Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "53a29a49",
   "metadata": {},
   "source": [
    "# ART CUT\n",
    "This notebook was created by [Francesca Borriello](https://github.com/Fran-cesca), [Lorenza Pierucci](https://github.com/LorenzaPierucci) and [Laura Travaglini](https://github.com/lauratravaglini) as part of their final project for the [Digital Pubishing and Electronic Storytelling](https://www.unibo.it/it/didattica/insegnamenti/insegnamento/2021/443749) course at the University of Bologna (academic year 2021/2022).\n",
    "\n",
    "# About \n",
    "Starting from the datasets made publicly available by the Museum of Modern Art of New York (**MoMA**) and by the **Tate galleries**, *Art cut* analyses artworks acquisitions throughout the years with the aim of understanding which criteria brought together the museums' collections in a historical and social perspective.\n",
    "\n",
    "Art and history of art are no sealed compartments: they are heavily inter-dependent with social, political, economic factors, which in turn influence our very perception of what art is. \n",
    "Cultural institutions – museums in particular – play a fundamental role in this intertwined dynamics: through their selection, they have the potential to shape the public understanding of arts and its modifications throughout time.\n",
    "\n",
    "In some way, what makes into museums makes into history of art and viceversa.\n",
    "\n",
    "From these considerations stems our analysis: how do external (social, political, economic) factors influence the perception of art and its history? \n",
    "A way to investigate it is by looking at some representative and influential museums around the world, and at their acquisition policies and campaigns in particular. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14791dfb",
   "metadata": {},
   "source": [
    "# 1. Creating dataframes.\n",
    "After importing all the necessary libraries, we can read our museums' online CSV files containing information about artworks and artists as `Pandas Dataframes` in order to better manipulate and analyse them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ce4e6d",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3260a852",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "import re\n",
    "from collections import defaultdict\n",
    "import ssl\n",
    "from json import JSONDecodeError\n",
    "from qwikidata.sparql import return_sparql_query_results # python library for working with sparql and linked data from WikiData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0791cf9e",
   "metadata": {},
   "source": [
    "For both Museums, we gather data directly from the remote files available on their Github pages ([MoMA](https://github.com/MuseumofModernArt/collection), [Tate](https://github.com/tategallery/collection)). \n",
    "In particular, we work on two separate datasets: one carries information about **artworks** (their title, date, acquisition year atc.), the other provides data on the **artists** (their name, nationality, gender etc.).\n",
    "In addition, we merge these two dataframes on some selected columns to create a new one for analysing **acquisition**-related issues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c20f9f",
   "metadata": {},
   "source": [
    "# MoMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54f227cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "spreadsheet = pd.read_csv('https://media.githubusercontent.com/media/MuseumofModernArt/collection/master/Artworks.csv')\n",
    "MoMA_artworks = spreadsheet[['ConstituentID','Title','Date', 'DateAcquired']]\n",
    "MoMA_artworks = MoMA_artworks.rename(columns = {'ConstituentID':'Id'})\n",
    "MoMA_artists = pd.read_csv('https://media.githubusercontent.com/media/MuseumofModernArt/collection/master/Artists.csv')\n",
    "MoMA_artists = MoMA_artists[['ConstituentID', 'DisplayName', 'Nationality', 'Gender']]\n",
    "MoMA_artists['ConstituentID'] = MoMA_artists['ConstituentID'].astype(str)\n",
    "MoMA_artists = MoMA_artists.rename(columns = {'ConstituentID':'Id', 'DisplayName':'Name'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6dff865a",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoMA_acquisitions = pd.merge(MoMA_artists, MoMA_artworks[['Id', 'DateAcquired']], on='Id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e21ee1d6",
   "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</th>\n",
       "      <th>Title</th>\n",
       "      <th>Date</th>\n",
       "      <th>DateAcquired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6210</td>\n",
       "      <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
       "      <td>1896</td>\n",
       "      <td>1996-04-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7470</td>\n",
       "      <td>City of Music, National Superior Conservatory ...</td>\n",
       "      <td>1987</td>\n",
       "      <td>1995-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7605</td>\n",
       "      <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
       "      <td>1903</td>\n",
       "      <td>1997-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7056</td>\n",
       "      <td>The Manhattan Transcripts Project, New York, N...</td>\n",
       "      <td>1980</td>\n",
       "      <td>1995-01-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7605</td>\n",
       "      <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
       "      <td>1903</td>\n",
       "      <td>1997-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140843</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954-55</td>\n",
       "      <td>2020-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140844</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954-55</td>\n",
       "      <td>2020-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140845</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954-55</td>\n",
       "      <td>2020-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140846</th>\n",
       "      <td>3048</td>\n",
       "      <td>Front cover of Sketchbook #24, New York City</td>\n",
       "      <td>1954-55</td>\n",
       "      <td>2020-12-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140847</th>\n",
       "      <td>3048</td>\n",
       "      <td>Front cover (verso) of Sketchbook #24, New Yor...</td>\n",
       "      <td>1954-55</td>\n",
       "      <td>2020-12-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140848 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id                                              Title     Date  \\\n",
       "0       6210  Ferdinandsbrücke Project, Vienna, Austria (Ele...     1896   \n",
       "1       7470  City of Music, National Superior Conservatory ...     1987   \n",
       "2       7605  Villa near Vienna Project, Outside Vienna, Aus...     1903   \n",
       "3       7056  The Manhattan Transcripts Project, New York, N...     1980   \n",
       "4       7605  Villa, project, outside Vienna, Austria, Exter...     1903   \n",
       "...      ...                                                ...      ...   \n",
       "140843  3048            Page from Sketchbook #24, New York City  1954-55   \n",
       "140844  3048            Page from Sketchbook #24, New York City  1954-55   \n",
       "140845  3048            Page from Sketchbook #24, New York City  1954-55   \n",
       "140846  3048       Front cover of Sketchbook #24, New York City  1954-55   \n",
       "140847  3048  Front cover (verso) of Sketchbook #24, New Yor...  1954-55   \n",
       "\n",
       "       DateAcquired  \n",
       "0        1996-04-09  \n",
       "1        1995-01-17  \n",
       "2        1997-01-15  \n",
       "3        1995-01-17  \n",
       "4        1997-01-15  \n",
       "...             ...  \n",
       "140843   2020-12-09  \n",
       "140844   2020-12-09  \n",
       "140845   2020-12-09  \n",
       "140846   2020-12-09  \n",
       "140847   2020-12-09  \n",
       "\n",
       "[140848 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(MoMA_artworks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "045bbd90",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Robert Arneson</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Doroteo Arnaiz</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Bill Arnold</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Charles Arnoldi</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Per Arnoldi</td>\n",
       "      <td>Danish</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15238</th>\n",
       "      <td>135018</td>\n",
       "      <td>Abdoulaye Konaté</td>\n",
       "      <td>Malian</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15239</th>\n",
       "      <td>135032</td>\n",
       "      <td>Yolanda Lopez</td>\n",
       "      <td>American</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15240</th>\n",
       "      <td>135042</td>\n",
       "      <td>Arnt Jensen</td>\n",
       "      <td>Danish</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15241</th>\n",
       "      <td>135111</td>\n",
       "      <td>After Sophie Taeuber-Arp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15242</th>\n",
       "      <td>135298</td>\n",
       "      <td>William Theophilius Brown</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15243 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Id                       Name Nationality  Gender\n",
       "0           1             Robert Arneson    American    Male\n",
       "1           2             Doroteo Arnaiz     Spanish    Male\n",
       "2           3                Bill Arnold    American    Male\n",
       "3           4            Charles Arnoldi    American    Male\n",
       "4           5                Per Arnoldi      Danish    Male\n",
       "...       ...                        ...         ...     ...\n",
       "15238  135018           Abdoulaye Konaté      Malian     NaN\n",
       "15239  135032              Yolanda Lopez    American  Female\n",
       "15240  135042                Arnt Jensen      Danish    Male\n",
       "15241  135111   After Sophie Taeuber-Arp         NaN     NaN\n",
       "15242  135298  William Theophilius Brown    American    Male\n",
       "\n",
       "[15243 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(MoMA_artists)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ffa20991",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DateAcquired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Robert Arneson</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1981-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Robert Arneson</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1997-05-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Doroteo Arnaiz</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>Male</td>\n",
       "      <td>1965-03-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Bill Arnold</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1972-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Bill Arnold</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1972-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135966</th>\n",
       "      <td>135018</td>\n",
       "      <td>Abdoulaye Konaté</td>\n",
       "      <td>Malian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-09-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135967</th>\n",
       "      <td>135032</td>\n",
       "      <td>Yolanda Lopez</td>\n",
       "      <td>American</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135968</th>\n",
       "      <td>135042</td>\n",
       "      <td>Arnt Jensen</td>\n",
       "      <td>Danish</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135969</th>\n",
       "      <td>135111</td>\n",
       "      <td>After Sophie Taeuber-Arp</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135970</th>\n",
       "      <td>135298</td>\n",
       "      <td>William Theophilius Brown</td>\n",
       "      <td>American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1967-12-13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135971 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id                       Name Nationality  Gender DateAcquired\n",
       "0            1             Robert Arneson    American    Male   1981-04-28\n",
       "1            1             Robert Arneson    American    Male   1997-05-28\n",
       "2            2             Doroteo Arnaiz     Spanish    Male   1965-03-09\n",
       "3            3                Bill Arnold    American    Male   1972-03-07\n",
       "4            3                Bill Arnold    American    Male   1972-03-07\n",
       "...        ...                        ...         ...     ...          ...\n",
       "135966  135018           Abdoulaye Konaté      Malian     NaN   2022-09-20\n",
       "135967  135032              Yolanda Lopez    American  Female          NaN\n",
       "135968  135042                Arnt Jensen      Danish    Male          NaN\n",
       "135969  135111   After Sophie Taeuber-Arp         NaN     NaN   1969-11-12\n",
       "135970  135298  William Theophilius Brown    American    Male   1967-12-13\n",
       "\n",
       "[135971 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(MoMA_acquisitions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee4916c",
   "metadata": {},
   "source": [
    "# Tate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12df2891",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Francesca\\AppData\\Roaming\\Python\\Python39\\site-packages\\IPython\\core\\interactiveshell.py:3172: DtypeWarning: Columns (9,13) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "spreadsheet = pd.read_csv('https://raw.githubusercontent.com/tategallery/collection/master/artwork_data.csv')\n",
    "Tate_artworks = spreadsheet[['artistId','title', 'year', 'acquisitionYear']]\n",
    "Tate_artworks = Tate_artworks.rename(columns = {'artistId':'Id', 'acquisitionYear':'DateAcquired', 'year':'Date', 'title':'Title'})\n",
    "Tate_artworks['Id'] = Tate_artworks['Id'].astype(str)\n",
    "Tate_artists = pd.read_csv('https://raw.githubusercontent.com/tategallery/collection/master/artist_data.csv')\n",
    "Tate_artists = Tate_artists[['id', 'name','placeOfBirth', 'gender']]\n",
    "Tate_artists = Tate_artists.rename(columns = {'id':'Id', 'name':'Name', 'gender':'Gender'})\n",
    "Tate_artists[\"Id\"] = Tate_artists[\"Id\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24c7a362",
   "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</th>\n",
       "      <th>Title</th>\n",
       "      <th>Date</th>\n",
       "      <th>DateAcquired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38</td>\n",
       "      <td>A Figure Bowing before a Seated Old Man with h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Two Drawings of Frightened Figures, Probably f...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>The Preaching of Warning. Verso: An Old Man En...</td>\n",
       "      <td>1785.0</td>\n",
       "      <td>1922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>Six Drawings of Figures with Outstretched Arms</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1922.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39</td>\n",
       "      <td>The Circle of the Lustful: Francesca da Rimini...</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>1919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69196</th>\n",
       "      <td>16646</td>\n",
       "      <td>Larvae (from Tampax Romana)</td>\n",
       "      <td>1975</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69197</th>\n",
       "      <td>16646</td>\n",
       "      <td>Living Womb (from Tampax Romana)</td>\n",
       "      <td>1976</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69198</th>\n",
       "      <td>2365</td>\n",
       "      <td>Present Tense</td>\n",
       "      <td>1996</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69199</th>\n",
       "      <td>2760</td>\n",
       "      <td>Work No. 227: The lights going on and off</td>\n",
       "      <td>2000</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69200</th>\n",
       "      <td>3824</td>\n",
       "      <td>Dancing Scene in the West Indies</td>\n",
       "      <td>1764</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69201 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id                                              Title    Date  \\\n",
       "0         38  A Figure Bowing before a Seated Old Man with h...     NaN   \n",
       "1         38  Two Drawings of Frightened Figures, Probably f...     NaN   \n",
       "2         38  The Preaching of Warning. Verso: An Old Man En...  1785.0   \n",
       "3         38     Six Drawings of Figures with Outstretched Arms     NaN   \n",
       "4         39  The Circle of the Lustful: Francesca da Rimini...  1826.0   \n",
       "...      ...                                                ...     ...   \n",
       "69196  16646                        Larvae (from Tampax Romana)    1975   \n",
       "69197  16646                   Living Womb (from Tampax Romana)    1976   \n",
       "69198   2365                                      Present Tense    1996   \n",
       "69199   2760          Work No. 227: The lights going on and off    2000   \n",
       "69200   3824                   Dancing Scene in the West Indies    1764   \n",
       "\n",
       "       DateAcquired  \n",
       "0            1922.0  \n",
       "1            1922.0  \n",
       "2            1922.0  \n",
       "3            1922.0  \n",
       "4            1919.0  \n",
       "...             ...  \n",
       "69196        2013.0  \n",
       "69197        2013.0  \n",
       "69198        2013.0  \n",
       "69199        2013.0  \n",
       "69200        2013.0  \n",
       "\n",
       "[69201 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Tate_artworks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e84ff778",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>placeOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10093</td>\n",
       "      <td>Abakanowicz, Magdalena</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Abbey, Edwin Austin</td>\n",
       "      <td>Philadelphia, United States</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2756</td>\n",
       "      <td>Abbott, Berenice</td>\n",
       "      <td>Springfield, United States</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Abbott, Lemuel Francis</td>\n",
       "      <td>Leicestershire, United Kingdom</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>622</td>\n",
       "      <td>Abrahams, Ivor</td>\n",
       "      <td>Wigan, United Kingdom</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3527</th>\n",
       "      <td>12542</td>\n",
       "      <td>Zorio, Gilberto</td>\n",
       "      <td>Andorno Micca, Italia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3528</th>\n",
       "      <td>2186</td>\n",
       "      <td>Zox, Larry</td>\n",
       "      <td>Des Moines, United States</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3529</th>\n",
       "      <td>621</td>\n",
       "      <td>Zuccarelli, Francesco</td>\n",
       "      <td>Italia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530</th>\n",
       "      <td>2187</td>\n",
       "      <td>Zuloaga, Ignacio</td>\n",
       "      <td>España</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>2188</td>\n",
       "      <td>Zyw, Aleksander</td>\n",
       "      <td>Polska</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3532 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id                    Name                    placeOfBirth  Gender\n",
       "0     10093  Abakanowicz, Magdalena                          Polska  Female\n",
       "1         0     Abbey, Edwin Austin     Philadelphia, United States    Male\n",
       "2      2756        Abbott, Berenice      Springfield, United States  Female\n",
       "3         1  Abbott, Lemuel Francis  Leicestershire, United Kingdom    Male\n",
       "4       622          Abrahams, Ivor           Wigan, United Kingdom    Male\n",
       "...     ...                     ...                             ...     ...\n",
       "3527  12542         Zorio, Gilberto           Andorno Micca, Italia    Male\n",
       "3528   2186              Zox, Larry       Des Moines, United States    Male\n",
       "3529    621   Zuccarelli, Francesco                          Italia    Male\n",
       "3530   2187        Zuloaga, Ignacio                          España    Male\n",
       "3531   2188         Zyw, Aleksander                          Polska    Male\n",
       "\n",
       "[3532 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Tate_artists)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96aa7146",
   "metadata": {},
   "source": [
    "# 2. Cleaning data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea349147",
   "metadata": {},
   "source": [
    "Identifying and fixing incoherent, corrupt or defective data is an essential process for ensuring a satisfactory threshold of reliability to any further analysis. Let us delve into it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39a1a22",
   "metadata": {},
   "source": [
    "## MoMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e3299f",
   "metadata": {},
   "source": [
    "### Missing values\n",
    "First of all, let us deal with missing values, substituting them with zeros to better handle them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd22d5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoMA_artists.fillna(value='0', inplace=True)\n",
    "MoMA_artworks.fillna(value='0', inplace=True)\n",
    "MoMA_acquisitions.fillna(value='0', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c65de8a6",
   "metadata": {},
   "source": [
    "### Dates\n",
    "#### Acquisition dates\n",
    "Artworks acquisition dates are in the form `YYYY-MM-DD`.<br>\n",
    "For the sake of our analysis, we extract only the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2268d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanAcquisitionDatesMoMA(date):\n",
    "    if '-' in date:\n",
    "        date = date.split('-')[0]\n",
    "        return date\n",
    "    else:\n",
    "        return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8833b363",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoMA_artworks[\"DateAcquired\"] = MoMA_artworks[\"DateAcquired\"].apply(cleanAcquisitionDatesMoMA)\n",
    "MoMA_acquisitions[\"DateAcquired\"] = MoMA_acquisitions[\"DateAcquired\"].apply(cleanAcquisitionDatesMoMA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548c1884",
   "metadata": {},
   "source": [
    "#### Artworks' creation dates\n",
    "In MoMA database, artworks' creation dates are mostly already represented by just one year.<br>\n",
    "Nevertheless, there are some exceptions: years separated by a slash or a dash and strings of any kind, such as '(1950).  (Prints executed 1948', '(1883, published 1897)' or '(1911, dated 1912, published c. 1917)'.\n",
    "\n",
    "We wrote a code to extract the year through a regex only matching the first sequence of four digits for each value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82f5c645",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanDatesMoMA(date):\n",
    "    x = re.search(\"\\d{4}\", date)\n",
    "    if x:\n",
    "        date = x.group()\n",
    "    else:\n",
    "        date = '0'\n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c86f36e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoMA_artworks[\"Date\"] = MoMA_artworks[\"Date\"].astype(str)\n",
    "MoMA_artworks[\"Date\"] = MoMA_artworks[\"Date\"].apply(cleanDatesMoMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7340f682",
   "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</th>\n",
       "      <th>Title</th>\n",
       "      <th>Date</th>\n",
       "      <th>DateAcquired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6210</td>\n",
       "      <td>Ferdinandsbrücke Project, Vienna, Austria (Ele...</td>\n",
       "      <td>1896</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7470</td>\n",
       "      <td>City of Music, National Superior Conservatory ...</td>\n",
       "      <td>1987</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7605</td>\n",
       "      <td>Villa near Vienna Project, Outside Vienna, Aus...</td>\n",
       "      <td>1903</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7056</td>\n",
       "      <td>The Manhattan Transcripts Project, New York, N...</td>\n",
       "      <td>1980</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7605</td>\n",
       "      <td>Villa, project, outside Vienna, Austria, Exter...</td>\n",
       "      <td>1903</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140843</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140844</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140845</th>\n",
       "      <td>3048</td>\n",
       "      <td>Page from Sketchbook #24, New York City</td>\n",
       "      <td>1954</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140846</th>\n",
       "      <td>3048</td>\n",
       "      <td>Front cover of Sketchbook #24, New York City</td>\n",
       "      <td>1954</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140847</th>\n",
       "      <td>3048</td>\n",
       "      <td>Front cover (verso) of Sketchbook #24, New Yor...</td>\n",
       "      <td>1954</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140848 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id                                              Title  Date  \\\n",
       "0       6210  Ferdinandsbrücke Project, Vienna, Austria (Ele...  1896   \n",
       "1       7470  City of Music, National Superior Conservatory ...  1987   \n",
       "2       7605  Villa near Vienna Project, Outside Vienna, Aus...  1903   \n",
       "3       7056  The Manhattan Transcripts Project, New York, N...  1980   \n",
       "4       7605  Villa, project, outside Vienna, Austria, Exter...  1903   \n",
       "...      ...                                                ...   ...   \n",
       "140843  3048            Page from Sketchbook #24, New York City  1954   \n",
       "140844  3048            Page from Sketchbook #24, New York City  1954   \n",
       "140845  3048            Page from Sketchbook #24, New York City  1954   \n",
       "140846  3048       Front cover of Sketchbook #24, New York City  1954   \n",
       "140847  3048  Front cover (verso) of Sketchbook #24, New Yor...  1954   \n",
       "\n",
       "       DateAcquired  \n",
       "0              1996  \n",
       "1              1995  \n",
       "2              1997  \n",
       "3              1995  \n",
       "4              1997  \n",
       "...             ...  \n",
       "140843         2020  \n",
       "140844         2020  \n",
       "140845         2020  \n",
       "140846         2020  \n",
       "140847         2020  \n",
       "\n",
       "[140848 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(MoMA_artworks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d2cd651",
   "metadata": {},
   "source": [
    "## Tate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "583d9d02",
   "metadata": {},
   "source": [
    "### Missing values\n",
    "Again, let us replace missing values and strings indicating lack of information with zeros, thus obtaining a dataframe filled with coherent data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc9b14f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_artists.fillna(value='0', inplace=True)\n",
    "Tate_artworks.fillna(value='0', inplace=True)\n",
    "Tate_artworks['Date'].replace(to_replace=['no date','c'], value='0', inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd4d3f8d",
   "metadata": {},
   "source": [
    "### Dates\n",
    "Artworks acquisition dates and artworks creation dates are represented as floats (e.g. 1997.0). We convert them to the YYYY format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d36274ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanDatesTate(date):\n",
    "    if '.' in date:\n",
    "        date = date.split('.')[0] \n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "695370e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_artworks[\"Date\"] = Tate_artworks[\"Date\"].astype(str)\n",
    "Tate_artworks[\"Date\"] = Tate_artworks[\"Date\"].apply(cleanDatesTate)\n",
    "Tate_artworks[\"DateAcquired\"] = Tate_artworks[\"DateAcquired\"].astype(str)\n",
    "Tate_artworks[\"DateAcquired\"] = Tate_artworks[\"DateAcquired\"].apply(cleanDatesTate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c68b5f5",
   "metadata": {},
   "source": [
    "### Artists' names\n",
    "In both Tate dataframes artists' names are in the form `Surname, Name`. For clarity purposes, we decided to normalise them as `Name Surname`, and wrote a code to do so. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e5c26f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanArtistsNames(name):\n",
    "    if ',' in name:\n",
    "        name= name.split(',')\n",
    "        name[0], name[1] = name[1], name[0]\n",
    "        name = ' '.join(name)\n",
    "    return name.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a325f329",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_artists[\"Name\"] = Tate_artists[\"Name\"].apply(cleanArtistsNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdaf8457",
   "metadata": {},
   "source": [
    "### Nationalities\n",
    "In Tate's dataframe about artists, nationalities are often in the form `city, country`(e.g. 'Philadelphia, United States') and sometimes just indicate a city (e.g. 'Wimbledon'). <br>\n",
    "Finally, all countries' names are in their original form (e.g. 'Nihon' for 'Japan'). <br>\n",
    "We normalised it indicating, for each artist, its country of origin, in English. <br>\n",
    "We looked for all diverging values and replaced them one by one through a script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fb1374e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanNationalitiesTate(naz): \n",
    "    if ',' in naz: \n",
    "        naz = naz.split(',')[1] \n",
    "    if naz == 'Blackheath': \n",
    "        naz= naz.replace('Blackheath', 'United Kingdom') \n",
    "    if naz == 'London': \n",
    "        naz= naz.replace('London', 'United Kingdom') \n",
    "    if naz == 'Kensington': \n",
    "        naz= naz.replace('Kensington', 'United Kingdom') \n",
    "    if naz == 'Chung-hua Min-kuo': \n",
    "        naz= naz.replace('Chung-hua Min-kuo', 'Taiwan') \n",
    "    if naz == 'Solothurn': \n",
    "        naz= naz.replace('Solothurn', 'Schweiz') \n",
    "    if naz == 'Melmerby': \n",
    "        naz= naz.replace('Melmerby', 'United Kingdom') \n",
    "    if naz == 'Montserrat': \n",
    "        naz= naz.replace('Montserrat', 'España') \n",
    "    if naz == 'Canterbury': \n",
    "        naz= naz.replace('Canterbury', 'United Kingdom') \n",
    "    if naz == 'Staten Island': \n",
    "        naz= naz.replace('Staten Island', 'United States') \n",
    "    if naz == 'Epsom': \n",
    "        naz= naz.replace('Epsom', 'United Kingdom') \n",
    "    if naz == 'Plymouth': \n",
    "        naz= naz.replace('Plymouth', 'United Kingdom') \n",
    "    if naz == 'Wimbledon': \n",
    "        naz= naz.replace('Wimbledon', 'United Kingdom') \n",
    "    if naz == 'Edinburgh': \n",
    "        naz= naz.replace('Edinburgh', 'United Kingdom') \n",
    "    if naz == 'Beckington': \n",
    "        naz= naz.replace('Beckington', 'United Kingdom') \n",
    "    if naz == 'Hertfordshire': \n",
    "        naz= naz.replace('Hertfordshire', 'United Kingdom') \n",
    "    if naz == 'Isle of Man': \n",
    "        naz= naz.replace('Isle of Man', 'United Kingdom') \n",
    "    if naz == 'Bristol': \n",
    "        naz= naz.replace('Bristol', 'United Kingdom') \n",
    "    if naz == 'Liverpool': \n",
    "        naz= naz.replace('Liverpool', 'United Kingdom') \n",
    "    if naz == 'Braintree': \n",
    "        naz= naz.replace('Braintree', 'United Kingdom') \n",
    "    if naz == 'Stoke on Trent': \n",
    "        naz= naz.replace('Stoke on Trent', 'United Kingdom') \n",
    "    if naz == 'Rochdale': \n",
    "        naz= naz.replace('Rochdale', 'United Kingdom') \n",
    "    if 'D.C.' in naz: \n",
    "        naz= naz.replace('D.C.', 'Colombia') \n",
    "    if 'Otok' in naz: \n",
    "        naz= naz.replace('Otok', 'Hrvatska') \n",
    "    if 'Département de la' in naz: \n",
    "        naz= naz.replace('Département de la', 'France') \n",
    "    if naz == 'Niederschlesien': \n",
    "        naz= naz.replace('Niederschlesien', 'Polska') \n",
    "    if naz == 'Perth': \n",
    "        naz= naz.replace('Perth', 'Australia') \n",
    "    if naz == 'Bermondsey': \n",
    "        naz= naz.replace('Bermondsey', 'United Kingdom') \n",
    "    if naz == 'Egremont': \n",
    "        naz= naz.replace('Egremont', 'United Kingdom') \n",
    "    if naz == 'Charlotte Amalie': \n",
    "        naz= naz.replace('Charlotte Amalie', 'United States') \n",
    "    if naz == 'Charlieu': \n",
    "        naz= naz.replace('Charlieu', 'France') \n",
    "    if naz == 'Stockholm': \n",
    "        naz= naz.replace('Stockholm', 'Sverige') \n",
    "    if naz == 'Auteuil': \n",
    "        naz= naz.replace('Auteuil', 'France') \n",
    " \n",
    "    if 'Polska' in naz: \n",
    "        naz = naz.replace('Polska', 'Poland') \n",
    "    if \"Yisra'el\" in naz: \n",
    "        naz = naz.replace(\"Yisra'el\", 'Israel') \n",
    "    if 'Deutschland' in naz: \n",
    "        naz = naz.replace('Deutschland', 'Germany') \n",
    "    if 'Schweiz' in naz: \n",
    "        naz = naz.replace('Schweiz', 'Switzerland') \n",
    "    if 'Suomi' in naz: \n",
    "        naz = naz.replace('Suomi', 'Finland') \n",
    "    if 'Zhonghua' in naz: \n",
    "        naz = naz.replace('Zhonghua', 'China') \n",
    "    if 'Türkiye' in naz: \n",
    "        naz = naz.replace('Türkiye', 'Turkey') \n",
    "    if 'Al-‘Iraq' in naz: \n",
    "        naz = naz.replace('Al-‘Iraq', 'Iraq') \n",
    "    if 'België' in naz: \n",
    "        naz = naz.replace('België', 'Belgium') \n",
    "    if 'Rossiya' in naz: \n",
    "        naz = naz.replace('Rossiya', 'Russia') \n",
    "    if 'Nihon' in naz: \n",
    "        naz = naz.replace('Nihon', 'Japan') \n",
    "    if 'Éire' in naz: \n",
    "        naz = naz.replace('Éire', 'Ireland') \n",
    "    if 'Österreich' in naz: \n",
    "        naz = naz.replace('Österreich', 'Austria') \n",
    "    if 'Saint Hélier' in naz: \n",
    "        naz = naz.replace('Saint Hélier', 'United Kingdom') \n",
    "    if 'Ceská Republik' in naz: \n",
    "        naz = naz.replace('Ceská Republik', 'Czech Republic') \n",
    "    if 'Ukrayina' in naz: \n",
    "        naz = naz.replace('Ukrayina', 'Ukraine') \n",
    "    if 'Ellás' in naz: \n",
    "        naz = naz.replace('Ellás', 'Greece') \n",
    "    if 'Latvija ' in naz: \n",
    "        naz = naz.replace('Latvija ', 'Latvia') \n",
    "    if 'Douglas' in naz: \n",
    "        naz = naz.replace('Douglas', 'United Kingdom') \n",
    "    if 'România' in naz: \n",
    "        naz = naz.replace('România', 'Romania') \n",
    "    if 'Sverige' in naz: \n",
    "        naz = naz.replace('Sverige', 'Sweden') \n",
    "    if 'Bharat' in naz: \n",
    "        naz = naz.replace('Bharat', 'India')     \n",
    "    if 'España' in naz: \n",
    "        naz = naz.replace('España', 'Spain')   \n",
    "    if 'Magyarország' in naz: \n",
    "        naz = naz.replace('Magyarország', 'Hungery')  \n",
    "    if 'Slovenská Republika' in naz: \n",
    "        naz = naz.replace('Slovenská Republika', 'Slovenia')  \n",
    "        \n",
    "    return naz.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f40615c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_artists[\"placeOfBirth\"] = Tate_artists[\"placeOfBirth\"].apply(cleanNationalitiesTate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02d719ab",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>placeOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10093</td>\n",
       "      <td>Magdalena Abakanowicz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Edwin Austin Abbey</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2756</td>\n",
       "      <td>Berenice Abbott</td>\n",
       "      <td>United States</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Lemuel Francis Abbott</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>622</td>\n",
       "      <td>Ivor Abrahams</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3527</th>\n",
       "      <td>12542</td>\n",
       "      <td>Gilberto Zorio</td>\n",
       "      <td>Italia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3528</th>\n",
       "      <td>2186</td>\n",
       "      <td>Larry Zox</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3529</th>\n",
       "      <td>621</td>\n",
       "      <td>Francesco Zuccarelli</td>\n",
       "      <td>Italia</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530</th>\n",
       "      <td>2187</td>\n",
       "      <td>Ignacio Zuloaga</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531</th>\n",
       "      <td>2188</td>\n",
       "      <td>Aleksander Zyw</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3532 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id                   Name    placeOfBirth  Gender\n",
       "0     10093  Magdalena Abakanowicz          Poland  Female\n",
       "1         0     Edwin Austin Abbey   United States    Male\n",
       "2      2756        Berenice Abbott   United States  Female\n",
       "3         1  Lemuel Francis Abbott  United Kingdom    Male\n",
       "4       622          Ivor Abrahams  United Kingdom    Male\n",
       "...     ...                    ...             ...     ...\n",
       "3527  12542         Gilberto Zorio          Italia    Male\n",
       "3528   2186              Larry Zox   United States    Male\n",
       "3529    621   Francesco Zuccarelli          Italia    Male\n",
       "3530   2187        Ignacio Zuloaga           Spain    Male\n",
       "3531   2188         Aleksander Zyw          Poland    Male\n",
       "\n",
       "[3532 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Tate_artists)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd842f9",
   "metadata": {},
   "source": [
    "# Integration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcae5a66",
   "metadata": {},
   "source": [
    "## Tate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddd016bd",
   "metadata": {},
   "source": [
    "Some information is missing. In particular, missing information relates to **gender**, We proceed to integrate it via **Wikidata** (when possible) and manually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ffd3e7",
   "metadata": {},
   "source": [
    "1. We create a subset of the dataframe containing all and only the the rows in which the gender information is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc762bde",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>placeOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>5221</td>\n",
       "      <td>Anonymous</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>657</td>\n",
       "      <td>Shusaku Arakawa</td>\n",
       "      <td>Japan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>2202</td>\n",
       "      <td>born 1945; Mel Ramsden Art &amp; Language (Michael...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>17138</td>\n",
       "      <td>born 1939; David Bainbridge Art &amp; Language (Te...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>668</td>\n",
       "      <td>born 1939; Michael Baldwin Art &amp; Language (Ter...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>14825</td>\n",
       "      <td>Shelagh Wakely</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>18071</td>\n",
       "      <td>Richard Westmacott</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>11740</td>\n",
       "      <td>José Yalenti</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3509</th>\n",
       "      <td>15539</td>\n",
       "      <td>Marc Voge) Young-Hae Chang Heavy Industries (Y...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>13099</td>\n",
       "      <td>Jakub Julian Ziolkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>116 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id                                               Name placeOfBirth  \\\n",
       "70     5221                                          Anonymous            0   \n",
       "84      657                                    Shusaku Arakawa        Japan   \n",
       "105    2202  born 1945; Mel Ramsden Art & Language (Michael...            0   \n",
       "106   17138  born 1939; David Bainbridge Art & Language (Te...            0   \n",
       "107     668  born 1939; Michael Baldwin Art & Language (Ter...            0   \n",
       "...     ...                                                ...          ...   \n",
       "3319  14825                                     Shelagh Wakely            0   \n",
       "3386  18071                                 Richard Westmacott            0   \n",
       "3495  11740                                       José Yalenti            0   \n",
       "3509  15539  Marc Voge) Young-Hae Chang Heavy Industries (Y...            0   \n",
       "3522  13099                            Jakub Julian Ziolkowski            0   \n",
       "\n",
       "     Gender  \n",
       "70        0  \n",
       "84        0  \n",
       "105       0  \n",
       "106       0  \n",
       "107       0  \n",
       "...     ...  \n",
       "3319      0  \n",
       "3386      0  \n",
       "3495      0  \n",
       "3509      0  \n",
       "3522      0  \n",
       "\n",
       "[116 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tate_to_integrate = Tate_artists[Tate_artists['Gender']== '0']\n",
    "display(Tate_to_integrate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e3d9fa2",
   "metadata": {},
   "source": [
    "2. We exclude some entities to which a gender cannot be attributed (e.g., collective or anonymous artists)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cbe69a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Anonymous']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'born 1945; Mel Ramsden Art & Language (Michael Baldwin  born 1944)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'born 1939; David Bainbridge Art & Language (Terry Atkinson  born 1941; Michael Baldwin  born 1945; Harold Hurrell  born']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'born 1939; Michael Baldwin Art & Language (Terry Atkinson  born 1945)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != '1939-1993; Mel Ramsden Art & Language (Ian Burn  born 1944)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Atlas Group']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Black Audio Film Collective (John Akomfrah; Reece Auguis; Edward George; Lina Gopaul; Avril Johnson; David Lawson; Trevo']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Fionnuala and Leslie Boyd and Evans']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British (?) School']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British (?) School 19th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 17th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 16th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 17th or 18th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 18th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 19th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'British School 20th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Chinese School 18th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'French School 18th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'French School 19th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'International Local (Sarah Charlesworth; Joseph Kosuth; Anthony McCall)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Italian or German (?) School 17th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Langlands and Bell, Ben and Nikki']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Italian or German (?) School 17th century']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Ben and Nikki Langlands and Bell']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Lucy and Eegyudluk']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'France) M/M (Paris']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'T R Uthco (Doug Hall born 1944, Diane Andrews Hall born 1945, Jody Procter 1944-1998)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Art & Language (Ian Burn, 1939-1993; Mel Ramsden, born 1944)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Unknown']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Marc Voge) Young-Hae Chang Heavy Industries (Young-Hae Chang']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'K.O.S.']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Diane Andrews Hall born 1945 T R Uthco (Doug Hall born 1944  Jody Procter 1944-1998)']\n",
    "Tate_to_integrate = Tate_to_integrate[Tate_to_integrate['Name'] != 'Mel Ramsden Art & Language (Ian Burn  born 1944)']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca42d53",
   "metadata": {},
   "source": [
    "3. We proceed to search the artists' **Wikidata entities**: this will then allow us to look up for their gender.\n",
    "The SPARQL query you can read below searches for human individuals with a specific artistic occupation (photograpers, artists, graphic artists, painters, video artists, sculptors and visual artists). The `{}` placeholder will be replaced by the artist's name from the dataframe via python `format()` method.\n",
    "We directly apply the query to our dataframe through a function taking advantage of `qwikidata` (a Python package allowing to interact with Wikidata) and we insert the result in a new colum named `Artist Entity` and created on the fly.<br>\n",
    "Since Wikidata SPARQL endpoint does not support heavy queries, we search for one occupation at a time and create a CSV file for all the artists for which the corresponding wikidata entity was found (e.g., all photographers).<br>\n",
    "We then continue the research thoughout the rest of the dataframe, from which we progressively remove the rows for which we obtain the wikidata entity. We work on copies to avoid compromising the original file.<br>\n",
    "Finally, we integrate all profession-specific CSV files into one dataframe, which contains all the rows lacking gender information with the corresponding Wikidata entity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "12cc29eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the SPARQL query.\n",
    "artists_ids = \"\"\"\n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
    "SELECT DISTINCT ?artist\n",
    "WHERE {\n",
    "    ?artist wdt:P31 wd:Q5 .\n",
    "    ?artist wdt:P106 ?occupation\n",
    "                  FILTER (?occupation IN (wd:Q1281618) ) \n",
    "    ?artist rdfs:label ?o\n",
    "    FILTER regex(?o, \\\"^{}$\\\" )\n",
    "            FILTER (langMatches(lang(?o), \"EN\")).\n",
    "}\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44930cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function for applying the query to the dataframe and returning the wanted results.\n",
    "def find_artists_ids(name):\n",
    "    query = artists_ids.format(name.strip())\n",
    "    res = return_sparql_query_results(query_string=query)\n",
    "   \n",
    "    try:\n",
    "        wdt_uri = res['results']['bindings']['artist']['value']\n",
    "    except (IndexError, KeyError):\n",
    "        return \"\"\n",
    "    return wdt_uri.split(\"/\")[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2060c319",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the query,\n",
    "Tate_to_integrate[\"Artist Entity\"] = Tate_to_integrate[\"Name\"].apply(find_artists_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "023e6d87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a CSV file for profession, e.g., photographers, visual artists etc. \n",
    "copy = Tate_to_integrate.copy(deep=True) \n",
    "sculptors =  copy[copy['Artist Entity']!= ''] \n",
    "sculptors.to_csv('Sculptors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcc1fbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the query iteratively, changing the occupation\n",
    "to_integrate =  copy[copy['Artist Entity']== ''] \n",
    "to_integrate[\"Artist Entity\"] = to_integrate[\"Name\"].apply(find_artists_genders_from_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c12a985f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Integrate all CSV files in one dataframe\n",
    "Tate_artists_integrated = pd.concat(map(pd.read_csv, ['Artists.csv', 'Photographers.csv', 'Videoartists.csv', 'Graphicartists.csv', 'Painters.csv', 'Integratedmanually.csv']), ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aed46ba8",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>placeOfBirth</th>\n",
       "      <th>Artist Entity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>657</td>\n",
       "      <td>Shusaku Arakawa</td>\n",
       "      <td>0</td>\n",
       "      <td>Nagoya, Nihon</td>\n",
       "      <td>Q478264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14424</td>\n",
       "      <td>Kiyohiko Komura</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q64826662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16926</td>\n",
       "      <td>Len Lye</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q1288566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5672</td>\n",
       "      <td>Vladimir Mayakovsky</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q132964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18266</td>\n",
       "      <td>Mithu Sen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q43136922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>17129</td>\n",
       "      <td>Vara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2107</td>\n",
       "      <td>Dee Villers</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>17732</td>\n",
       "      <td>Francis Vivares</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q5493438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>11825</td>\n",
       "      <td>Imke Wagener</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>13099</td>\n",
       "      <td>Jakub Julian Ziolkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Q1267768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id                     Name  Gender   placeOfBirth Artist Entity\n",
       "0     657          Shusaku Arakawa       0  Nagoya, Nihon       Q478264\n",
       "1   14424          Kiyohiko Komura       0              0     Q64826662\n",
       "2   16926                  Len Lye       0              0      Q1288566\n",
       "3    5672      Vladimir Mayakovsky       0              0       Q132964\n",
       "4   18266                Mithu Sen       0              0     Q43136922\n",
       "..    ...                      ...     ...            ...           ...\n",
       "77  17129                     Vara       0              0             0\n",
       "78   2107              Dee Villers       0              0             0\n",
       "79  17732          Francis Vivares       0              0      Q5493438\n",
       "80  11825             Imke Wagener       0              0             0\n",
       "81  13099  Jakub Julian Ziolkowski       0              0      Q1267768\n",
       "\n",
       "[82 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Tate_artists_integrated)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7056b4a5",
   "metadata": {},
   "source": [
    "4. Once we have all possible Wikidata entities, we interrogate Wikidata to retrieve artists' gender.<br>\n",
    "We apply the following SPARQL query to our new dataframe and add the retrieved gender information in a new `gender` column, created on the fly (the information which could not be found on Wikidata was integrated manually)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8e47681d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define the SPARQL query.\n",
    "artists_genders = \"\"\" \n",
    "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> \n",
    "PREFIX wd: <http://www.wikidata.org/entity/> \n",
    "SELECT DISTINCT (SAMPLE(?genderLabel) AS ?genderL)\n",
    "WHERE {{ \n",
    "     wd:{}  wdt:P21 ?gender . \n",
    "     ?gender rdfs:label ?genderLabel\n",
    "    FILTER (langMatches(lang(?genderLabel), \"EN\"))\n",
    "}} \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c71151be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the function for applying the query to the dataframe and returning the wanted results.\n",
    "def find_artists_genders(wikiId): \n",
    "    query = artists_genders.format(wikiId.strip()) \n",
    "    res = return_sparql_query_results(query_string=query) \n",
    "    \n",
    "    try: \n",
    "        gender= res['results']['bindings']['genderL']['value'] \n",
    "    except (IndexError, KeyError, JSONDecodeError, ChunkedEncodingError): \n",
    "        return \"\" \n",
    "    return gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae10c314",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the query.\n",
    "Tate_artists_integrated['Gender'] = ArtistIntegrated['Artist Entity'].apply(find_artists_genders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e44b26dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Manually integrate on a CSV file the missing information.\n",
    "Tate_artists_with_gender = pd.read_csv('ArtistIntegratedManually.csv')\n",
    "Tate_artists_with_gender[\"Id\"] = Tate_artists_with_gender['Id'].astype(str)\n",
    "Tate_artists_with_gender = Tate_artists_with_gender[['Id', 'Name', 'placeOfBirth', 'Gender' ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8570d5f5",
   "metadata": {},
   "source": [
    "5. Finally, we can add the dataframe with integrated information to the the dataframe already containing gender data (excluding collective and anonym artists). We will add the acquisition year from each artists by merging it to the arworks dataframe: this will allow us for further analyses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "39389660",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_gender = Tate_artists[Tate_artists['Gender'] != '0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01f9b3ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_final = Tate_gender.append(Tate_artists_with_gender)\n",
    "# Wikidata results are lowercase, let us capitalize them and count occurrences for each gender.\n",
    "Tate_final['Gender'].replace(to_replace=['male'], value='Male', inplace= True)\n",
    "Tate_final['Gender'].replace(to_replace=['female'], value='Female', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c74f3cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_acquisitions = pd.merge(Tate_final, Tate_artworks[['Id', 'DateAcquired']], on='Id', how='left')\n",
    "Tate_acquisitions.fillna(value='0', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "000bf3c7",
   "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</th>\n",
       "      <th>Name</th>\n",
       "      <th>placeOfBirth</th>\n",
       "      <th>Gender</th>\n",
       "      <th>DateAcquired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10093</td>\n",
       "      <td>Magdalena Abakanowicz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Female</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10093</td>\n",
       "      <td>Magdalena Abakanowicz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Female</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10093</td>\n",
       "      <td>Magdalena Abakanowicz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Female</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10093</td>\n",
       "      <td>Magdalena Abakanowicz</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Female</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Edwin Austin Abbey</td>\n",
       "      <td>United States</td>\n",
       "      <td>Male</td>\n",
       "      <td>1924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68768</th>\n",
       "      <td>17129</td>\n",
       "      <td>Vara</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68769</th>\n",
       "      <td>2107</td>\n",
       "      <td>Dee Villers</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68770</th>\n",
       "      <td>17732</td>\n",
       "      <td>Francis Vivares</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68771</th>\n",
       "      <td>11825</td>\n",
       "      <td>Imke Wagener</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68772</th>\n",
       "      <td>13099</td>\n",
       "      <td>Jakub Julian Ziolkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68773 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id                     Name   placeOfBirth  Gender DateAcquired\n",
       "0      10093    Magdalena Abakanowicz         Poland  Female         2009\n",
       "1      10093    Magdalena Abakanowicz         Poland  Female         2009\n",
       "2      10093    Magdalena Abakanowicz         Poland  Female         2009\n",
       "3      10093    Magdalena Abakanowicz         Poland  Female         2009\n",
       "4          0       Edwin Austin Abbey  United States    Male         1924\n",
       "...      ...                      ...            ...     ...          ...\n",
       "68768  17129                     Vara              0       0            0\n",
       "68769   2107              Dee Villers              0       0         1975\n",
       "68770  17732          Francis Vivares              0    Male            0\n",
       "68771  11825             Imke Wagener              0  Female            0\n",
       "68772  13099  Jakub Julian Ziolkowski              0    Male            0\n",
       "\n",
       "[68773 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Tate_acquisitions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424e4796",
   "metadata": {},
   "source": [
    "# Exploration\n",
    "We can now start exploring our Museums, to get to know them better through available data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cfe32c",
   "metadata": {},
   "source": [
    "## How many artworks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1e765f0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total artworks at MoMA : 140848\n",
      "Total artworks at Tate : 69201\n"
     ]
    }
   ],
   "source": [
    "museums=[MoMA_artworks, Tate_artworks]\n",
    "names = ['MoMA','Tate']\n",
    "for museum in museums:\n",
    "    selected_rows = museum[~museum['Title'].isnull()]\n",
    "    name = names.pop(0)\n",
    "    print(\"Total artworks at\", name, \":\", len(selected_rows.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b41fb02",
   "metadata": {},
   "source": [
    "## When do artworks date back?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d9ec499b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most ancient artwork at MoMA dates back to 1768\n",
      "Most recent artwork at MoMA dates back to 2022\n",
      "Most ancient artwork at Tate dates back to 1545\n",
      "Most recent artwork at Tate dates back to 2012\n"
     ]
    }
   ],
   "source": [
    "museums=[MoMA_artworks, Tate_artworks]\n",
    "names = ['MoMA','Tate']\n",
    "for museum in museums:\n",
    "    museum[\"Date\"] = museum[\"Date\"].astype(int)\n",
    "    museum.sort_values(by=['Date'], inplace=True)\n",
    "    museumWithoutZeros = museum[museum['Date'] != 0]\n",
    "    firstDate = museumWithoutZeros['Date'].iat[0]\n",
    "    lastDate = museumWithoutZeros['Date'].iat[-1]\n",
    "    name = names.pop(0)\n",
    "    print(\"Most ancient artwork at\", name, \"dates back to\",firstDate )\n",
    "    print(\"Most recent artwork at\", name, \"dates back to\",lastDate )    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c86fe4f",
   "metadata": {},
   "source": [
    "## When were artworks acquired?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "222263bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most ancient acquisition at MoMA dates back to 1929\n",
      "Most recent acquisition at MoMA dates back to 2022\n",
      "Most ancient acquisition at Tate dates back to 1823\n",
      "Most recent acquisition at Tate dates back to 2013\n"
     ]
    }
   ],
   "source": [
    "museums=[MoMA_artworks, Tate_artworks]\n",
    "names = ['MoMA','Tate']\n",
    "for museum in museums:\n",
    "    museum[\"DateAcquired\"] = museum[\"DateAcquired\"].astype(int)\n",
    "    museum.sort_values(by=['DateAcquired'], inplace=True)\n",
    "    museumWithoutZeros = museum[museum['DateAcquired'] != 0]\n",
    "    firstDate = museumWithoutZeros['DateAcquired'].iat[0]\n",
    "    lastDate = museumWithoutZeros['DateAcquired'].iat[-1]\n",
    "    name = names.pop(0)\n",
    "    print(\"Most ancient acquisition at\", name, \"dates back to\",firstDate )\n",
    "    print(\"Most recent acquisition at\", name, \"dates back to\",lastDate )    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37a0e216",
   "metadata": {},
   "source": [
    "## Which periods are represented at the Museums? In which proportion?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24297033",
   "metadata": {},
   "source": [
    "### MoMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "500cb56e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'18th': 87, '19th': 6902, '20th': 116283, '21th': 10656}\n"
     ]
    }
   ],
   "source": [
    "MoMA_artworks.to_csv('MoMA_artworks.csv') \n",
    "with open('MoMA_artworks.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    centuries_MoMa = {'18th': 0, '19th': 0, '20th': 0, '21th': 0}\n",
    "    for item in reader: \n",
    "        if int(item['Date']) in range (1700,1800): \n",
    "            centuries_MoMa['18th'] += 1   \n",
    "        if int(item['Date']) in range (1800,1900): \n",
    "            centuries_MoMa['19th'] += 1\n",
    "        if int(item['Date']) in range (1900,2000): \n",
    "            centuries_MoMa['20th'] += 1  \n",
    "        if int(item['Date']) in range (2000,2011): \n",
    "            centuries_MoMa['21th'] += 1    \n",
    " \n",
    "print(centuries_MoMa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "00702099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18th 0.06496027716384924 %\n",
      "19th 5.153515321665372 %\n",
      "20th 86.82501045337794 %\n",
      "21th 7.956513947792844 %\n"
     ]
    }
   ],
   "source": [
    "tot_centuries_MoMa = centuries_MoMa['18th'] + centuries_MoMa['19th'] + centuries_MoMa['20th'] + centuries_MoMa['21th'] \n",
    "for el in centuries_MoMa:\n",
    "    percentage = (centuries_MoMa[el]/tot_centuries_MoMa)*100 \n",
    "    print (el, percentage,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66075f58",
   "metadata": {},
   "source": [
    "### Tate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cd9e20a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'16th': 15, '17th': 87, '18th': 4364, '19th': 39509, '20th': 18111, '21th': 1643}\n"
     ]
    }
   ],
   "source": [
    "Tate_artworks.to_csv('Tate_artworks.csv') \n",
    "with open('Tate_artworks.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    centuries_Tate = {'16th':0, '17th':0, '18th': 0, '19th': 0, '20th': 0, '21th': 0}\n",
    "    for item in reader: \n",
    "        if int(item['Date']) in range (1500,1600): \n",
    "            centuries_Tate['16th'] += 1 \n",
    "        if int(item['Date']) in range (1600,1700): \n",
    "            centuries_Tate['17th'] += 1 \n",
    "        if int(item['Date']) in range (1700,1800): \n",
    "            centuries_Tate['18th'] += 1   \n",
    "        if int(item['Date']) in range (1800,1900): \n",
    "            centuries_Tate['19th'] += 1\n",
    "        if int(item['Date']) in range (1900,2000): \n",
    "            centuries_Tate['20th'] += 1  \n",
    "        if int(item['Date']) in range (2000,2011): \n",
    "            centuries_Tate['21th'] += 1    \n",
    " \n",
    "print(centuries_Tate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1667000b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16th 0.02353716518382526 %\n",
      "17th 0.1365155580661865 %\n",
      "18th 6.847745924147562 %\n",
      "19th 61.995323949850146 %\n",
      "20th 28.418773242950618 %\n",
      "21th 2.57810415980166 %\n"
     ]
    }
   ],
   "source": [
    "tot_centuries_Tate = centuries_Tate['16th']+ centuries_Tate['17th'] + centuries_Tate['18th'] + centuries_Tate['19th'] + centuries_Tate['20th'] + centuries_Tate['21th'] \n",
    "for el in centuries_Tate:\n",
    "    percentage = (centuries_Tate[el]/tot_centuries_Tate)*100 \n",
    "    print (el, percentage,'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed72b5fd",
   "metadata": {},
   "source": [
    "## Artists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b31d20e0",
   "metadata": {},
   "source": [
    "For examining artist-related issues, we rely on the two CSV files from the Museums containing information about them, which we already transformed into dataframes (Tate_artists and MoMA_artists). <br> \n",
    "In doing so, we avoid duplicates (the same artists may have more than one artwork in the same museum)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e343eee8",
   "metadata": {},
   "source": [
    "### How many artists?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a5df735a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of artists at MoMA 15243\n"
     ]
    }
   ],
   "source": [
    "print('Total number of artists at MoMA', len(MoMA_artists))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d8d0ff4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of artists at Tate 3532\n"
     ]
    }
   ],
   "source": [
    "print('Total number of artists at Tate', len(Tate_artists))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe6535c",
   "metadata": {},
   "source": [
    "### Artists' gender: which is the most represented overall?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2422ccc8",
   "metadata": {},
   "source": [
    "### Tate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "737201a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      2943\n",
       "Female     534\n",
       "0           10\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tate_acquisitions_dropped = Tate_acquisitions.drop_duplicates(subset='Name', keep=\"first\")\n",
    "Tate_acquisitions_dropped['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "992af91a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male artists' percentage is: 85 %; Female artists' percentage is: 15 %\n"
     ]
    }
   ],
   "source": [
    "genderCount = {'Male': 2954, 'Female': 534}\n",
    "tot = len(Tate_acquisitions_dropped)\n",
    "print (\"Male artists' percentage is:\", round((genderCount['Male']/tot)*100),'%;', \"Female artists' percentage is:\", round((genderCount['Female']/tot)*100),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10536977",
   "metadata": {},
   "source": [
    "### MoMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6433bcc5",
   "metadata": {},
   "source": [
    "As for Tate, let us analyse the frequency of male and female artists in the collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f96c489f",
   "metadata": {},
   "outputs": [],
   "source": [
    "MoMA_artists['Gender'].replace(to_replace=['male'], value='Male', inplace= True)\n",
    "MoMA_artists['Gender'].replace(to_replace=['female'], value='Female', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "11a9867d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male          9732\n",
       "0             3165\n",
       "Female        2343\n",
       "Non-Binary       2\n",
       "Non-binary       1\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MoMA_artists['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e72c6567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male artists' percentage is: 64 %; Female artists' percentage is: 15 %\n"
     ]
    }
   ],
   "source": [
    "genderCount = {'Male': 9732, 'Female': 2343}\n",
    "tot = len(MoMA_artists)\n",
    "print (\"Male artists' percentage is:\", round((genderCount['Male']/tot)*100),'%;', \"Female artists' percentage is:\", round((genderCount['Female']/tot)*100),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497ef99e",
   "metadata": {},
   "source": [
    "## Focus: most represented women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "acbdadca",
   "metadata": {},
   "outputs": [],
   "source": [
    "Tate_Women= Tate_acquisitions[Tate_acquisitions['Gender']== 'Female']\n",
    "Tate_Women['Name'].value_counts().to_csv('donneTate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "acf5ab03",
   "metadata": {},
   "outputs": [],
   "source": [
    "MomA_Women= MoMA_acquisitions[MoMA_acquisitions['Gender']== 'Female']\n",
    "MomA_Women['Name'].value_counts().to_csv('donneMoMA.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059444fd",
   "metadata": {},
   "source": [
    "### Nationalities: which are the most represented nationalities overall?\n",
    "As for genres, we examine the distribution of artists' nationalities in out datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a435e232",
   "metadata": {},
   "source": [
    "### MoMa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "16e83f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "American      5181\n",
       "0             2472\n",
       "German         965\n",
       "British        860\n",
       "French         847\n",
       "              ... \n",
       "Persian          1\n",
       "Ugandan          1\n",
       "Cypriot          1\n",
       "Mozambican       1\n",
       "Tanzanian        1\n",
       "Name: Nationality, Length: 120, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MoMA_artists['Nationality'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5551facb",
   "metadata": {},
   "source": [
    "### Tate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "81d26eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United Kingdom    1522\n",
       "0                  492\n",
       "United States      341\n",
       "France             160\n",
       "Germany            142\n",
       "                  ... \n",
       "Malaysia             1\n",
       "Costa Rica           1\n",
       "Iraq                 1\n",
       "Eesti                1\n",
       "Tanzania             1\n",
       "Name: placeOfBirth, Length: 99, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tate_artists['placeOfBirth'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5625d37b",
   "metadata": {},
   "source": [
    "# Acquisition criteria."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20702e1",
   "metadata": {},
   "source": [
    "## 1.  In which years are artists' works mostly acquired?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3abe7b0",
   "metadata": {},
   "source": [
    "### Year by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d626f9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1964    12828\n",
       "2008     7204\n",
       "1968     6894\n",
       "0        6682\n",
       "2001     4170\n",
       "        ...  \n",
       "1933       93\n",
       "1932       18\n",
       "1929        9\n",
       "1930        7\n",
       "1931        3\n",
       "Name: DateAcquired, Length: 95, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MoMA_artworks['DateAcquired'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ee20ea98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1856    37893\n",
       "1997     3706\n",
       "1975     3046\n",
       "2009     1364\n",
       "1979     1166\n",
       "        ...  \n",
       "1873        1\n",
       "1843        1\n",
       "1863        1\n",
       "1842        1\n",
       "1855        1\n",
       "Name: DateAcquired, Length: 179, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tate_artworks['DateAcquired'].astype(str).astype(int)\n",
    "Tate_artworks['DateAcquired'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62c9d0cd",
   "metadata": {},
   "source": [
    "### Every ten years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d7abcc",
   "metadata": {},
   "source": [
    "Let us focus on the 20th century and analyse acquisitions from a larger perspective: not year by year, but for every ten years."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe6680e",
   "metadata": {},
   "source": [
    "### MoMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3dcf4c53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MoMA: defaultdict(<class 'dict'>, {'1930s': 1794, '1940s': 7821, '1950s': 6396, '1960s': 30716, '1970s': 13196, '1980s': 10229, '1990s': 11446, '2000s': 26861})\n"
     ]
    }
   ],
   "source": [
    "MoMA_artworks.to_csv('MoMA_artworks.csv') \n",
    "with open('MoMA_artworks.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    years= defaultdict(dict) \n",
    "    for item in reader: \n",
    "        if int(item['DateAcquired']) in range(1928,1940): \n",
    "            if '1930s' not in years: \n",
    "                years['1930s'] = 1\n",
    "            else: \n",
    "                years['1930s'] += 1\n",
    "            \n",
    "        if int(item['DateAcquired']) in range(1940,1950): \n",
    "            if '1940s' not  in years: \n",
    "                years['1940s'] = 1\n",
    "            else: \n",
    "                years['1940s'] += 1\n",
    "     \n",
    "        if int(item['DateAcquired']) in range(1950,1960): \n",
    "            if '1950s' not in years: \n",
    "                    years['1950s'] = 1\n",
    "            else: \n",
    "                    years['1950s'] += 1\n",
    "\n",
    "        if int(item['DateAcquired']) in range(1960,1970): \n",
    "            if '1960s' not in years: \n",
    "                    years['1960s'] = 1\n",
    "            else: \n",
    "                    years['1960s'] += 1\n",
    "     \n",
    "        if int(item['DateAcquired']) in range(1970,1980): \n",
    "            if '1970s' not in years: \n",
    "                years['1970s'] = 1\n",
    "            else: \n",
    "                years['1970s'] += 1 \n",
    "        \n",
    "        if int(item['DateAcquired']) in range(1980,1990): \n",
    "            if '1980s' not in years: \n",
    "                years['1980s'] = 1\n",
    "            else: \n",
    "                years['1980s'] += 1 \n",
    "    \n",
    "        if int(item['DateAcquired']) in range(1990,2000): \n",
    "            if '1990s' not in years: \n",
    "                years['1990s'] = 1\n",
    "            else: \n",
    "                years['1990s'] += 1\n",
    "\n",
    "        if int(item['DateAcquired']) in range(2000,2011): \n",
    "            if '2000s' not in years: \n",
    "                   years['2000s'] = 1\n",
    "            else: \n",
    "                years['2000s'] += 1\n",
    "\n",
    "         \n",
    "     \n",
    "print('MoMA:', years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "694e0fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tate: defaultdict(<class 'dict'>, {'1900s': 414, '1910s': 617, '1920s': 1055, '1930s': 494, '1940s': 736, '1950s': 552, '1960s': 874, '1970s': 6357, '1980s': 5284, '1990s': 6449, '2000s': 5345})\n"
     ]
    }
   ],
   "source": [
    "Tate_artworks.to_csv('Tate_artworks.csv') \n",
    "with open('Tate_artworks.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    years= defaultdict(dict) \n",
    "    for item in reader: \n",
    "        if int(item['DateAcquired']) in range(1900,1910): \n",
    "            if '1900s' not in years: \n",
    "                years['1900s'] = 1\n",
    "            else: \n",
    "                years['1900s'] += 1\n",
    "    \n",
    "        if int(item['DateAcquired']) in range(1910,1920): \n",
    "            if '1910s' not in years: \n",
    "                years['1910s'] = 1\n",
    "            else: \n",
    "                years['1910s'] += 1\n",
    "    \n",
    "        if int(item['DateAcquired']) in range(1920,1930): \n",
    "            if '1920s' not in years: \n",
    "                years['1920s'] = 1\n",
    "            else: \n",
    "                years['1920s'] += 1\n",
    "         \n",
    "        if int(item['DateAcquired']) in range(1930,1940): \n",
    "            if '1930s' not in years: \n",
    "                years['1930s'] = 1\n",
    "            else: \n",
    "                years['1930s'] += 1\n",
    "        \n",
    "            \n",
    "        if int(item['DateAcquired']) in range(1940,1950): \n",
    "            if '1940s' not  in years: \n",
    "                years['1940s'] = 1\n",
    "            else: \n",
    "                years['1940s'] += 1\n",
    "     \n",
    "        if int(item['DateAcquired']) in range(1950,1960): \n",
    "            if '1950s' not in years: \n",
    "                    years['1950s'] = 1\n",
    "            else: \n",
    "                    years['1950s'] += 1\n",
    "\n",
    "        if int(item['DateAcquired']) in range(1960,1970): \n",
    "            if '1960s' not in years: \n",
    "                    years['1960s'] = 1\n",
    "            else: \n",
    "                    years['1960s'] += 1\n",
    "     \n",
    "        if int(item['DateAcquired']) in range(1970,1980): \n",
    "            if '1970s' not in years: \n",
    "                years['1970s'] = 1\n",
    "            else: \n",
    "                years['1970s'] += 1 \n",
    "        \n",
    "        if int(item['DateAcquired']) in range(1980,1990): \n",
    "            if '1980s' not in years: \n",
    "                years['1980s'] = 1\n",
    "            else: \n",
    "                years['1980s'] += 1 \n",
    "    \n",
    "        if int(item['DateAcquired']) in range(1990,2000): \n",
    "            if '1990s' not in years: \n",
    "                years['1990s'] = 1\n",
    "            else: \n",
    "                years['1990s'] += 1\n",
    "\n",
    "        if int(item['DateAcquired']) in range(2000,2011): \n",
    "            if '2000s' not in years: \n",
    "                   years['2000s'] = 1\n",
    "            else: \n",
    "                years['2000s'] += 1\n",
    "\n",
    "         \n",
    "     \n",
    "print('Tate:', years)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21012a99",
   "metadata": {},
   "source": [
    "## Gender Gap \n",
    "We have already analysed the total number of male and female artists. Let us now anlysed the gender of acquired artists in time. In this way, we will try to investigate the gender gap in our museum's collections and try to understand how it changes (if it does) throughout the years. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffd77772",
   "metadata": {},
   "source": [
    "### MoMa"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ba11b60",
   "metadata": {},
   "source": [
    "Number of female and male artists acquired every ten years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "265cfeb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1930s': {'Male': 184, 'Female': 16}, '1940s': {'Male': 697, 'Female': 98}, '1950s': {'Male': 858, 'Female': 93}, '1960s': {'Male': 1463, 'Female': 145}, '1970s': {'Male': 889, 'Female': 158}, '1980s': {'Male': 1142, 'Female': 211}, '1990s': {'Male': 926, 'Female': 280}, '2000s': {'Male': 1771, 'Female': 503}}\n"
     ]
    }
   ],
   "source": [
    "MoMA_acquisitions_dropped = MoMA_acquisitions.drop_duplicates(subset='Name', keep=\"first\")\n",
    "MoMA_acquisitions_dropped.to_csv('MoMA_acquisitions_dropped.csv')\n",
    "with open('MoMA_acquisitions_dropped.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    gender={'1930s': {'Male': 0, 'Female': 0}, '1940s': {'Male': 0, 'Female': 0}, '1950s': {'Male': 0, 'Female': 0}, '1960s': {'Male': 0, 'Female': 0}, '1970s': {'Male': 0, 'Female': 0}, '1980s': {'Male': 0, 'Female': 0}, '1990s': {'Male': 0, 'Female': 0}, '2000s': {'Male': 0, 'Female': 0}} \n",
    "    for item in reader: \n",
    "        if int(item['DateAcquired']) in range (1928,1940): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1930s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1930s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1940,1950): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1940s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1940s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1950,1960): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1950s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1950s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1960,1970): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1960s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1960s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1970,1980): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1970s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1970s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1980,1990): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1980s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1980s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (1990,2000): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1990s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1990s']['Male'] += 1 \n",
    "        if int(item['DateAcquired']) in range (2000,2011): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['2000s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['2000s']['Male'] += 1 \n",
    "    print(gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "705c4f23",
   "metadata": {},
   "source": [
    "Percentage of female-male artists every ten years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a3219a6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1930s Male 92 % Female 8 %\n",
      "1940s Male 88 % Female 12 %\n",
      "1950s Male 90 % Female 10 %\n",
      "1960s Male 91 % Female 9 %\n",
      "1970s Male 85 % Female 15 %\n",
      "1980s Male 84 % Female 16 %\n",
      "1990s Male 77 % Female 23 %\n",
      "2000s Male 78 % Female 22 %\n"
     ]
    }
   ],
   "source": [
    "for el in gender: \n",
    "    tot = gender[el]['Male'] + gender[el]['Female'] \n",
    "    percentage = (gender[el]['Male']/tot)*100 \n",
    "    print (el, 'Male', round(percentage),'%', 'Female', round(100-percentage),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2186da",
   "metadata": {},
   "source": [
    "## Tate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be0f8e8",
   "metadata": {},
   "source": [
    "Number of female and male artists acquired every ten years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "99e652fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'1900s': {'Male': 85, 'Female': 1}, '1910s': {'Male': 102, 'Female': 7}, '1920s': {'Male': 171, 'Female': 19}, '1930s': {'Male': 127, 'Female': 27}, '1940s': {'Male': 100, 'Female': 13}, '1950s': {'Male': 136, 'Female': 12}, '1960s': {'Male': 214, 'Female': 17}, '1970s': {'Male': 481, 'Female': 67}, '1980s': {'Male': 285, 'Female': 44}, '1990s': {'Male': 318, 'Female': 72}, '2000s': {'Male': 402, 'Female': 145}}\n"
     ]
    }
   ],
   "source": [
    "Tate_acquisitions_dropped.to_csv('Tate_acquisitions_dropped.csv')\n",
    "with open('Tate_acquisitions_dropped.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    gender={'1900s':{'Male': 0, 'Female': 0}, '1910s': {'Male': 0, 'Female': 0}, '1920s':{'Male': 0, 'Female': 0}, '1930s': {'Male': 0, 'Female': 0}, '1940s': {'Male': 0, 'Female': 0}, '1950s': {'Male': 0, 'Female': 0}, '1960s': {'Male': 0, 'Female': 0}, '1970s': {'Male': 0, 'Female': 0}, '1980s': {'Male': 0, 'Female': 0}, '1990s': {'Male': 0, 'Female': 0}, '2000s': {'Male': 0, 'Female': 0}} \n",
    "    for item in reader: \n",
    "        if int(item['DateAcquired']) in range(1900,1910): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1900s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1900s']['Male'] += 1 \n",
    "    \n",
    "        if int(item['DateAcquired']) in range(1910,1920): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1910s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1910s']['Male'] += 1 \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1920,1930): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1920s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1920s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1930,1940): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1930s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1930s']['Male'] += 1       \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1940,1950): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1940s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1940s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1950,1960): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1950s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1950s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1960,1970): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1960s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1960s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1970,1980): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1970s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1970s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1980,1990): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1980s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1980s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (1990,2000): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['1990s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['1990s']['Male'] += 1 \n",
    "                \n",
    "        if int(item['DateAcquired']) in range (2000,2011): \n",
    "            if (item['Gender'] == 'Female'):  \n",
    "                gender['2000s']['Female'] += 1 \n",
    "            else: \n",
    "                gender['2000s']['Male'] += 1 \n",
    "                \n",
    "    print(gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d6e590",
   "metadata": {},
   "source": [
    "Percentage of female-male artists acquired every ten years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5074bad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1900s Male 99 % Female 1 %\n",
      "1910s Male 94 % Female 6 %\n",
      "1920s Male 90 % Female 10 %\n",
      "1930s Male 82 % Female 18 %\n",
      "1940s Male 88 % Female 12 %\n",
      "1950s Male 92 % Female 8 %\n",
      "1960s Male 93 % Female 7 %\n",
      "1970s Male 88 % Female 12 %\n",
      "1980s Male 87 % Female 13 %\n",
      "1990s Male 82 % Female 18 %\n",
      "2000s Male 73 % Female 27 %\n"
     ]
    }
   ],
   "source": [
    "for el in gender: \n",
    "    tot = gender[el]['Male'] + gender[el]['Female'] \n",
    "    percentage = (gender[el]['Male']/tot)*100 \n",
    "    print (el, 'Male', round(percentage),'%', 'Female', round(100-percentage),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952ad657",
   "metadata": {},
   "source": [
    "## Nationalities "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d85013",
   "metadata": {},
   "source": [
    "Does nationality affect the artists' selection? Does acquisition campaigns show different tendencies and patterns throughout the years, when it comes to the artists' nationality?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d33d24c",
   "metadata": {},
   "source": [
    "### MoMA\n",
    "For every ten years, we count the nationalities' frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ec41ea09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'1980s': {'American': 715, 'Danish': 14, 'Estonian': 2, 'Swedish': 11, 'Finnish': 8, 'British': 100, 'Romanian': 2, 'French': 100, 'Belgian': 12, 'Dutch': 34, 'Norwegian': 4, 'Czech': 14, 'Austrian': 24, 'Italian': 37, 'Japanese': 72, 'German': 112, 'Russian': 18, 'Swiss': 54, 'Canadian': 33, 'Congolese': 1, 'Brazilian': 2, 'Hungarian': 9, 'Polish': 17, 'Icelandic': 1, 'Australian': 6, 'Croatian': 3, 'Spanish': 13, 'Slovak': 1, 'Cuban': 4, 'Mexican': 10, 'Greek': 1, 'Chinese': 1, 'Native American': 2, 'Chilean': 3, 'Nationality unknown': 5, 'Israeli': 7, 'Argentine': 1, 'Colombian': 2, 'Luxembourger': 1, 'Venezuelan': 1, 'Portuguese': 1, 'Peruvian': 3, 'Indian': 1, 'Haitian': 1, 'Moroccan': 1, '0': 14, 'Latvian': 2, 'Georgian': 1, 'Irish': 1, 'Ukrainian': 1, 'Puerto Rican': 1}, '1960s': {'Spanish': 21, '0': 15, 'American': 645, 'Italian': 95, 'French': 174, 'Japanese': 68, 'British': 71, 'Guatemalan': 2, 'Finnish': 4, 'Argentine': 37, 'Kuwaiti': 1, 'German': 146, 'Nationality unknown': 36, 'Chilean': 20, 'Swiss': 31, 'Danish': 12, 'Brazilian': 22, 'Austrian': 20, 'Canadian': 48, 'Swedish': 26, 'Israeli': 12, 'Portuguese': 1, 'Indian': 7, 'Romanian': 3, 'Dutch': 18, 'Uruguayan': 4, 'Polish': 29, 'Australian': 4, 'Belgian': 16, 'Peruvian': 11, 'Cuban': 6, 'Mexican': 15, 'Russian': 26, 'Czech': 8, 'Venezuelan': 7, 'Bolivian': 1, 'Slovenian': 5, 'Greek': 4, 'Panamanian': 2, 'Croatian': 4, 'Turkish': 2, 'Icelandic': 2, 'Ecuadorian': 2, 'Colombian': 5, 'Iranian': 4, 'Hungarian': 4, 'Serbian': 2, 'Canadian Inuit': 3, 'Costa Rican': 1, 'Paraguayan': 1, 'Haitian': 1, 'Zimbabwean': 3, 'South African': 1, 'Moroccan': 2, 'Norwegian': 1, 'Tanzanian': 1, 'Irish': 2, 'Sudanese': 1, 'Taiwanese': 1, 'Ethiopian': 1, 'Korean': 1, 'Chinese': 2}, '1970s': {'American': 590, 'British': 83, 'Polish': 7, 'Italian': 33, 'Romanian': 3, 'Russian': 17, 'Israeli': 13, 'Japanese': 62, 'Mexican': 15, 'Dutch': 22, 'Danish': 7, 'Chilean': 7, 'Finnish': 2, 'Australian': 12, 'French': 52, 'Swiss': 16, 'Ukrainian': 2, 'German': 56, 'Brazilian': 6, 'Argentine': 3, 'Slovenian': 5, 'Spanish': 13, 'Nationality unknown': 21, 'Colombian': 13, 'Irish': 2, 'Cuban': 4, 'Peruvian': 1, 'Canadian': 7, 'Belgian': 9, 'Turkish': 2, 'Greek': 2, 'Austrian': 9, 'Swedish': 2, 'Venezuelan': 14, 'South African': 1, 'Hungarian': 2, 'Norwegian': 1, 'Korean': 3, 'Czech': 8, '0': 3, 'Tunisian': 1, 'Uruguayan': 2, 'Serbian': 1, 'Panamanian': 1, 'Paraguayan': 1}, '1950s': {'American': 323, 'French': 163, 'Swedish': 16, 'Israeli': 4, 'Japanese': 96, 'Finnish': 9, 'German': 90, 'British': 48, '0': 16, 'Italian': 42, 'Swiss': 39, 'Hungarian': 3, 'Canadian': 9, 'Cuban': 3, 'Dutch': 25, 'Nationality unknown': 55, 'Spanish': 5, 'Brazilian': 13, 'Chilean': 3, 'Polish': 7, 'Belgian': 10, 'Mexican': 5, 'Haitian': 1, 'Algerian': 1, 'Argentine': 4, 'Venezuelan': 1, 'Guatemalan': 2, 'Austrian': 7, 'Danish': 14, 'Canadian Inuit': 2, 'Russian': 2, 'Serbian': 2, 'Korean': 2, 'Czech': 4, 'Nicaraguan': 1, 'Norwegian': 6, 'Turkish': 1, 'Slovenian': 1, 'Australian': 2, 'Colombian': 2, 'Tanzanian': 1, 'Irish': 1, 'Peruvian': 1, 'Icelandic': 1}, '1940s': {'Mexican': 32, 'American': 435, 'Palestinian': 1, 'Colombian': 3, 'Polish': 6, 'British': 57, '0': 22, 'Costa Rican': 1, 'Spanish': 6, 'Argentine': 15, 'Italian': 31, 'French': 55, 'German': 37, 'Haitian': 10, 'Brazilian': 9, 'Bolivian': 1, 'Swedish': 10, 'Cuban': 10, 'Peruvian': 2, 'Nationality unknown': 19, 'Swiss': 16, 'Dutch': 6, 'Greek': 2, 'Chilean': 2, 'Chinese': 3, 'Australian': 3, 'Belgian': 7, 'Czech': 3, 'Uruguayan': 3, 'Ecuadorian': 2, 'Austrian': 7, 'Japanese': 3, 'Romanian': 1, 'Finnish': 3, 'Danish': 3, 'Nicaraguan': 1, 'Guatemalan': 1, 'Russian': 13, 'Hungarian': 3, 'Canadian': 1, 'Native American': 1, 'Yugoslav': 1, 'Scottish': 1}, '1930s': {'Spanish': 19, 'American': 179, 'Palestinian': 1, 'British': 56, 'Chilean': 1, 'German': 18, 'French': 18, 'Nationality unknown': 5, 'Swiss': 6, '0': 5, 'Japanese': 2, 'Norwegian': 1, 'Russian': 3, 'Italian': 2, 'Mexican': 1, 'Hungarian': 1, 'Dutch': 3, 'Swedish': 1, 'Bolivian': 1, 'Czechoslovakian': 1}, '1990s': {'Swedish': 5, 'French': 70, 'Finnish': 7, 'American': 631, 'Italian': 53, 'Argentine': 7, 'Dutch': 37, 'Japanese': 68, 'Spanish': 29, 'German': 86, 'Swiss': 41, 'Austrian': 20, 'Colombian': 3, 'British': 85, 'Polish': 19, 'Icelandic': 2, 'Danish': 17, 'Greek': 1, 'Russian': 25, 'Mexican': 10, 'Portuguese': 2, 'Cuban': 6, 'Czech': 10, 'Israeli': 4, 'Belgian': 4, 'Norwegian': 4, 'Brazilian': 15, 'Azerbaijani': 1, '0': 15, 'Chilean': 8, 'Canadian': 12, 'Senegalese': 1, 'Indian': 2, 'Nationality unknown': 8, 'Thai': 1, 'Australian': 5, 'South African': 4, 'Iranian': 2, 'Peruvian': 1, 'Scottish': 1, 'Romanian': 2, 'Chinese': 6, 'Ukrainian': 2, 'Pakistani': 1, 'Korean': 1, 'Bahamian': 1, 'Malian': 1, 'Latvian': 1, 'Taiwanese': 1, 'Hungarian': 1, 'Uruguayan': 1, 'Czechoslovakian': 1}, '2000s': {'Finnish': 11, 'American': 701, 'Italian': 55, 'Ukrainian': 4, 'Czech': 16, 'British': 113, 'Russian': 68, 'German': 186, 'Austrian': 76, 'Japanese': 54, 'Spanish': 15, 'French': 80, 'Dutch': 45, 'Danish': 29, 'Nationality unknown': 22, 'Swedish': 13, 'Belgian': 18, 'Thai': 1, 'South African': 49, 'Lithuanian': 2, 'Australian': 6, 'Swiss': 44, 'Brazilian': 25, 'Pakistani': 3, 'Korean': 12, 'Polish': 18, 'Argentine': 15, 'Bahamian': 1, 'Cuban': 12, 'Iranian': 2, 'Canadian': 30, 'Bulgarian': 3, '0': 366, 'Bosnian': 4, 'Georgian': 2, 'Turkish': 10, 'Scottish': 13, 'Indian': 5, 'Croatian': 5, 'Chinese': 32, 'Hungarian': 10, 'Israeli': 17, 'Congolese': 1, 'Venezuelan': 4, 'Icelandic': 2, 'Peruvian': 5, 'Uruguayan': 1, 'Malian': 1, 'Colombian': 7, 'Irish': 3, 'Mexican': 11, 'Egyptian': 3, 'Luxembourger': 1, 'Kenyan': 1, 'Nigerian': 1, 'Taiwanese': 2, 'Norwegian': 6, 'Slovenian': 4, 'Albanian': 2, 'Slovak': 6, 'Romanian': 3, 'Azerbaijani': 1, 'Ivorian': 1, 'Malaysian': 2, 'Serbian': 1, 'Singaporean': 1, 'Namibian': 2, 'Ghanaian': 2, 'Afghan': 1, 'Lebanese': 1, 'Ugandan': 1, 'Zimbabwean': 1, 'Cameroonian': 1, 'New Zealander': 1, 'Welsh': 1}})\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict  \n",
    " \n",
    "with open('MoMA_acquisitions_dropped.csv', mode='r', encoding='utf-8') as csvfile: \n",
    "    reader = csv.DictReader(csvfile) \n",
    "    nationalities = defaultdict(dict) \n",
    "    for item in reader: \n",
    "        if int(item['DateAcquired']) in range (1928,1941): \n",
    "            if item['Nationality'] not in nationalities['1930s']: \n",
    "                nationalities['1930s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1930s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1940,1951): \n",
    "            if item['Nationality'] not in nationalities['1940s']: \n",
    "                nationalities['1940s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1940s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1950,1961): \n",
    "            if item['Nationality'] not in nationalities['1950s']: \n",
    "                nationalities['1950s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1950s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1960,1971): \n",
    "            if item['Nationality'] not in nationalities['1960s']: \n",
    "                nationalities['1960s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1960s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1970,1981): \n",
    "            if item['Nationality'] not in nationalities['1970s']: \n",
    "                nationalities['1970s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1970s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1980,1991): \n",
    "            if item['Nationality'] not in nationalities['1980s']: \n",
    "                nationalities['1980s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1980s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (1990,2001): \n",
    "            if item['Nationality'] not in nationalities['1990s']: \n",
    "                nationalities['1990s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['1990s'][item['Nationality']] += 1 \n",
    "        if int(item['DateAcquired']) in range (2000,2011): \n",
    "            if item['Nationality'] not in nationalities['2000s']: \n",
    "                nationalities['2000s'][item['Nationality']] = 1 \n",
    "            else: \n",
    "                nationalities['2000s'][item['Nationality']] += 1 \n",
    "         \n",
    "                 \n",
    " \n",
    "print(nationalities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1254ba42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'2000s': {'Poland': 9, 'United States': 99, 'Germany': 39, 'Finland': 1, 'China': 10, 'Iraq': 1, 'Russia': 2, 'United Kingdom': 150, 'Belgium': 5, 'México': 7, 'Perú': 3, 'Ukraine': 3, 'Îran': 5, 'Italia': 12, 'Venezuela': 4, 'Turkey': 2, 'France': 14, 'Israel': 5, 'Brasil': 17, 'Jugoslavija': 2, 'Uganda': 1, 'Norge': 1, 'Nederland': 4, 'South Africa': 6, '0': 26, 'Romania': 2, 'Argentina': 8, 'Cuba': 4, 'Canada': 9, 'Ireland': 4, 'Greece': 2, 'Colombia': 5, 'Latvija': 1, 'Sweden': 1, 'Chile': 2, 'Czech Republica': 4, 'Danmark': 5, 'Spain': 5, 'Austria': 4, 'Switzerland': 6, 'Pakistan': 1, 'Mehoz': 2, 'India': 3, 'Japan': 10, 'Bahamas': 1, 'Hungery': 3, 'Bangladesh': 1, 'Hrvatska': 2, 'Slovenia': 2, \"Taehan Min'guk\": 1, 'Zimbabwe': 1, 'Sri Lanka': 1, 'New Zealand': 4, 'Luxembourg': 1, 'Ísland': 1, 'Pilipinas': 1, 'Lietuva': 2, 'Australia': 2, 'Al-Lubnan': 3, 'Kenya': 1, \"Al-Jaza'ir\": 1, 'Lao': 1, 'Malta': 1, 'Panamá': 1, 'Misr': 1, 'Portugal': 3, 'Shqipëria': 1, 'Nicaragua': 1, 'Mauritius': 1, 'Bulgaria': 1, 'Indonesia': 1, 'Zambia': 1}, '1920s': {'United States': 4, 'United Kingdom': 130, 'France': 28, 'Italia': 3, 'Russia': 3, 'India': 2, 'Ireland': 3, 'Chile': 1, 'Danmark': 1, 'Australia': 2, 'Canada': 2, 'Nederland': 1, 'Romania': 1, 'Mauritius': 1, 'South Africa': 1, 'New Zealand': 1, 'Ukraine': 1, 'Sweden': 1, '0': 2, 'Switzerland': 1, 'Spain': 1}, '1970s': {'United Kingdom': 243, 'Switzerland': 4, 'Germany': 22, 'United States': 70, 'Belgium': 11, 'Italia': 11, 'Spain': 5, 'Nederland': 7, 'Canada': 16, 'France': 11, '0': 85, 'Austria': 6, 'South Africa': 4, 'New Zealand': 3, 'Australia': 9, 'Ireland': 5, 'India': 3, 'Venezuela': 1, 'Ukraine': 3, 'Hungery': 5, 'Greece': 1, 'Eesti': 1, 'Russia': 4, 'Romania': 3, 'Slovenia': 1, 'Belarus': 2, 'Armenia': 1, 'Poland': 6, 'Colombia': 2, 'Norge': 1, 'Czech Republica': 3, 'Myanmar': 1, 'Israel': 1, 'Brasil': 1, 'China': 4, 'Singapore': 1, 'Taiwan': 1, 'Chile': 1, 'Japan': 2, 'Sweden': 3, 'Sri Lanka': 1, 'México': 1, 'Misr': 2, 'Viet Nam': 1, 'Samoa': 1}, '1990s': {'Israel': 6, 'United Kingdom': 185, '0': 120, 'France': 16, 'Pakistan': 2, 'Poland': 5, 'United States': 22, 'Germany': 26, 'Italia': 8, 'Barbados': 1, 'Ireland': 7, 'Hungery': 1, 'Sweden': 2, 'Nederland': 3, 'Belgium': 4, 'South Africa': 1, 'Mehoz': 1, 'Al-Lubnan': 1, 'Tanzania': 1, 'Îran': 1, 'Austria': 2, 'Danmark': 1, 'Switzerland': 1, 'India': 1, \"Taehan Min'guk\": 1, 'Ísland': 1, 'Canada': 3, 'Japan': 4, 'Australia': 2, 'Jamaica': 1, 'Spain': 2, 'Brasil': 1, 'China': 1, 'Greece': 1, 'Guyana': 1}, '1980s': {'United States': 46, 'United Kingdom': 126, '0': 44, 'Romania': 3, 'France': 20, 'Italia': 14, 'Germany': 23, 'Bosna i Hercegovina': 2, 'Slovenija': 6, 'Pakistan': 1, 'India': 3, 'Guyana': 1, 'Ireland': 6, 'Austria': 5, 'Switzerland': 8, 'Bulgaria': 1, 'Ukraine': 3, 'Nederland': 4, 'Jamaica': 1, 'Poland': 7, 'Canada': 2, 'Indonesia': 1, 'Belgium': 1, 'Danmark': 1, 'Czech Republica': 2, 'Russia': 2, 'Argentina': 1, 'Sweden': 2, 'Hungery': 2, 'Hrvatska': 2, 'Spain': 2, 'Jugoslavija': 1, 'China': 1, 'Misr': 1, 'Nagoya, Nihon': 1}, '1900s': {'United Kingdom': 66, 'Austria': 1, '0': 9, 'France': 4, 'Danmark': 1, 'Ireland': 1, 'Australia': 1, 'United States': 2, 'Turkey': 1}, '1960s': {'United Kingdom': 84, 'Argentina': 1, 'Italia': 12, 'Malaysia': 1, 'Switzerland': 1, 'France': 17, 'Russia': 6, 'Ukraine': 4, 'Poland': 5, '0': 16, 'Germany': 14, 'Sweden': 2, 'Ireland': 3, 'United States': 33, 'South Africa': 4, 'Czech Republica': 3, 'Belgium': 7, 'Brasil': 1, 'Spain': 10, 'India': 3, 'Canada': 2, 'Pakistan': 2, 'Makedonija': 1, 'Romania': 3, 'Nederland': 3, 'Austria': 2, 'Tunis': 1, 'Australia': 1, 'New Zealand': 1, 'Hungery': 1, 'Hrvatska': 1, 'Venezuela': 1, 'Greece': 2, 'Japan': 1}, '1930s': {'United Kingdom': 94, 'Nederland': 4, 'France': 17, 'Russia': 6, 'United States': 3, 'Australia': 1, 'Poland': 2, 'Ireland': 1, 'Spain': 2, 'Belgium': 2, 'Austria': 2, 'India': 3, 'Italia': 3, 'Hrvatska': 2, '0': 6, 'Canada': 1, 'Germany': 1, 'Norge': 1, 'Misr': 1, 'Schlesien': 1, 'South Africa': 1}, '1950s': {'Poland': 5, 'Italia': 15, 'Switzerland': 4, 'United Kingdom': 60, 'Ukraine': 4, 'Germany': 9, \"Al-Jaza'ir\": 1, 'Ireland': 5, 'France': 15, '0': 11, 'Russia': 7, 'Schlesien': 1, 'Romania': 2, 'United States': 8, 'Greece': 3, 'Spain': 2, 'Australia': 2, 'Belgium': 3, 'Pakistan': 1, 'Argentina': 1, 'Austria': 3, 'Czech Republica': 1, 'Cuba': 1, 'Moldova': 1, 'Lietuva': 2, 'Sweden': 1, 'Canada': 1, 'México': 1, 'Latvija': 1, 'Belarus': 1, 'Portugal': 1, 'China': 1}, '1940s': {'United Kingdom': 76, 'Viet Nam': 1, 'Ireland': 3, 'France': 8, 'South Africa': 1, 'Nederland': 2, 'Portugal': 1, 'Belarus': 1, 'Austria': 3, 'India': 2, 'Germany': 3, 'Italia': 6, 'Spain': 2, '0': 3, 'China': 1, 'New Zealand': 1, 'United States': 3, 'Switzerland': 1, 'Latvija': 1, 'Poland': 1}, '1910s': {'United Kingdom': 76, '0': 6, 'Nederland': 3, 'Belgium': 2, 'France': 8, 'United States': 5, 'Austria': 1, 'Australia': 2, 'Hungery': 1, 'Hrvatska': 1, 'Ireland': 2, 'Switzerland': 1, 'Germany': 1}})\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict \n",
    "\n",
    "with open('Tate_acquisitions_dropped.csv', mode='r', encoding='utf-8') as csvfile:\n",
    "    reader = csv.DictReader(csvfile)\n",
    "    nationalities = defaultdict(dict)\n",
    "    for item in reader:\n",
    "        if int(item['DateAcquired']) in range (1900,1910):\n",
    "            if item['placeOfBirth'] not in nationalities['1900s']:\n",
    "                nationalities['1900s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1900s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1910,1920):\n",
    "            if item['placeOfBirth'] not in nationalities['1910s']:\n",
    "                nationalities['1910s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1910s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1920,1930):\n",
    "            if item['placeOfBirth'] not in nationalities['1920s']:\n",
    "                nationalities['1920s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1920s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1930,1940):\n",
    "            if item['placeOfBirth'] not in nationalities['1930s']:\n",
    "                nationalities['1930s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1930s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1940,1951):\n",
    "            if item['placeOfBirth'] not in nationalities['1940s']:\n",
    "                nationalities['1940s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1940s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1950,1961):\n",
    "            if item['placeOfBirth'] not in nationalities['1950s']:\n",
    "                nationalities['1950s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1950s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1960,1971):\n",
    "            if item['placeOfBirth'] not in nationalities['1960s']:\n",
    "                nationalities['1960s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1960s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1970,1981):\n",
    "            if item['placeOfBirth'] not in nationalities['1970s']:\n",
    "                nationalities['1970s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1970s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1980,1991):\n",
    "            if item['placeOfBirth'] not in nationalities['1980s']:\n",
    "                nationalities['1980s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1980s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (1990,2001):\n",
    "            if item['placeOfBirth'] not in nationalities['1990s']:\n",
    "                nationalities['1990s'][item['placeOfBirth']] = 1\n",
    "            else:\n",
    "                nationalities['1990s'][item['placeOfBirth']] += 1\n",
    "        if int(item['DateAcquired']) in range (2000,2011): \n",
    "            if item['placeOfBirth'] not in nationalities['2000s']: \n",
    "                nationalities['2000s'][item['placeOfBirth']] = 1 \n",
    "            else: \n",
    "                nationalities['2000s'][item['placeOfBirth']] += 1 \n",
    "        \n",
    "                \n",
    "\n",
    "print(nationalities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a3d165",
   "metadata": {},
   "source": [
    "## Centuries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5afc6047",
   "metadata": {},
   "source": [
    "### Moma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "78a13673",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'1930s': {'20th': 9}, '1940s': {'20th': 6973, '19th': 570}, '1950s': {'20th': 5784, '19th': 524, '18th': 5}, '1960s': {'20th': 27951, '19th': 2408, '18th': 81}, '1970s': {'20th': 12328, '19th': 739}, '1980s': {'20th': 8847, '19th': 1328, '18th': 1}, '1990s': {'20th': 11065, '19th': 236}, '2000s': {'20th': 18452, '19th': 296, '21th': 6817}})\n"
     ]
    }
   ],
   "source": [
    "with open('MoMA_artworks.csv', mode='r', encoding='utf-8') as csvfile:\n",
    "    reader = csv.DictReader(csvfile)\n",
    "    MoMA_centuries_acquired = defaultdict(dict)\n",
    "    for item in reader:\n",
    "        if int(item['DateAcquired']) in range (1900,1910):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1910s']:\n",
    "                    MoMA_centuries_acquired['1910s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1910s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1910s']:\n",
    "                    MoMA_centuries_acquired['1910s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1910s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1910s']:\n",
    "                    MoMA_centuries_acquired['1910s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1910s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1910s']:\n",
    "                    MoMA_centuries_acquired['1910s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1910s']['19th'] += 1\n",
    "            if int(item['Date']) in range(1900,1911):\n",
    "                if '20th' not in MoMA_centuries_acquired['1910s']:\n",
    "                    MoMA_centuries_acquired['1910s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1910s']['20th'] += 1\n",
    "            \n",
    "            \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1910,1920):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1920s']:\n",
    "                    MoMA_centuries_acquired['1920s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1920s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1920s']:\n",
    "                    MoMA_centuries_acquired['1920s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1920s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1920s']:\n",
    "                    MoMA_centuries_acquired['1920s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1920s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1920s']:\n",
    "                    MoMA_centuries_acquired['1920s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1920s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1920s']:\n",
    "                    MoMA_centuries_acquired['1920s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1920s']['20th'] += 1\n",
    "        \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1920,1930):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1930s']:\n",
    "                    MoMA_centuries_acquired['1930s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1930s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1930s']:\n",
    "                    MoMA_centuries_acquired['1930s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1930s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1930s']:\n",
    "                    MoMA_centuries_acquired['1930s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1930s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1930s']:\n",
    "                    MoMA_centuries_acquired['1930s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1930s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1930s']:\n",
    "                    MoMA_centuries_acquired['1930s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1930s']['20th'] += 1\n",
    "   \n",
    "   \n",
    "   \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1940,1950):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1940s']:\n",
    "                    MoMA_centuries_acquired['1940s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1940s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1940s']:\n",
    "                    MoMA_centuries_acquired['1940s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1940s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1940s']:\n",
    "                    MoMA_centuries_acquired['1940s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1940s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1940s']:\n",
    "                    MoMA_centuries_acquired['1940s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1940s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1940s']:\n",
    "                    MoMA_centuries_acquired['1940s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1940s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1950,1960):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1950s']:\n",
    "                    MoMA_centuries_acquired['1950s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1950s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1950s']:\n",
    "                    MoMA_centuries_acquired['1950s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1950s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1950s']:\n",
    "                    MoMA_centuries_acquired['1950s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1950s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1950s']:\n",
    "                    MoMA_centuries_acquired['1950s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1950s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1950s']:\n",
    "                    MoMA_centuries_acquired['1950s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1950s']['20th'] += 1\n",
    "\n",
    "                    \n",
    "        if int(item['DateAcquired']) in range (1960,1970):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1960s']:\n",
    "                    MoMA_centuries_acquired['1960s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1960s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1960s']:\n",
    "                    MoMA_centuries_acquired['1960s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1960s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1960s']:\n",
    "                    MoMA_centuries_acquired['1960s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1960s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1960s']:\n",
    "                    MoMA_centuries_acquired['1960s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1960s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1960s']:\n",
    "                    MoMA_centuries_acquired['1960s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1960s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1970,1980):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1970s']:\n",
    "                    MoMA_centuries_acquired['1970s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1970s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1970s']:\n",
    "                    MoMA_centuries_acquired['1970s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1970s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1970s']:\n",
    "                    MoMA_centuries_acquired['1970s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1970s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1970s']:\n",
    "                    MoMA_centuries_acquired['1970s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1970s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1970s']:\n",
    "                    MoMA_centuries_acquired['1970s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1970s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1980,1990):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1980s']:\n",
    "                    MoMA_centuries_acquired['1980s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1980s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1980s']:\n",
    "                    MoMA_centuries_acquired['1980s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1980s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1980s']:\n",
    "                    MoMA_centuries_acquired['1980s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1980s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1980s']:\n",
    "                    MoMA_centuries_acquired['1980s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1980s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1980s']:\n",
    "                    MoMA_centuries_acquired['1980s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1980s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1990,2000):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['1990s']:\n",
    "                    MoMA_centuries_acquired['1990s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1990s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['1990s']:\n",
    "                    MoMA_centuries_acquired['1990s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1990s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['1990s']:\n",
    "                    MoMA_centuries_acquired['1990s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1990s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['1990s']:\n",
    "                    MoMA_centuries_acquired['1990s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1990s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['1990s']:\n",
    "                    MoMA_centuries_acquired['1990s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['1990s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (2000,2011):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['16th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['17th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['18th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['19th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['20th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['20th'] += 1\n",
    "            if int(item['Date']) in range(2000,2011):\n",
    "                if '21th' not in MoMA_centuries_acquired['2000s']:\n",
    "                    MoMA_centuries_acquired['2000s']['21th'] = 1\n",
    "                else:\n",
    "                    MoMA_centuries_acquired['2000s']['21th'] += 1\n",
    "        \n",
    "        \n",
    "print(MoMA_centuries_acquired)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aef463",
   "metadata": {},
   "source": [
    "## Tate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6b78875a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'dict'>, {'1900s': {'19th': 245, '20th': 57, '18th': 18}, '1910s': {'19th': 267, '18th': 31, '20th': 210, '17th': 4}, '1920s': {'20th': 264, '19th': 560, '18th': 43, '17th': 2, '16th': 1}, '1930s': {'19th': 116, '20th': 299, '18th': 35, '17th': 1, '16th': 1}, '1940s': {'20th': 481, '19th': 155, '18th': 54, '17th': 2}, '1950s': {'20th': 439, '19th': 58, '17th': 12, '18th': 25, '16th': 2}, '1960s': {'20th': 691, '19th': 78, '18th': 64, '17th': 13, '16th': 3}, '1970s': {'20th': 5484, '18th': 96, '19th': 745, '16th': 2, '17th': 3}, '1980s': {'20th': 3028, '18th': 155, '19th': 1804, '16th': 2, '17th': 16}, '1990s': {'20th': 2448, '19th': 727, '17th': 19, '18th': 329, '16th': 2}, '2000s': {'20th': 3844, '21th': 1307, '18th': 38, '19th': 24, '16th': 1, '17th': 5}})\n"
     ]
    }
   ],
   "source": [
    "with open('Tate_artworks.csv', mode='r', encoding='utf-8') as csvfile:\n",
    "    reader = csv.DictReader(csvfile)\n",
    "    Tate_centuries_acquired = defaultdict(dict)\n",
    "    for item in reader:\n",
    "        if int(item['DateAcquired']) in range (1900,1910):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1900s']:\n",
    "                    Tate_centuries_acquired['1900s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1900s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1900s']:\n",
    "                    Tate_centuries_acquired['1900s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1900s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1900s']:\n",
    "                    Tate_centuries_acquired['1900s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1900s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1900s']:\n",
    "                    Tate_centuries_acquired['1900s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1900s']['19th'] += 1\n",
    "            if int(item['Date']) in range(1900,1911):\n",
    "                if '20th' not in Tate_centuries_acquired['1900s']:\n",
    "                    Tate_centuries_acquired['1900s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1900s']['20th'] += 1\n",
    "            \n",
    "            \n",
    "        if int(item['DateAcquired']) in range (1910,1920):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1910s']:\n",
    "                    Tate_centuries_acquired['1910s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1910s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1910s']:\n",
    "                    Tate_centuries_acquired['1910s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1910s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1910s']:\n",
    "                    Tate_centuries_acquired['1910s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1910s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1910s']:\n",
    "                    Tate_centuries_acquired['1910s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1910s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1910s']:\n",
    "                    Tate_centuries_acquired['1910s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1910s']['20th'] += 1\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1920,1930):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1920s']:\n",
    "                    Tate_centuries_acquired['1920s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1920s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1920s']:\n",
    "                    Tate_centuries_acquired['1920s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1920s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1920s']:\n",
    "                    Tate_centuries_acquired['1920s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1920s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1920s']:\n",
    "                    Tate_centuries_acquired['1920s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1920s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1920s']:\n",
    "                    Tate_centuries_acquired['1920s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1920s']['20th'] += 1\n",
    "                    \n",
    "                    \n",
    "        if int(item['DateAcquired']) in range (1930,1940):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1930s']:\n",
    "                    Tate_centuries_acquired['1930s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1930s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1930s']:\n",
    "                    Tate_centuries_acquired['1930s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1930s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1930s']:\n",
    "                    Tate_centuries_acquired['1930s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1930s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1930s']:\n",
    "                    Tate_centuries_acquired['1930s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1930s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1930s']:\n",
    "                    Tate_centuries_acquired['1930s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1930s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1940,1950):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1940s']:\n",
    "                    Tate_centuries_acquired['1940s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1940s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1940s']:\n",
    "                    Tate_centuries_acquired['1940s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1940s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1940s']:\n",
    "                    Tate_centuries_acquired['1940s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1940s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1940s']:\n",
    "                    Tate_centuries_acquired['1940s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1940s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1940s']:\n",
    "                    Tate_centuries_acquired['1940s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1940s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1950,1960):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1950s']:\n",
    "                    Tate_centuries_acquired['1950s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1950s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1950s']:\n",
    "                    Tate_centuries_acquired['1950s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1950s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1950s']:\n",
    "                    Tate_centuries_acquired['1950s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1950s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1950s']:\n",
    "                    Tate_centuries_acquired['1950s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1950s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1950s']:\n",
    "                    Tate_centuries_acquired['1950s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1950s']['20th'] += 1\n",
    "\n",
    "                    \n",
    "        if int(item['DateAcquired']) in range (1960,1970):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1960s']:\n",
    "                    Tate_centuries_acquired['1960s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1960s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1960s']:\n",
    "                    Tate_centuries_acquired['1960s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1960s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1960s']:\n",
    "                    Tate_centuries_acquired['1960s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1960s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1960s']:\n",
    "                    Tate_centuries_acquired['1960s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1960s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1960s']:\n",
    "                    Tate_centuries_acquired['1960s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1960s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1970,1980):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1970s']:\n",
    "                    Tate_centuries_acquired['1970s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1970s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1970s']:\n",
    "                    Tate_centuries_acquired['1970s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1970s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1970s']:\n",
    "                    Tate_centuries_acquired['1970s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1970s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1970s']:\n",
    "                    Tate_centuries_acquired['1970s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1970s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1970s']:\n",
    "                    Tate_centuries_acquired['1970s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1970s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1980,1990):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1980s']:\n",
    "                    Tate_centuries_acquired['1980s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1980s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1980s']:\n",
    "                    Tate_centuries_acquired['1980s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1980s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1980s']:\n",
    "                    Tate_centuries_acquired['1980s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1980s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1980s']:\n",
    "                    Tate_centuries_acquired['1980s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1980s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1980s']:\n",
    "                    Tate_centuries_acquired['1980s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1980s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        if int(item['DateAcquired']) in range (1990,2000):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['1990s']:\n",
    "                    Tate_centuries_acquired['1990s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1990s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['1990s']:\n",
    "                    Tate_centuries_acquired['1990s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1990s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['1990s']:\n",
    "                    Tate_centuries_acquired['1990s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1990s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['1990s']:\n",
    "                    Tate_centuries_acquired['1990s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1990s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['1990s']:\n",
    "                    Tate_centuries_acquired['1990s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['1990s']['20th'] += 1\n",
    "\n",
    "        \n",
    "        \n",
    "        if int(item['DateAcquired']) in range (2000,2011):\n",
    "            if int(item['Date']) in range(1500,1600):\n",
    "                if '16th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['16th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['16th'] += 1\n",
    "            if int(item['Date']) in range(1600,1700):\n",
    "                if '17th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['17th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['17th'] += 1\n",
    "            if int(item['Date']) in range(1700,1800):\n",
    "                if '18th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['18th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['18th'] += 1\n",
    "            if int(item['Date']) in range(1800,1900):\n",
    "                if '19th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['19th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['19th'] += 1 \n",
    "            if int(item['Date']) in range(1900,2000):\n",
    "                if '20th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['20th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['20th'] += 1\n",
    "            if int(item['Date']) in range(2000,2011):\n",
    "                if '21th' not in Tate_centuries_acquired['2000s']:\n",
    "                    Tate_centuries_acquired['2000s']['21th'] = 1\n",
    "                else:\n",
    "                    Tate_centuries_acquired['2000s']['21th'] += 1\n",
    "        \n",
    "        \n",
    "print(Tate_centuries_acquired)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}