f\"<h4>{j['birthPlace']} ({j['freq']})</h4>\" + p_elements\n",
    "    a = HBox([message], layout=Layout(width='200px', height='200px', overflow_y='auto',overflow=\"auto\"))\n",
    "\n",
    "\n",
    "    circle = CircleMarker(location=(j['lat'], j['long']), color=\"black\", name=f\"{j['birthPlace']}\", \\\n",
    "                          fill_color='blue', radius = int(math.log((j['freq'] + 2) * 2,  2)), weight=1)\n",
    "\n",
    "    circle.popup = a\n",
    "\n",
    "    markers.append(circle)\n",
    "\n",
    "\n",
    "print(\"Markers' number \" + str(len(markers)))\n",
    "\n",
    "\n",
    "l_1901_1945 = LayerGroup(name='Place of birth of the workers: 1901-1945', layers=(markers))\n",
    "\n",
    "m2.add_layer(l_1901_1945)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "anonymous-ivory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Markers' number 3332\n"
     ]
    }
   ],
   "source": [
    "markers = []\n",
    "popups = []\n",
    "\n",
    "\n",
    "## Extract dataframe rows\n",
    "for i,j in _1946_.iterrows():\n",
    "    message = HTML()\n",
    "    p_elements = ''\n",
    "\n",
    "    for z in j['persons'].split(','):\n",
    "\n",
    "        if '|' in z :\n",
    "            zs = z.split('|')\n",
    "            p_elements += f\"\"\"<a href=\"{zs[0]}\" target=\"_blank\">{zs[1]}</a><br/>\"\"\"\n",
    "        else:\n",
    "            p_elements = ''\n",
    "\n",
    "    message.value = f\"<h4>{j['birthPlace']} ({j['freq']})</h4>\" + p_elements\n",
    "    a = HBox([message], layout=Layout(width='200px', height='200px', overflow_y='auto',overflow=\"auto\"))\n",
    "\n",
    "\n",
    "    circle = CircleMarker(location=(j['lat'], j['long']), color=\"black\", name=f\"{j['birthPlace']}\", \\\n",
    "                          fill_color='DarkOrchid', radius = int(math.log((j['freq'] + 2) * 2,  2)), weight=1)\n",
    "\n",
    "    circle.popup = a\n",
    "\n",
    "    markers.append(circle)\n",
    "\n",
    "\n",
    "print(\"Markers' number \" + str(len(markers)))\n",
    "\n",
    "l_1946_ = LayerGroup(name='Place of birth of the workers before 1945 ', layers=(markers))\n",
    "\n",
    "m2.add_layer(l_1946_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minimal-objective",
   "metadata": {},
   "source": [
    "## **Universities**\n",
    "\n",
    "Now, we will analyse universities attended by the individuals with the maps realization. In Wikidata, the universities are associed to a place with geographical coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "narrative-bishop",
   "metadata": {},
   "outputs": [],
   "source": [
    "### It's define the database ligne to use\n",
    "pk_query = 45\n",
    "\n",
    "# Connexion to the database\n",
    "original_db = 'data/sparql_queries.db'\n",
    "conn = sql.connect(original_db)\n",
    "\n",
    "c = conn.cursor()\n",
    "\n",
    "### It runs the query on the SQLite database to get the row values \n",
    "c.execute('SELECT * FROM query WHERE pk_query = ?', [pk_query]) ### a list around argument is needed for a string longer then one\n",
    "#c.execute('SELECT * FROM query WHERE pk_query = 10')\n",
    "\n",
    "rc = c.fetchone()\n",
    "\n",
    "# close connexion\n",
    "conn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "temporal-assist",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDescription:\u001b[0m\n",
      "\n",
      "SELECT: In this query, we will analyze the places of studies of the individuals and we will realize maps.\n",
      "-----\n",
      "\u001b[1mDatabase:\u001b[0m\n",
      "\n",
      "Wikidata\n",
      "----\n",
      "\u001b[1mNotes:\u001b[0m\n",
      "\n",
      " \n",
      "----\n",
      "\u001b[1mTime stamp:\u001b[0m\n",
      "\n",
      "2021-07-05 16:15:09\n",
      "--------------\n",
      "\u001b[1mSparl endpoint:\u001b[0m\n",
      "\n",
      "http://localhost:7200/repositories/IMPORT\n",
      "-----------\n",
      "\u001b[1mQuery:\u001b[0m\n",
      "\n",
      "PREFIX  wikibase: <http://wikiba.se/ontology#>\n",
      "PREFIX  wd:   \t  <http://www.wikidata.org/entity/>\n",
      "PREFIX  wdt:  \t  <http://www.wikidata.org/prop/direct/>\n",
      "PREFIX  rdfs: \t  <http://www.w3.org/2000/01/rdf-schema#>\n",
      "PREFIX  owl:      <http://www.w3.org/2002/07/owl#>\n",
      "PREFIX  rdf:      <http://www.w3.org/1999/02/22-rdf-syntax-ns#>\n",
      "PREFIX  xsd:      <http://www.w3.org/2001/XMLSchema#>\n",
      "\n",
      "SELECT  Distinct  ?person ?name  ?occupation ?year ?educatedAt ?longitude ?latitude\n",
      "WHERE{ {\n",
      "SELECT   Distinct ?person ?name ?occupation (ROUND(AVG(?birthDate_avg)) AS ?year) ?educatedAt  ?longitude ?latitude\n",
      "    WHERE { {\n",
      "    SELECT Distinct ?person ?nameLabel (GROUP_CONCAT(DISTINCT(?occLabel); SEPARATOR = \"|\") AS ?occupation1) ?gender  ?nationality (YEAR(?bD) AS ?birthDate_avg)  (MIN(?educatedAt) AS ?educatedAt) (MIN(?long) AS ?longitude) (MIN(?lat) AS ?latitude) \n",
      "   WHERE\n",
      "       {?person wdt:P569 ?bD ;\n",
      "      rdfs:label ?nameLabel .\n",
      "       ?person wdt:P21 ?gen .\n",
      "       ?gen rdfs:label ?gender .\n",
      "       ?person wdt:P106 ?occ .\n",
      "       ?occ  rdfs:label ?occLabel .\n",
      "       ?person wdt:P27 ?na .\n",
      "       ?na rdfs:label ?nationality .\n",
      "       ?person wdt:P69 ?educ .\n",
      "       ?educ rdfs:label ?educatedAt.\n",
      "       ?educ wikibase:geoLatitude  ?lat .\n",
      "       ?educ wikibase:geoLongitude ?long .\n",
      "       FILTER ( regex(?occLabel, \"^economist$\", \"i\") || regex(?occLabel, \"^jurist$\", \"i\")  || regex(?occLabel, \"^lawyer$\", \"i\") || regex(?occLabel, \"^judge$\", \"i\") || regex(?occLabel, \"^attorney at law$\", \"i\") || regex(?occLabel, \"^advocate$\", \"i\") || regex(?occLabel, \"^magistrate$\", \"i\") || regex(?occLabel, \"^legal advocate in France$\", \"i\"))\n",
      "       FILTER ( regex(?educatedAt, \"university\", \"i\") || regex(?educatedAt, \"school\", \"i\")  || regex(?educatedAt, \"college\", \"i\"))\n",
      "     FILTER (lang(?nameLabel) = 'en')\n",
      "                }\n",
      "           GROUP BY ?person ?nationality ?gender ?bD ?nameLabel\n",
      "}\n",
      "                BIND(REPLACE(?occupation1, \"attorney at law\", \"jurist\", \"i\") AS ?occupation2)\n",
      "                BIND(REPLACE(?occupation2, \"lawyer\", \"jurist\", \"i\") AS ?occupation3)\n",
      "                BIND(REPLACE(?occupation3, \"judge\", \"jurist\", \"i\") AS ?occupation4)\n",
      "                BIND(REPLACE(?occupation4, \"advocate\", \"jurist\", \"i\") AS ?occupation5)\n",
      "        \t\tBIND(REPLACE(?occupation5, \"magistrate\", \"jurist\", \"i\") AS ?occupation6)\n",
      "                BIND(REPLACE(?occupation6, \"^legal advocate in France$\", \"jurist\", \"i\") AS ?occupation)\n",
      "                BIND(str(?nameLabel) AS ?name)\n",
      "    }\n",
      "       GROUP BY ?person ?nationality ?occupation ?gender ?birthPlace ?longitude ?latitude ?name ?educatedAt\n",
      "\n",
      "}\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(\"\\033[1mDescription:\\033[0m\\n\\n\"+rc[3]  + \"\\n-----\\n\" +\"\\033[1mDatabase:\\033[0m\\n\\n\"+ rc[2]+  \"\\n----\\n\"+\"\\033[1mNotes:\\033[0m\\n\\n\" +rc[7] + \"\\n----\\n\"+\"\\033[1mTime stamp:\\033[0m\\n\\n\"+rc[8]+  \"\\n--------------\\n\" +\"\\033[1mSparl endpoint:\\033[0m\\n\\n\"+   rc[5] + \"\\n-----------\\n\" +\"\\033[1mQuery:\\033[0m\\n\\n\"+ rc[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "passing-charleston",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<urlopen error [Errno 111] Connection refused>\n"
     ]
    }
   ],
   "source": [
    "### Execute the SPARQL query wrapped in the function in the library _sparql_functions.py_\n",
    "# The first setting correspond to SPARQL Endpoint, the seconde to the query\n",
    "q = spqf.get_json_sparql_result(rc[5],rc[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "figured-williams",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47827\n"
     ]
    }
   ],
   "source": [
    "##### Transform the result into a list with a fonction of the library #####\n",
    "#### Result of the query\n",
    "r_query = [l for l in spqf.sparql_result_to_list(q)]\n",
    "print(len(r_query))\n",
    "#r_query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "polyphonic-geometry",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47827\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>occupation</th>\n",
       "      <th>year</th>\n",
       "      <th>educatedAt</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b</td>\n",
       "      <td>Stephen Harper</td>\n",
       "      <td>economist</td>\n",
       "      <td>1959</td>\n",
       "      <td>University of Calgary</td>\n",
       "      <td>-114.13305555556</td>\n",
       "      <td>51.0775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c</td>\n",
       "      <td>Joseph C. O'Mahoney</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1884</td>\n",
       "      <td>Columbia University</td>\n",
       "      <td>-73.961944444444</td>\n",
       "      <td>38.898333333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62</td>\n",
       "      <td>Hugh Meade Alcorn, Jr.</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1907</td>\n",
       "      <td>Dartmouth College</td>\n",
       "      <td>-72.288333333333</td>\n",
       "      <td>41.311581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4</td>\n",
       "      <td>Bud Cullen</td>\n",
       "      <td>jurist|jurist</td>\n",
       "      <td>1927</td>\n",
       "      <td>University of Toronto</td>\n",
       "      <td>-79.395746</td>\n",
       "      <td>43.662917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962</td>\n",
       "      <td>Mihály Ficsor</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1938</td>\n",
       "      <td>Eötvös Loránd University</td>\n",
       "      <td>19.0585</td>\n",
       "      <td>46.2502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>urn:uuid:f4d660c4-0a91-4a26-b73e-fee221b0919a</td>\n",
       "      <td>Buddy Leach</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1934</td>\n",
       "      <td>Leesville High School</td>\n",
       "      <td>-91.1783</td>\n",
       "      <td>30.4145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>urn:uuid:f2876fd2-f25d-4da2-bc0c-27b240176a5e</td>\n",
       "      <td>Ibtihaj Al-Yawar</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1946</td>\n",
       "      <td>University of Baghdad</td>\n",
       "      <td>44.381719444444</td>\n",
       "      <td>33.270111111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>urn:uuid:ea20da04-0487-4f39-b815-3ab55a2e3f5d</td>\n",
       "      <td>John Herman Rice</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1876</td>\n",
       "      <td>Clongowes Wood College</td>\n",
       "      <td>-6.6834444444444</td>\n",
       "      <td>53.310916666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>urn:uuid:56e2bdaa-d39c-4665-9b8d-f16e7dc8b88e</td>\n",
       "      <td>Ann Harding</td>\n",
       "      <td>economist</td>\n",
       "      <td>1958</td>\n",
       "      <td>London School of Economics and Political Science</td>\n",
       "      <td>-0.11666666666667</td>\n",
       "      <td>-33.88778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>urn:uuid:a51a6950-f8bc-4e7f-ac21-f15a73bea5eb</td>\n",
       "      <td>Szilvia Vetter</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1983</td>\n",
       "      <td>Budapest Metropolitan University</td>\n",
       "      <td>19.0585</td>\n",
       "      <td>47.4906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>urn:uuid:39d7d26a-e79c-448d-9e1a-e6cbf1195ad1</td>\n",
       "      <td>François Bourguignon</td>\n",
       "      <td>economist</td>\n",
       "      <td>1945</td>\n",
       "      <td>University of Orléans</td>\n",
       "      <td>1.93398</td>\n",
       "      <td>47.8507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>urn:uuid:42059454-7941-47d7-9bc7-5ec28743220c</td>\n",
       "      <td>Fatoumata Sylla</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1954</td>\n",
       "      <td>University Toulouse I - Capitole</td>\n",
       "      <td>1.43667</td>\n",
       "      <td>43.6064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>urn:uuid:fb8010fe-ab7c-4f8f-a851-7a07bcec70e2</td>\n",
       "      <td>Youssef Fenianos</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1964</td>\n",
       "      <td>Lebanese University</td>\n",
       "      <td>35.522189</td>\n",
       "      <td>33.827815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>urn:uuid:540d8caa-be15-478e-974f-50f02ddf2680</td>\n",
       "      <td>ʻAbd Allah Fikri Khani</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1922</td>\n",
       "      <td>American University of Beirut</td>\n",
       "      <td>35.482283333333</td>\n",
       "      <td>33.51138889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>urn:uuid:e0f09d06-e014-4a92-bec1-40da7a945f49</td>\n",
       "      <td>Simon Schwichtenberg</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1990</td>\n",
       "      <td>University of Bremen</td>\n",
       "      <td>8.8536111111111</td>\n",
       "      <td>53.108611111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id_entity                    name  \\\n",
       "0   urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b          Stephen Harper   \n",
       "1   urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c     Joseph C. O'Mahoney   \n",
       "2   urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62  Hugh Meade Alcorn, Jr.   \n",
       "3   urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4              Bud Cullen   \n",
       "4   urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962           Mihály Ficsor   \n",
       "5   urn:uuid:f4d660c4-0a91-4a26-b73e-fee221b0919a             Buddy Leach   \n",
       "6   urn:uuid:f2876fd2-f25d-4da2-bc0c-27b240176a5e        Ibtihaj Al-Yawar   \n",
       "7   urn:uuid:ea20da04-0487-4f39-b815-3ab55a2e3f5d        John Herman Rice   \n",
       "8   urn:uuid:56e2bdaa-d39c-4665-9b8d-f16e7dc8b88e             Ann Harding   \n",
       "9   urn:uuid:a51a6950-f8bc-4e7f-ac21-f15a73bea5eb          Szilvia Vetter   \n",
       "10  urn:uuid:39d7d26a-e79c-448d-9e1a-e6cbf1195ad1    François Bourguignon   \n",
       "11  urn:uuid:42059454-7941-47d7-9bc7-5ec28743220c         Fatoumata Sylla   \n",
       "12  urn:uuid:fb8010fe-ab7c-4f8f-a851-7a07bcec70e2        Youssef Fenianos   \n",
       "13  urn:uuid:540d8caa-be15-478e-974f-50f02ddf2680  ʻAbd Allah Fikri Khani   \n",
       "14  urn:uuid:e0f09d06-e014-4a92-bec1-40da7a945f49    Simon Schwichtenberg   \n",
       "\n",
       "       occupation  year                                        educatedAt  \\\n",
       "0       economist  1959                             University of Calgary   \n",
       "1          jurist  1884                               Columbia University   \n",
       "2          jurist  1907                                 Dartmouth College   \n",
       "3   jurist|jurist  1927                             University of Toronto   \n",
       "4          jurist  1938                          Eötvös Loránd University   \n",
       "5          jurist  1934                             Leesville High School   \n",
       "6          jurist  1946                             University of Baghdad   \n",
       "7          jurist  1876                            Clongowes Wood College   \n",
       "8       economist  1958  London School of Economics and Political Science   \n",
       "9          jurist  1983                  Budapest Metropolitan University   \n",
       "10      economist  1945                             University of Orléans   \n",
       "11         jurist  1954                  University Toulouse I - Capitole   \n",
       "12         jurist  1964                               Lebanese University   \n",
       "13         jurist  1922                     American University of Beirut   \n",
       "14         jurist  1990                              University of Bremen   \n",
       "\n",
       "                 long              lat  \n",
       "0    -114.13305555556          51.0775  \n",
       "1    -73.961944444444  38.898333333333  \n",
       "2    -72.288333333333        41.311581  \n",
       "3          -79.395746        43.662917  \n",
       "4             19.0585          46.2502  \n",
       "5            -91.1783          30.4145  \n",
       "6     44.381719444444  33.270111111111  \n",
       "7    -6.6834444444444  53.310916666667  \n",
       "8   -0.11666666666667        -33.88778  \n",
       "9             19.0585          47.4906  \n",
       "10            1.93398          47.8507  \n",
       "11            1.43667          43.6064  \n",
       "12          35.522189        33.827815  \n",
       "13    35.482283333333      33.51138889  \n",
       "14    8.8536111111111  53.108611111111  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Create a dataframe\n",
    "df_educ = pd.DataFrame(r_query)\n",
    "df_educ.columns =['id_entity', \"name\", \"occupation\", \"year\", \"educatedAt\", \"long\" ,\"lat\"]\n",
    "print(len(df_educ))\n",
    "df_educ[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "forbidden-circus",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_educ.to_csv('spreadsheets/Maps/birth_educatedAt.csv', sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "private-recall",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_educ=pd.read_csv('spreadsheets/Maps/birth_educatedAt.csv', sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "external-subcommittee",
   "metadata": {},
   "outputs": [],
   "source": [
    "### We encode to obtain of three types: \"jurist\", \"economist\" and \"jurist/economist\"  \n",
    "## ( In the SPARQL query, a concatenation of the type has been processed cf. SQLite: query n°106)\n",
    "df_educ['occupation'].replace([\"jurist|jurist\", \"jurist|jurist|jurist\", \"jurist|jurist|jurist|jurist\", \"legal jurist in France\", \"jurist|legal jurist in France\"],'jurist',inplace=True)\n",
    "df_educ['occupation'].replace([\"jurist|jurist|economist\", \"jurist|jurist|jurist|economist\", \"jurist|jurist|jurist|jurist\", \"jurist|economist|jurist\", \"economist|jurist\", \"jurist|economist\"],'jurist/economist',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "level-bible",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the person born after 1950\n",
    "df_educ=df_educ[df_educ[\"year\"]<=1950]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "accessible-petersburg",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1950    846\n",
       "1948    762\n",
       "1947    732\n",
       "1949    719\n",
       "1946    671\n",
       "       ... \n",
       "1774     27\n",
       "1784     26\n",
       "1771     23\n",
       "1775     20\n",
       "1777     15\n",
       "Length: 181, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_years = df_educ.groupby('year').size()\n",
    "gp_years.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "precise-context",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counstruct a cut making sens with the historical situation\n",
    "cut_years = [1770, 1861, 1901, 1921, 1941, 1951]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "reverse-concentrate",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the date of birth into integer\n",
    "df_educ[\"year\"]=df_educ[\"year\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "funky-colon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      int64\n",
       "id_entity      object\n",
       "name           object\n",
       "occupation     object\n",
       "year            int64\n",
       "educatedAt     object\n",
       "long          float64\n",
       "lat           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the transformation\n",
    "df_educ.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "coordinated-dispute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>occupation</th>\n",
       "      <th>year</th>\n",
       "      <th>educatedAt</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c</td>\n",
       "      <td>Joseph C. O'Mahoney</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1884</td>\n",
       "      <td>Columbia University</td>\n",
       "      <td>-73.961944</td>\n",
       "      <td>38.898333</td>\n",
       "      <td>1861-1901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62</td>\n",
       "      <td>Hugh Meade Alcorn, Jr.</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1907</td>\n",
       "      <td>Dartmouth College</td>\n",
       "      <td>-72.288333</td>\n",
       "      <td>41.311581</td>\n",
       "      <td>1901-1921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4</td>\n",
       "      <td>Bud Cullen</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1927</td>\n",
       "      <td>University of Toronto</td>\n",
       "      <td>-79.395746</td>\n",
       "      <td>43.662917</td>\n",
       "      <td>1921-1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962</td>\n",
       "      <td>Mihály Ficsor</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1938</td>\n",
       "      <td>Eötvös Loránd University</td>\n",
       "      <td>19.058500</td>\n",
       "      <td>46.250200</td>\n",
       "      <td>1921-1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>urn:uuid:f4d660c4-0a91-4a26-b73e-fee221b0919a</td>\n",
       "      <td>Buddy Leach</td>\n",
       "      <td>jurist</td>\n",
       "      <td>1934</td>\n",
       "      <td>Leesville High School</td>\n",
       "      <td>-91.178300</td>\n",
       "      <td>30.414500</td>\n",
       "      <td>1921-1941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                      id_entity  \\\n",
       "1           1  urn:uuid:9ce56c33-08da-497c-afb0-38cb6ff6536c   \n",
       "2           2  urn:uuid:490ab506-b377-4e39-bf39-3d6e3f732a62   \n",
       "3           3  urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4   \n",
       "4           4  urn:uuid:f76558c8-7df4-479a-a3f0-a2f7f14f0962   \n",
       "5           5  urn:uuid:f4d660c4-0a91-4a26-b73e-fee221b0919a   \n",
       "\n",
       "                     name occupation  year                educatedAt  \\\n",
       "1     Joseph C. O'Mahoney     jurist  1884       Columbia University   \n",
       "2  Hugh Meade Alcorn, Jr.     jurist  1907         Dartmouth College   \n",
       "3              Bud Cullen     jurist  1927     University of Toronto   \n",
       "4           Mihály Ficsor     jurist  1938  Eötvös Loránd University   \n",
       "5             Buddy Leach     jurist  1934     Leesville High School   \n",
       "\n",
       "        long        lat     period  \n",
       "1 -73.961944  38.898333  1861-1901  \n",
       "2 -72.288333  41.311581  1901-1921  \n",
       "3 -79.395746  43.662917  1921-1941  \n",
       "4  19.058500  46.250200  1921-1941  \n",
       "5 -91.178300  30.414500  1921-1941  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code the years grouping them per period\n",
    "df_educ['period'] = pd.cut(df_educ['year'], cut_years, right=False)\n",
    "### make the periods more readable \n",
    "df_educ['period'] = df_educ['period'].apply(lambda row : str(int(row.left))+'-'+str(int(row.right)))\n",
    "df_educ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "enabling-addiction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id_entity</th>\n",
       "      <th>name</th>\n",
       "      <th>occupation</th>\n",
       "      <th>year</th>\n",
       "      <th>educatedAt</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, id_entity, name, occupation, year, educatedAt, long, lat, period]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the coding\n",
    "df_educ[df_educ['period'].isnull()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cooperative-tunnel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period\n",
       "1770-1861    6691\n",
       "1861-1901    6984\n",
       "1901-1921    4917\n",
       "1921-1941    7805\n",
       "1941-1951    6737\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by period\n",
    "gb_period = df_educ.groupby('period').size()\n",
    "gb_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "friendly-syria",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform array into list\n",
    "list_periods = gb_period.index.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reserved-expansion",
   "metadata": {},
   "source": [
    "### Inspect the birth distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "confident-cursor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1770-1861', '1861-1901', '1901-1921', '1921-1941', '1941-1951']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = [l for l in gb_period.index]\n",
    "eff = [l for l in gb_period]\n",
    "\n",
    "print(objects[:5])\n",
    "plt.figure(figsize=(15,7))\n",
    "plt.xticks(rotation=45)\n",
    "a = plt.bar(objects, eff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "norman-lambda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the type of period or transform as string \n",
    "df_educ['period'] = df_educ['period'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "starting-carol",
   "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>educatedAt</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>period</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. H. Parker High School</td>\n",
       "      <td>-86.83060</td>\n",
       "      <td>33.514700</td>\n",
       "      <td>1921-1941</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A.C. Davis High School</td>\n",
       "      <td>-118.32900</td>\n",
       "      <td>40.806944</td>\n",
       "      <td>1861-1901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aalborg Cathedral School</td>\n",
       "      <td>9.91139</td>\n",
       "      <td>57.048100</td>\n",
       "      <td>1921-1941</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aalborg Cathedral School</td>\n",
       "      <td>9.91139</td>\n",
       "      <td>57.048100</td>\n",
       "      <td>1941-1951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aalborg University</td>\n",
       "      <td>9.98500</td>\n",
       "      <td>57.015000</td>\n",
       "      <td>1941-1951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 educatedAt       long        lat     period  frequency\n",
       "0  A. H. Parker High School  -86.83060  33.514700  1921-1941          1\n",
       "1    A.C. Davis High School -118.32900  40.806944  1861-1901          1\n",
       "2  Aalborg Cathedral School    9.91139  57.048100  1921-1941          2\n",
       "3  Aalborg Cathedral School    9.91139  57.048100  1941-1951          1\n",
       "4        Aalborg University    9.98500  57.015000  1941-1951          1"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by site of study and count the number of individuals per period\n",
    "gb_period_uni = df_educ.groupby(['educatedAt','long','lat', 'period']).agg({'year': 'count'}).reset_index()\n",
    "gb_period_uni = gb_period_uni.rename(columns={'year': 'frequency'})\n",
    "gb_period_uni.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "rolled-animal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the dataframe\n",
    "gb_period_uni.to_csv('maps/birth_place_educatedAt.csv', sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "found-directive",
   "metadata": {},
   "source": [
    "## **Create maps**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "controversial-marks",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05f4f1a5564247c59d27009a41fcd69d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map(center=[35, 5], controls=(AttributionControl(options=['position', 'prefix'], position='bottomright'), Widg…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choose basemap\n",
    "open_street_map = basemap_to_tiles(basemaps.OpenStreetMap.Mapnik)\n",
    "watercolor = basemap_to_tiles(basemaps.Stamen.Watercolor)\n",
    "esri_sat = basemap_to_tiles(basemaps.Esri.WorldImagery)\n",
    "\n",
    "## https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Styling.html\n",
    "m1 = Map(layers=(open_street_map,watercolor,esri_sat), center=(35, 5), layout=Layout(width='100%', height='600px'),\\\n",
    "        zoom_control=False    # , zoom=4, scroll_wheel_zoom= True\n",
    "       )\n",
    "\n",
    "zoom_slider = IntSlider(description='Zoom level:', min=0, max=15, value=2)\n",
    "jslink((zoom_slider, 'value'), (m1, 'zoom'))\n",
    "widget_control1 = WidgetControl(widget=zoom_slider, position='bottomright')\n",
    "m1.add_control(widget_control1)\n",
    "\n",
    "control = LayersControl(name='layCont', position='topright')\n",
    "m1.add_control(control)\n",
    "\n",
    "m1.add_control(ScaleControl(position='topleft'))\n",
    "\n",
    "m1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "broadband-navigation",
   "metadata": {},
   "source": [
    "## **Add the layer with the universities**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "owned-employee",
   "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>educatedAt</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>period</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. H. Parker High School</td>\n",
       "      <td>-86.83060</td>\n",
       "      <td>33.514700</td>\n",
       "      <td>1921-1941</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A.C. Davis High School</td>\n",
       "      <td>-118.32900</td>\n",
       "      <td>40.806944</td>\n",
       "      <td>1861-1901</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aalborg Cathedral School</td>\n",
       "      <td>9.91139</td>\n",
       "      <td>57.048100</td>\n",
       "      <td>1921-1941</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aalborg Cathedral School</td>\n",
       "      <td>9.91139</td>\n",
       "      <td>57.048100</td>\n",
       "      <td>1941-1951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aalborg University</td>\n",
       "      <td>9.98500</td>\n",
       "      <td>57.015000</td>\n",
       "      <td>1941-1951</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 educatedAt       long        lat     period  frequency\n",
       "0  A. H. Parker High School  -86.83060  33.514700  1921-1941          1\n",
       "1    A.C. Davis High School -118.32900  40.806944  1861-1901          1\n",
       "2  Aalborg Cathedral School    9.91139  57.048100  1921-1941          2\n",
       "3  Aalborg Cathedral School    9.91139  57.048100  1941-1951          1\n",
       "4        Aalborg University    9.98500  57.015000  1941-1951          1"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count individuals per universities and period\n",
    "gb_count_uni = df_educ.groupby(['educatedAt','long','lat']).agg({'year': 'count'}).reset_index()\n",
    "gb_count_uni = gb_count_uni.rename(columns={'year': 'frequency'})\n",
    "gb_period_uni.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "acceptable-report",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_period_uni[\"period\"]=gb_period_uni[\"period\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "scenic-convert",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Markers' number 7785\n"
     ]
    }
   ],
   "source": [
    "# Browse the dataframe and extract values\n",
    "markers = []\n",
    "for i,v in gb_count_uni.iterrows():\n",
    "    marker = Marker(location=(v['lat'], v['long']), draggable=False,\\\n",
    "                    title=  v['educatedAt'] + ' (' + str(v['frequency']) + ')' )\n",
    "                    \n",
    "    \n",
    "    markers.append(marker)\n",
    "\n",
    "print(\"Markers' number \" + str(len(markers)))\n",
    "\n",
    "lg2 = MarkerCluster(name='Universities', markers=markers)\n",
    "\n",
    "m1.add_layer(lg2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interstate-integer",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Save and display in HTML format \n",
    "## Notice the interactivity doesn't work\n",
    "## Open the file in a browser \n",
    "m1.save('maps/birth_place_educatedAt.html', title='Universities')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-oliver",
   "metadata": {},
   "source": [
    "**Comments**: it is possible to combine the universities and persons layers. But this uses a lot of ressources. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "severe-print",
   "metadata": {},
   "source": [
    "## **Maps with polygone**\n",
    "\n",
    "Now, another method to realize maps is proposed. This one is less interactive, than the previous one, but it permits more to visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "everyday-drawing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import a dataframe with the polygon of the world's countries from geopandas\n",
    "# A polygon permits to have the outlines of an object\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "integral-prague",
   "metadata": {
    "tags": []
   },
   "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>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28400000.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>22270.0</td>\n",
       "      <td>POLYGON ((61.21081709172574 35.65007233330923,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12799293.0</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>110300.0</td>\n",
       "      <td>(POLYGON ((16.32652835456705 -5.87747039146621...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3639453.0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>21810.0</td>\n",
       "      <td>POLYGON ((20.59024743010491 41.85540416113361,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4798491.0</td>\n",
       "      <td>Asia</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>ARE</td>\n",
       "      <td>184300.0</td>\n",
       "      <td>POLYGON ((51.57951867046327 24.24549713795111,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40913584.0</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>573900.0</td>\n",
       "      <td>(POLYGON ((-65.50000000000003 -55.199999999999...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pop_est      continent                  name iso_a3  gdp_md_est  \\\n",
       "0  28400000.0           Asia           Afghanistan    AFG     22270.0   \n",
       "1  12799293.0         Africa                Angola    AGO    110300.0   \n",
       "2   3639453.0         Europe               Albania    ALB     21810.0   \n",
       "3   4798491.0           Asia  United Arab Emirates    ARE    184300.0   \n",
       "4  40913584.0  South America             Argentina    ARG    573900.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((61.21081709172574 35.65007233330923,...  \n",
       "1  (POLYGON ((16.32652835456705 -5.87747039146621...  \n",
       "2  POLYGON ((20.59024743010491 41.85540416113361,...  \n",
       "3  POLYGON ((51.57951867046327 24.24549713795111,...  \n",
       "4  (POLYGON ((-65.50000000000003 -55.199999999999...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "forced-product",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only the colums intresting\n",
    "world = world[['continent', 'geometry']]\n",
    "# Dissolve function aggregate the polygon from country to continent \n",
    "# cf. https://geopandas.org/docs/user_guide/aggregation_with_dissolve.html?highlight=dissolve\n",
    "continents_w = world.dissolve(by='continent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "designed-bikini",
   "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>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>(POLYGON ((49.54351891459575 -12.4698328589405...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>(POLYGON ((-159.2081835601977 -79.497059421708...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>(POLYGON ((120.7156087586305 -10.2395813940878...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>(POLYGON ((-52.55642473001839 2.50470530843705...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>(POLYGON ((173.0203747907408 -40.9190524228564...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seven seas (open ocean)</th>\n",
       "      <td>POLYGON ((68.935 -48.62500000000001, 69.58 -48...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  geometry\n",
       "continent                                                                 \n",
       "Africa                   (POLYGON ((49.54351891459575 -12.4698328589405...\n",
       "Antarctica               (POLYGON ((-159.2081835601977 -79.497059421708...\n",
       "Asia                     (POLYGON ((120.7156087586305 -10.2395813940878...\n",
       "Europe                   (POLYGON ((-52.55642473001839 2.50470530843705...\n",
       "North America            (POLYGON ((-61.68000000000001 10.76, -61.105 1...\n",
       "Oceania                  (POLYGON ((173.0203747907408 -40.9190524228564...\n",
       "Seven seas (open ocean)  POLYGON ((68.935 -48.62500000000001, 69.58 -48...\n",
       "South America            (POLYGON ((-68.63401022758316 -52.636370458874..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continents_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "adequate-repair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply a maps of the continents\n",
    "continents_w.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "recorded-tiger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>uri_entity</th>\n",
       "      <th>year</th>\n",
       "      <th>nationality</th>\n",
       "      <th>continent</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b</td>\n",
       "      <td>1959</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4</td>\n",
       "      <td>1927</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>urn:uuid:98651569-7301-4398-a760-bcb03931dc0f</td>\n",
       "      <td>1955</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>urn:uuid:080c5cc7-c62c-46ec-b0e9-0b98b6adc235</td>\n",
       "      <td>1927</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>urn:uuid:f967d755-8e28-4729-9966-1aa3e0bdba98</td>\n",
       "      <td>1827</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110358</th>\n",
       "      <td>110358</td>\n",
       "      <td>urn:uuid:bc2bef0b-434e-44d2-b42e-b25bcb069724</td>\n",
       "      <td>1961</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110359</th>\n",
       "      <td>110359</td>\n",
       "      <td>urn:uuid:0e82b54f-c7aa-4f78-8d87-351a45c79646</td>\n",
       "      <td>1837</td>\n",
       "      <td>Panama State</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110360</th>\n",
       "      <td>110360</td>\n",
       "      <td>urn:uuid:53602670-a356-4fa0-b273-2ad2e750a3f0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Q8770091</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110361</th>\n",
       "      <td>110361</td>\n",
       "      <td>urn:uuid:fce2d4e8-7b44-439c-8570-75bae75f9a87</td>\n",
       "      <td>1958</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110362</th>\n",
       "      <td>110362</td>\n",
       "      <td>urn:uuid:1db72f48-c80b-4803-8441-e3d2eb4f062c</td>\n",
       "      <td>1957</td>\n",
       "      <td>United States Virgin Islands</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110363 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index                                     uri_entity  year  \\\n",
       "0            0  urn:uuid:fb3527da-478c-4e4f-a861-9de00b9d154b  1959   \n",
       "1            1  urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4  1927   \n",
       "2            2  urn:uuid:98651569-7301-4398-a760-bcb03931dc0f  1955   \n",
       "3            3  urn:uuid:080c5cc7-c62c-46ec-b0e9-0b98b6adc235  1927   \n",
       "4            4  urn:uuid:f967d755-8e28-4729-9966-1aa3e0bdba98  1827   \n",
       "...        ...                                            ...   ...   \n",
       "110358  110358  urn:uuid:bc2bef0b-434e-44d2-b42e-b25bcb069724  1961   \n",
       "110359  110359  urn:uuid:0e82b54f-c7aa-4f78-8d87-351a45c79646  1837   \n",
       "110360  110360  urn:uuid:53602670-a356-4fa0-b273-2ad2e750a3f0  1924   \n",
       "110361  110361  urn:uuid:fce2d4e8-7b44-439c-8570-75bae75f9a87  1958   \n",
       "110362  110362  urn:uuid:1db72f48-c80b-4803-8441-e3d2eb4f062c  1957   \n",
       "\n",
       "                         nationality      continent  \\\n",
       "0                             Canada  North America   \n",
       "1                             Canada  North America   \n",
       "2                             Canada  North America   \n",
       "3                             Canada  North America   \n",
       "4                             Canada  North America   \n",
       "...                              ...            ...   \n",
       "110358                      Barbados  South America   \n",
       "110359                  Panama State  South America   \n",
       "110360                      Q8770091  South America   \n",
       "110361                         Aruba  South America   \n",
       "110362  United States Virgin Islands  South America   \n",
       "\n",
       "                                                 geometry  \n",
       "0       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "1       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "2       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "3       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "4       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "...                                                   ...  \n",
       "110358  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110359  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110360  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110361  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110362  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "\n",
       "[110363 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge polygon with a dataset previously used\n",
    "continent=pd.read_csv(\"spreadsheets/Maps/continent_map.csv\", sep=\"|\")\n",
    "continents=pd.merge(continent,continents_w, on='continent', sort=False).reset_index()\n",
    "continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "stainless-adolescent",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>uri_entity</th>\n",
       "      <th>year</th>\n",
       "      <th>nationality</th>\n",
       "      <th>continent</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4</td>\n",
       "      <td>1927</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>urn:uuid:080c5cc7-c62c-46ec-b0e9-0b98b6adc235</td>\n",
       "      <td>1927</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>urn:uuid:f967d755-8e28-4729-9966-1aa3e0bdba98</td>\n",
       "      <td>1827</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>urn:uuid:50628dbc-50cd-4fd7-93f6-a444c2dac636</td>\n",
       "      <td>1860</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>urn:uuid:d5e86bba-4821-421e-9706-438a58c978a3</td>\n",
       "      <td>1873</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>(POLYGON ((-61.68000000000001 10.76, -61.105 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110355</th>\n",
       "      <td>110355</td>\n",
       "      <td>urn:uuid:59c5f1e8-c8d3-4009-b496-a5dc3fdab1ed</td>\n",
       "      <td>1838</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110356</th>\n",
       "      <td>110356</td>\n",
       "      <td>urn:uuid:0b09a04b-0f51-470b-89aa-556077f8caad</td>\n",
       "      <td>1921</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110357</th>\n",
       "      <td>110357</td>\n",
       "      <td>urn:uuid:ca2d5779-6163-4154-b214-daccbd093ce5</td>\n",
       "      <td>1949</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110359</th>\n",
       "      <td>110359</td>\n",
       "      <td>urn:uuid:0e82b54f-c7aa-4f78-8d87-351a45c79646</td>\n",
       "      <td>1837</td>\n",
       "      <td>Panama State</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110360</th>\n",
       "      <td>110360</td>\n",
       "      <td>urn:uuid:53602670-a356-4fa0-b273-2ad2e750a3f0</td>\n",
       "      <td>1924</td>\n",
       "      <td>Q8770091</td>\n",
       "      <td>South America</td>\n",
       "      <td>(POLYGON ((-68.63401022758316 -52.636370458874...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82684 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index                                     uri_entity  year  \\\n",
       "1            1  urn:uuid:41ad1b0e-5f5e-43b6-9137-ea5f6bfe3cf4  1927   \n",
       "3            3  urn:uuid:080c5cc7-c62c-46ec-b0e9-0b98b6adc235  1927   \n",
       "4            4  urn:uuid:f967d755-8e28-4729-9966-1aa3e0bdba98  1827   \n",
       "5            5  urn:uuid:50628dbc-50cd-4fd7-93f6-a444c2dac636  1860   \n",
       "6            6  urn:uuid:d5e86bba-4821-421e-9706-438a58c978a3  1873   \n",
       "...        ...                                            ...   ...   \n",
       "110355  110355  urn:uuid:59c5f1e8-c8d3-4009-b496-a5dc3fdab1ed  1838   \n",
       "110356  110356  urn:uuid:0b09a04b-0f51-470b-89aa-556077f8caad  1921   \n",
       "110357  110357  urn:uuid:ca2d5779-6163-4154-b214-daccbd093ce5  1949   \n",
       "110359  110359  urn:uuid:0e82b54f-c7aa-4f78-8d87-351a45c79646  1837   \n",
       "110360  110360  urn:uuid:53602670-a356-4fa0-b273-2ad2e750a3f0  1924   \n",
       "\n",
       "         nationality      continent  \\\n",
       "1             Canada  North America   \n",
       "3             Canada  North America   \n",
       "4             Canada  North America   \n",
       "5             Canada  North America   \n",
       "6             Canada  North America   \n",
       "...              ...            ...   \n",
       "110355      Barbados  South America   \n",
       "110356      Barbados  South America   \n",
       "110357      Barbados  South America   \n",
       "110359  Panama State  South America   \n",
       "110360      Q8770091  South America   \n",
       "\n",
       "                                                 geometry  \n",
       "1       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "3       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "4       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "5       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "6       (POLYGON ((-61.68000000000001 10.76, -61.105 1...  \n",
       "...                                                   ...  \n",
       "110355  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110356  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110357  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110359  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "110360  (POLYGON ((-68.63401022758316 -52.636370458874...  \n",
       "\n",
       "[82684 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop people after 1950  (because the number of individuals after 1950 decreases strongly,\n",
    "# it is very probably a biais on the datas does not enter in Wikidata yet)\n",
    "continents=continents[continents[\"year\"] < 1950]\n",
    "continents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "enabling-tulsa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slice dataframe into several dataframes by period\n",
    "continents_1770_1861=continents[continents[\"year\"] <= 1861]\n",
    "continents_1861_1901=continents[(continents[\"year\"] > 1861) & (continents[\"year\"] <= 1901)]\n",
    "continents_1901_1921=continents[(continents[\"year\"] > 1901) & (continents[\"year\"] <= 1921)]\n",
    "continents_1921_1941=continents[(continents[\"year\"] > 1821) & (continents[\"year\"] <= 1941)]\n",
    "continents_1941_1950=continents[(continents[\"year\"] > 1941) & (continents[\"year\"] <= 1950)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "polar-guyana",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the row by continent\n",
    "gb_cont_1770_1861 = continents_1770_1861.groupby(['continent']).size()\n",
    "gb_cont_1861_1901 = continents_1861_1901.groupby(['continent']).size()\n",
    "gb_cont_1901_1921 = continents_1901_1921.groupby(['continent']).size()\n",
    "gb_cont_1921_1941 = continents_1921_1941.groupby(['continent']).size()\n",
    "gb_cont_1941_1950 = continents_1941_1950.groupby(['continent']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "rational-limit",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add a column 'frequency'\n",
    "gb_cont_1770_1861 = gb_cont_1770_1861.to_frame()\n",
    "gb_cont_1770_1861.columns = ['frequency']\n",
    "gb_cont_1861_1901 = gb_cont_1861_1901.to_frame()\n",
    "gb_cont_1861_1901.columns = ['frequency']\n",
    "gb_cont_1901_1921 = gb_cont_1901_1921.to_frame()\n",
    "gb_cont_1901_1921.columns = ['frequency']\n",
    "gb_cont_1921_1941 = gb_cont_1921_1941.to_frame()\n",
    "gb_cont_1921_1941.columns = ['frequency']\n",
    "gb_cont_1941_1950 = gb_cont_1941_1950.to_frame()\n",
    "gb_cont_1941_1950.columns = ['frequency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fatty-container",
   "metadata": {
    "tags": []
   },
   "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>frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>1688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>5430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>2429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               frequency\n",
       "continent               \n",
       "Africa               278\n",
       "Asia                1688\n",
       "Europe              5430\n",
       "North America       2429\n",
       "Oceania              211\n",
       "South America         96"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_cont_1941_1950"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "affected-history",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Merge each dataframe with polygon\n",
    "pd_cont_1770_1861=pd.merge(gb_cont_1770_1861,continents_w, on='continent', sort=False).reset_index()\n",
    "pd_cont_1861_1901=pd.merge(gb_cont_1861_1901,continents_w, on='continent', sort=False).reset_index()\n",
    "pd_cont_1901_1921=pd.merge(gb_cont_1901_1921,continents_w, on='continent', sort=False).reset_index()\n",
    "pd_cont_1921_1941=pd.merge(gb_cont_1921_1941,continents_w, on='continent', sort=False).reset_index()\n",
    "pd_cont_1941_1950=pd.merge(gb_cont_1941_1950,continents_w, on='continent', sort=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "continuous-hartford",
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is necessary to use the 'GeoDataFrame' function to the dataframes are well understood by Geopandas\n",
    "pd_cont_1770_1861=  gpd.GeoDataFrame(pd_cont_1770_1861)\n",
    "pd_cont_1861_1901=  gpd.GeoDataFrame(pd_cont_1861_1901)\n",
    "pd_cont_1901_1921=  gpd.GeoDataFrame(pd_cont_1901_1921)\n",
    "pd_cont_1921_1941=  gpd.GeoDataFrame(pd_cont_1921_1941)\n",
    "pd_cont_1941_1950=  gpd.GeoDataFrame(pd_cont_1941_1950)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "brazilian-oriental",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keep only the number of individuals. We will use them on the maps later.\n",
    "frequency_1770_1861=pd_cont_1770_1861['frequency']\n",
    "frequency_1861_1901=pd_cont_1861_1901['frequency']\n",
    "frequency_1901_1921=pd_cont_1901_1921['frequency']\n",
    "frequency_1921_1941=pd_cont_1921_1941['frequency']\n",
    "frequency_1941_1950=pd_cont_1941_1950['frequency']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "continent-richardson",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the percentage of individuals for each period\n",
    "perc_1770_1861= (frequency_1770_1861 / frequency_1770_1861.sum()) * 100\n",
    "perc_1861_1901= (frequency_1861_1901 / frequency_1861_1901.sum()) * 100\n",
    "perc_1901_1921= (frequency_1901_1921 / frequency_1901_1921.sum()) * 100\n",
    "perc_1921_1941= (frequency_1921_1941 / frequency_1921_1941.sum()) * 100\n",
    "perc_1941_1950= (frequency_1941_1950 / frequency_1941_1950.sum()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "tutorial-complex",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The settings are used to set the number minimum and maximum and keep the same for each map. \n",
    "vmin=2000\n",
    "vmin_per=0\n",
    "vmax=12000\n",
    "vmax_per=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "primary-stylus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 20 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the maps\n",
    "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8), (ax9, ax10)) = plt.subplots(\n",
    "                                    ncols=2,\n",
    "                                    nrows=5,\n",
    "                                    figsize=(20, 15))\n",
    "\n",
    "fig.suptitle(x=0.50,y=1.01, t='Maps of the number of individuals by continent (World) and periods')\n",
    "\n",
    "pd_cont_1770_1861.plot(column=frequency_1770_1861, ax=ax1, cmap='OrRd', legend=True, vmin=vmin, vmax=vmax)\n",
    "pd_cont_1770_1861.plot(column=perc_1770_1861,      ax=ax2, cmap='YlGn', legend=True, vmin=vmin_per, vmax=vmax_per)\n",
    "pd_cont_1861_1901.plot(column=frequency_1861_1901, ax=ax3, cmap='OrRd', legend=True, vmin=vmin, vmax=vmax)\n",
    "pd_cont_1861_1901.plot(column=perc_1861_1901,      ax=ax4, cmap='YlGn', legend=True, vmin=vmin_per, vmax=vmax_per)\n",
    "pd_cont_1901_1921.plot(column=frequency_1901_1921, ax=ax5, cmap='OrRd', legend=True, vmin=vmin, vmax=vmax)\n",
    "pd_cont_1901_1921.plot(column=perc_1901_1921,      ax=ax6, cmap='YlGn', legend=True, vmin=vmin_per, vmax=vmax_per)\n",
    "pd_cont_1921_1941.plot(column=frequency_1921_1941, ax=ax7, cmap='OrRd', legend=True, vmin=vmin, vmax=vmax)\n",
    "pd_cont_1921_1941.plot(column=perc_1921_1941,      ax=ax8, cmap='YlGn', legend=True, vmin=vmin_per, vmax=vmax_per)\n",
    "pd_cont_1941_1950.plot(column=frequency_1941_1950, ax=ax9, cmap='OrRd', legend=True, vmin=vmin, vmax=vmax)\n",
    "pd_cont_1941_1950.plot(column=perc_1941_1950,      ax=ax10, cmap='YlGn', legend=True, vmin=vmin_per, vmax=vmax_per)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "ax1.set_title('period: 1770-1861 (number of individuals)')\n",
    "ax2.set_title('period: 1770-1861 (percentage)')\n",
    "ax3.set_title('period: 1861-1901 (number of individuals)')\n",
    "ax4.set_title('period: 1861-1901 (percentage)')\n",
    "ax5.set_title('period: 1901-1921 (number of individuals)')\n",
    "ax6.set_title('period: 1901-1921 (percentage)')\n",
    "ax7.set_title('period: 1921-1941 (number of individuals)')\n",
    "ax8.set_title('period: 1921-1941 (percentage)')\n",
    "ax9.set_title('period: 1941-1950 (number of individuals)')\n",
    "ax10.set_title('period: 1941-1950 (percentage)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "golden-kentucky",
   "metadata": {},
   "source": [
    "**Comments**: This method uses lew resources compared to the previous one. This is the advantage to see better the number of individuals globally. Conversely, the individuals disappear. Unfortunalty, we can't find polygon for European europe. Another way to realise it, it will be to create the geographical points for each region. "
   ]
  }
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