wikidata-mayors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "layout: post\n",
    "category: blog\n",
    "title: \"Where do Mayors Come From: Querying Wikidata with Python and SPARQL\"\n",
    "tags: [Wikidata, SPARQL, Python, Pandas, Data Science]\n",
    "image: /assets/wikidata_mayors_screen.png\n",
    "---\n",
    "\n",
    "In this article, we will be going through building queries for Wikidata with Python and SPARQL by taking a look where mayors in Europe are born. This tutorial is building up the knowledge to collect the data responsible for this [interactive visualization](https://janakiev.com/wikidata-mayors/) from the header image which was done with [deck.gl](http://deck.gl/#/).\n",
    "\n",
    "[Wikidata](https://www.wikidata.org/) is a free and collaborative [Linked Open Data (LOD)](https://en.wikipedia.org/wiki/Linked_data) knowledge base which can be edited by humans and machines. The project started 2012 by the [Wikimedia Foundation](https://foundation.wikimedia.org/wiki/Home) as an effort to centralize interwiki links, infoboxes and enable rich queries. Its ambitious goal is to structure the whole human knowledge in a way that is machine readable and it speaks well to the vision of Tim Berners-Lee in his [TED talk](https://www.youtube.com/watch?v=OM6XIICm_qo) of 2009. Surprisingly, the idea of the [Semantic Web](https://en.wikipedia.org/wiki/Semantic_Web) existed already in 2001 which is comprised of Linked Data. There have been many projects preceding Wikidata. There is [DBpedia](https://en.wikipedia.org/wiki/DBpedia) which is based on the infoboxes in Wikipedia, [Friend of a Friend (FOAF)](https://en.wikipedia.org/wiki/FOAF_(ontology)) which is an ontology to describe relationships in social networks, [GeoNames](https://en.wikipedia.org/wiki/GeoNames) which provides a database with geographical names, [Upper Mapping and Binding Exchange Layer (UMBEL)](https://en.wikipedia.org/wiki/UMBEL) which is a knowledge graph of concepts and entities and a whole set of others, but Wikidata seems to be the most ambitious project between them.\n",
    "\n",
    "All of the data there is free (under the [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) aka public domain), while anyone can edit and contribute to it. So it works in a similar way to Wikipedia. On most (if not all) Wikipedia pages, there is a _Wikidata Item_ link to its corresponding item in Wikidata, where you can find the linked information listed. Note that you can still find holes, but as it is a community effort, this is becoming better and growing over time by every contribution. To access the structured data you can query Wikidata by using its [SPARQL endpoint](https://query.wikidata.org/) which enables you to run advanced queries, or by using its [REST API](https://www.wikidata.org/w/api.php).\n",
    "\n",
    "In this diagram, you can see the structure of a Wikidata item. Each item has a list of [statements](https://www.wikidata.org/wiki/Help:Statements), which are triples in the form `SUBJECT` - `PREDICATE` - `OBJECT` (e.g. Douglas Adams is educated at the St John's College). In Wikidata the subject is referred to as [item](https://www.wikidata.org/wiki/Help:Items) and the predicate is referred to as [property](https://www.wikidata.org/wiki/Help:Properties). Each property has a value, which can be again an item, text, number, date, or GPS coordinates among others. Each value can have additional [qualifiers](https://www.wikidata.org/wiki/Help:Qualifiers) which have additional information with other property-value pairs such as start time. This structure will be important when we start to express queries with SPARQL.\n",
    "\n",
    "![Wikidata Data Model](assets/wikidata_data_model.png)\n",
    "\n",
    "image from [SPARQL/WIKIDATA Qualifiers, References and Ranks](https://en.wikibooks.org/wiki/SPARQL/WIKIDATA_Qualifiers,_References_and_Ranks).\n",
    "\n",
    "Also, all the code for this article and the interactive visualization can be found in this [repository](https://github.com/njanakiev/wikidata-mayors)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing SPARQL\n",
    "\n",
    "Before getting to Python we will dissect [SPARQL](https://en.wikipedia.org/wiki/SPARQL) to get comfortable doing some queries. SPARQL is a query language used to retrieve data stored as [RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework) (Resource Description Framework) and it is standardized by the W3C. It is a powerful language to query Linked data and we can also use it to query Wikidata. The syntax is similar to SQL, but it has some differences for people trained in SQL. One key difference is that in SQL you tend to avoid `JOIN` clauses as they can slow down queries, but in SPARQL the queries mostly consist of joins. But hang in there and let's take a look at such a query. In this example, we want to list all countries in the European Union.\n",
    "\n",
    "```sparql\n",
    "SELECT ?country ?countryLabel WHERE {\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n",
    "}\n",
    "```\n",
    "You can try this query yourself [here](https://query.wikidata.org/#SELECT%20%3Fcountry%20%3FcountryLabel%20WHERE%20%7B%0A%20%20%3Fcountry%20wdt%3AP463%20wd%3AQ458.%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20%0A%20%20%20%20bd%3AserviceParam%20wikibase%3Alanguage%20%22%5BAUTO_LANGUAGE%5D%2Cen%22.%20%0A%20%20%7D%0A%7D). Note that you can test and play with each query at [https://query.wikidata.org/](https://query.wikidata.org/). The editor there offers a handful of useful features. If you hover over the properties and items in the editor you will get information about them and the editor additionally offers autocompletion. You will also find a list of examples which are quite handy when starting fresh.\n",
    "\n",
    "Starting with the `SELECT` clause, we define the variables we want to get (variables are prefixed with a question mark). Inside the `WHERE` clause, we set restrictions which mostly take the form of the triples we have covered previously. The statement `?country wdt:P463 wd:Q458.` collects all items which have the property [member of (P463)](https://www.wikidata.org/wiki/Property:P463) with object [European Union (Q458)](https://www.wikidata.org/wiki/Q458) into the variable `country`. As you can see, the statements read like a sentence (i.e. country is a member of the European Union). You also notice that there are the prefixes `wd:` and `wdt:`. These denote items with `wd:` and properties with `wdt:`. We will cover more complicated prefixes later on in this tutorial when we will get into the SPARQL data representation. \n",
    "\n",
    "Finally, you will see a confusing part `SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }` within the query. This snippet is responsible for retrieving labels for the collected items into an additional variable with `Label` postfix in the specified language (in this case English). In this query, this would be the `countryLabel` variable storing the label for the `country` variable. Note that the label is only retrieved for items that have a label in the particular language selected (in this case `\"en\"` for English), as there might be items that are not translated into this particular language.\n",
    "\n",
    "Interesting sidenote: When running the query you will notice [Kingdom of the Netherlands](https://en.wikipedia.org/wiki/Kingdom_of_the_Netherlands) with Wikidata item [Q29999](https://www.wikidata.org/wiki/Q29999) in the list of European countries. Surprisingly, [Netherlands](https://en.wikipedia.org/wiki/Netherlands) ([Q55](https://www.wikidata.org/wiki/Q55)) is a constituent country of the Kingdom of the Netherlands, but it is not a country. It is similar to how England is part of the United Kingdom. This [video](https://www.youtube.com/watch?v=WBcn-lzTJbk) does a great job explaining the situation if you were puzzled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Queries\n",
    "\n",
    "Let's now explore other properties of the countries we have selected. If you take a look at [Germany (Q183)](https://www.wikidata.org/wiki/Q183), then you can see a whole host of properties like [population (P1082)](https://www.wikidata.org/wiki/Property:P1082), [median income (P3529)](https://www.wikidata.org/wiki/Property:P3529) or even images with the [image (P18)](https://www.wikidata.org/wiki/Property:P18) property. SPARQL enables us to retrieve those too which leads us to the next query.\n",
    "\n",
    "```sparql\n",
    "SELECT \n",
    "  ?country ?countryLabel ?population ?area ?medianIncome\n",
    "WHERE {\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  ?country wdt:P1082 ?population.\n",
    "  ?country wdt:P2046 ?area.\n",
    "  ?country wdt:P3529 ?medianIncome.\n",
    "  \n",
    "  SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n",
    "}\n",
    "```\n",
    "You can try this query [here](https://query.wikidata.org/#SELECT%20%0A%20%20%3Fcountry%20%3FcountryLabel%20%0A%20%20%3Fcapital%20%3FcapitalLabel%0A%20%20%3Fpopulation%20%3Farea%20%3FmedianIncome%0AWHERE%20%7B%0A%20%20%3Fcountry%20wdt%3AP463%20wd%3AQ458.%0A%20%20%3Fcountry%20wdt%3AP36%20%3Fcapital.%0A%20%20%3Fcountry%20wdt%3AP1082%20%3Fpopulation.%0A%20%20%3Fcountry%20wdt%3AP2046%20%3Farea.%0A%20%20%3Fcountry%20wdt%3AP3529%20%3FmedianIncome.%0A%20%20%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22.%20%7D%0A%7D).\n",
    "\n",
    "After trying this query you will notice that the list of countries became shorter.  The reason for this is that each country item that has no population, area or median income as a property is ignored by the query. You can imagine those triples also as a filter constraining the triples that only match this query. We can add the `OPTIONAL` clause which will leave those variables empty if the query cannot find triples within this clause.\n",
    "\n",
    "```sparql\n",
    "SELECT \n",
    "  ?country ?countryLabel ?population ?area ?medianIncome\n",
    "WHERE {\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  OPTIONAL { ?country wdt:P1082 ?population }\n",
    "  OPTIONAL { ?country wdt:P2046 ?area }\n",
    "  OPTIONAL { ?country wdt:P3529 ?medianIncome }\n",
    "    \n",
    "  SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n",
    "}\n",
    "```\n",
    "\n",
    "You can try this query [here](https://query.wikidata.org/#SELECT%20%0A%20%20%3Fcountry%20%3FcountryLabel%20%0A%20%20%3Fcapital%20%3FcapitalLabel%0A%20%20%3Fpopulation%20%3Farea%20%3FmedianIncome%0AWHERE%20%7B%0A%20%20%3Fcountry%20wdt%3AP463%20wd%3AQ458.%0A%20%20%3Fcountry%20wdt%3AP36%20%3Fcapital.%0A%20%20%20%20%0A%20%20OPTIONAL%20%7B%20%3Fcountry%20wdt%3AP1082%20%3Fpopulation%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fcountry%20wdt%3AP2046%20%3Farea%20%7D%0A%20%20OPTIONAL%20%7B%20%3Fcountry%20wdt%3AP3529%20%3FmedianIncome%20%7D%0A%20%20%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20bd%3AserviceParam%20wikibase%3Alanguage%20%22en%22.%20%7D%0A%7D). Now we see in the table that we will find all countries again.\n",
    "\n",
    "![Query Result](assets/query_result.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPARQL Data Representation\n",
    "\n",
    "We continue our journey with a complicated query which we will unpack step by step. Our goal is now to get for all countries, the capital, the population, the mayor, his birthday and finally his birthplace. The query looks like this.\n",
    "\n",
    "```sparql\n",
    "SELECT DISTINCT \n",
    "  ?country ?countryLabel ?capital ?capitalLabel ?population \n",
    "  ?mayor ?mayorLabel ?birth_place ?birth_placeLabel ?birth_date ?age\n",
    "WHERE {\n",
    "  # Get all european countries, their capitals and the population of the capital\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  ?country wdt:P36 ?capital.\n",
    "  OPTIONAL { ?capital wdt:P1082 ?population. }\n",
    "  \n",
    "  # Get all mayors without an end date\n",
    "  ?capital p:P6 ?statement.\n",
    "  ?statement ps:P6 ?mayor.\n",
    "  FILTER NOT EXISTS { ?statement pq:P582 ?end_date }\n",
    "  \n",
    "  # Get birth place, birth date and age of mayor\n",
    "  ?mayor wdt:P19 ?birth_place.\n",
    "  ?mayor wdt:P569 ?birth_date.\n",
    "  BIND(year(now()) - year(?birth_date) AS ?age)\n",
    "  \n",
    "  SERVICE wikibase:label { \n",
    "    bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\". \n",
    "  }\n",
    "}\n",
    "```\n",
    "You can try this query [here](https://query.wikidata.org/#SELECT%20DISTINCT%20%0A%20%20%3Fcountry%20%3FcountryLabel%20%3Fcapital%20%3FcapitalLabel%20%3Fpopulation%20%0A%20%20%3Fmayor%20%3FmayorLabel%20%3Fbirth_place%20%3Fbirth_placeLabel%20%3Fbirth_date%20%3Fage%0AWHERE%20%7B%0A%20%20%3Fcountry%20wdt%3AP463%20wd%3AQ458.%0A%20%20%3Fcountry%20wdt%3AP36%20%3Fcapital.%0A%20%20OPTIONAL%20%7B%20%3Fcapital%20wdt%3AP1082%20%3Fpopulation.%20%7D%0A%20%20%0A%20%20%3Fcapital%20p%3AP6%20%3Fstatement.%0A%20%20%3Fstatement%20ps%3AP6%20%3Fmayor.%0A%20%20FILTER%20NOT%20EXISTS%20%7B%20%3Fstatement%20pq%3AP582%20%3Fend_date%20%7D%0A%20%20%20%20%0A%20%20%3Fmayor%20wdt%3AP19%20%3Fbirth_place.%0A%20%20%3Fmayor%20wdt%3AP569%20%3Fbirth_date.%0A%20%20BIND%28year%28now%28%29%29%20-%20year%28%3Fbirth_date%29%20AS%20%3Fage%29%0A%20%20%0A%20%20SERVICE%20wikibase%3Alabel%20%7B%20%0A%20%20%20%20bd%3AserviceParam%20wikibase%3Alanguage%20%22%5BAUTO_LANGUAGE%5D%2Cen%22.%20%0A%20%20%7D%0A%7D).\n",
    "\n",
    "Let's unpack what is happening here. First, we start by getting the capital of the country which we simply get via the [capital (P36)](https://www.wikidata.org/wiki/Property:P36) property. Next, we get to a more complicated part. To understand how to get to the mayor we have to look at the SPARQL Data Representation in this diagram.\n",
    "\n",
    "![SPARQL data representation](assets/SPARQL_data_representation.png)\n",
    "\n",
    "image from [SPARQL/WIKIDATA Qualifiers, References and Ranks](https://en.wikibooks.org/wiki/SPARQL/WIKIDATA_Qualifiers,_References_and_Ranks).\n",
    "\n",
    "This graph of the data representation that you see here shows the ways you can traverse it to get to various pieces of information with SPARQL starting from an item (in the graph shown as `wd:Q1234`). You can see on the left the classical path we took in our previous triples by using the `wdt:` prefix which leads to the value which can be another item, a numeric value (e.g. the population as in one of the previous queries) or various other data types.\n",
    "\n",
    "If you take a look at an item like [Rome (Q220)](https://www.wikidata.org/wiki/Q220), you will notice that there are various statements for the [head of government (P6)](https://www.wikidata.org/wiki/Property:P6). We want to get the one which has no end date. We can do this by traversing to the statement node with the `p:` prefix and storing it in the `statement` variable. From this variable, we can get the mayor with the `ps:` prefix. We could have done that with `wdt:` as we already have learned but we want to go one step further. We want to get to [end time (P582)](https://www.wikidata.org/wiki/Property:P582) which is stored as a qualifier in the statement. We can traverse to the qualifier with the `pq:` prefix which would give us the end date, but we want mayors without an end date. This can be done by using the `FILTER NOT EXISTS` clause which excludes all triples with statement node that have an end date.\n",
    "\n",
    "In the final part, we collect the birthplace, the birth date and the age of the mayor. In order to calculate his age, we use the `BIND` expression. This expression can be used to bind some expression to a variable (in our case the age variable). For this expression, we subtract the year of the birth date with the current year. This concludes this query. You can dig deeper in [SPARQL/WIKIDATA Qualifiers, References and Ranks](https://en.wikibooks.org/wiki/SPARQL/WIKIDATA_Qualifiers,_References_and_Ranks) which describes the data representation in further detail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving SPARQL Queries with Python\n",
    "\n",
    "We have seen how to work with SPARQL and we can also download the resulting tables in the editor, but how do we automate the whole process? We can access the Wikidata SPARQL endpoint also with Python, which enables us to directly load and analyze the data we have queried. To do this, we will employ the [request](http://docs.python-requests.org/en/master/) module which does a great job at doing HTTP requests with all its necessary tooling. We can create the request by adding the query as a parameter as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "url = 'https://query.wikidata.org/sparql'\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "  ?countryLabel ?population ?area ?medianIncome ?age\n",
    "WHERE {\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  OPTIONAL { ?country wdt:P1082 ?population }\n",
    "  OPTIONAL { ?country wdt:P2046 ?area }\n",
    "  OPTIONAL { ?country wdt:P3529 ?medianIncome }\n",
    "  OPTIONAL { ?country wdt:P571 ?inception. \n",
    "    BIND(year(now()) - year(?inception) AS ?age)\n",
    "  }\n",
    "  SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n",
    "}\n",
    "\"\"\"\n",
    "r = requests.get(url, params = {'format': 'json', 'query': query})\n",
    "data = r.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have packed the query in the `query` variable and we need to additionally supply request with the SPARQL endpoint URL which is [https://query.wikidata.org/sparql](https://query.wikidata.org/sparql). We want to use JSON as an output file, so we add this also to our request. The API returns XML as default but supports besides JSON also TSV, CSV and Binary RDF. This request returns a JSON with all the rows collected from the query, which we can use collect the rows into a [Pandas](https://pandas.pydata.org/) DataFrame as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>population</th>\n",
       "      <th>area</th>\n",
       "      <th>medianIncome</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kingdom of the Netherlands</th>\n",
       "      <td>17100715.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>203.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>4761865.0</td>\n",
       "      <td>70274.147397</td>\n",
       "      <td>25969.0</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11150516.0</td>\n",
       "      <td>30528.000000</td>\n",
       "      <td>31112.0</td>\n",
       "      <td>188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>9830485.0</td>\n",
       "      <td>93011.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>46528024.0</td>\n",
       "      <td>505990.000000</td>\n",
       "      <td>21959.0</td>\n",
       "      <td>539.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            population           area  medianIncome     age\n",
       "country                                                                    \n",
       "Kingdom of the Netherlands  17100715.0            NaN           NaN   203.0\n",
       "Ireland                      4761865.0   70274.147397       25969.0    81.0\n",
       "Belgium                     11150516.0   30528.000000       31112.0   188.0\n",
       "Hungary                      9830485.0   93011.400000           NaN  1018.0\n",
       "Spain                       46528024.0  505990.000000       21959.0   539.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from collections import OrderedDict\n",
    "\n",
    "countries = []\n",
    "for item in data['results']['bindings']:\n",
    "    countries.append(OrderedDict({\n",
    "        'country': item['countryLabel']['value'],\n",
    "        'population': item['population']['value'],\n",
    "        'area': item['area']['value'] \n",
    "            if 'area' in item else None,\n",
    "        'medianIncome': item['medianIncome']['value'] \n",
    "            if 'medianIncome' in item else None,\n",
    "        'age': item['age']['value'] \n",
    "            if 'age' in item else None}))\n",
    "\n",
    "df = pd.DataFrame(countries)\n",
    "df.set_index('country', inplace=True)\n",
    "df = df.astype({'population': float, 'area': float, 'medianIncome': float, 'age': float})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the collected data visually and compare the various properties for each country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.figure(figsize=(16, 12))\n",
    "for i, label in enumerate(['population', 'medianIncome', 'area', 'age']):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    df_plot = df[label].sort_values().dropna()\n",
    "    df_plot.plot(kind='barh', color='C0', ax=plt.gca());\n",
    "    plt.ylabel('')\n",
    "    plt.xticks(rotation=30)\n",
    "    plt.title(label.capitalize())\n",
    "    plt.ticklabel_format(style='plain', axis='x')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mayors of all European Capitals\n",
    "\n",
    "In our final query, we will take a look at where mayors are born by adding the coordinates to the query. In order to get the latitude and longitude coordinates as variables, we need to add the following snippet.\n",
    "\n",
    "```sparql\n",
    "?capital p:P625/psv:P625 ?capital_node.\n",
    "?capital_node wikibase:geoLatitude ?capital_lat.\n",
    "?capital_node wikibase:geoLongitude ?capital_lon.\n",
    "```\n",
    "\n",
    "In the first line, we traverse the graph of the previously shown data representation. The slash in `p:P625/psv:P625` means that we continue to the _Value node_ of the [coordinate location (P625)](https://www.wikidata.org/wiki/Property:P625) without using a separate variable for the _Statement node_. Then, `wikibase:geoLatitude` and `wikibase:geoLongitude` are responsible for retrieving the latitude and longitude from the _Value node_ respectively. For more information, take a look at [Precision, Units and Coordinates](https://en.wikibooks.org/wiki/SPARQL/WIKIDATA_Precision,_Units_and_Coordinates)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>countryLabel</th>\n",
       "      <th>capital_lon</th>\n",
       "      <th>capital_lat</th>\n",
       "      <th>population</th>\n",
       "      <th>mayorLabel</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>birth_placeLabel</th>\n",
       "      <th>birth_place_lon</th>\n",
       "      <th>birth_place_lat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capitalLabel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tallinn</th>\n",
       "      <td>Estonia</td>\n",
       "      <td>24.745000</td>\n",
       "      <td>59.437222</td>\n",
       "      <td>446055.0</td>\n",
       "      <td>Taavi Aas</td>\n",
       "      <td>1966-01-10T00:00:00Z</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Tallinn</td>\n",
       "      <td>24.745000</td>\n",
       "      <td>59.437222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brussels</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>4.354700</td>\n",
       "      <td>50.846700</td>\n",
       "      <td>176545.0</td>\n",
       "      <td>Philippe Close</td>\n",
       "      <td>1971-03-18T00:00:00Z</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Namur</td>\n",
       "      <td>4.866667</td>\n",
       "      <td>50.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sofia</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>42.700000</td>\n",
       "      <td>1286383.0</td>\n",
       "      <td>Yordanka Fandakova</td>\n",
       "      <td>1962-04-12T00:00:00Z</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Samokov</td>\n",
       "      <td>23.560000</td>\n",
       "      <td>42.338056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warsaw</th>\n",
       "      <td>Poland</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>52.216667</td>\n",
       "      <td>1753977.0</td>\n",
       "      <td>Hanna Gronkiewicz-Waltz</td>\n",
       "      <td>1952-11-04T00:00:00Z</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>21.033333</td>\n",
       "      <td>52.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stockholm</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>18.068611</td>\n",
       "      <td>59.329444</td>\n",
       "      <td>935619.0</td>\n",
       "      <td>Karin Wanngård</td>\n",
       "      <td>1975-06-29T00:00:00Z</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Ekerö</td>\n",
       "      <td>17.799879</td>\n",
       "      <td>59.274446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             countryLabel  capital_lon  capital_lat  population  \\\n",
       "capitalLabel                                                      \n",
       "Tallinn           Estonia    24.745000    59.437222    446055.0   \n",
       "Brussels          Belgium     4.354700    50.846700    176545.0   \n",
       "Sofia            Bulgaria    23.333333    42.700000   1286383.0   \n",
       "Warsaw             Poland    21.033333    52.216667   1753977.0   \n",
       "Stockholm          Sweden    18.068611    59.329444    935619.0   \n",
       "\n",
       "                           mayorLabel            birth_date   age  \\\n",
       "capitalLabel                                                        \n",
       "Tallinn                     Taavi Aas  1966-01-10T00:00:00Z  52.0   \n",
       "Brussels               Philippe Close  1971-03-18T00:00:00Z  47.0   \n",
       "Sofia              Yordanka Fandakova  1962-04-12T00:00:00Z  56.0   \n",
       "Warsaw        Hanna Gronkiewicz-Waltz  1952-11-04T00:00:00Z  66.0   \n",
       "Stockholm              Karin Wanngård  1975-06-29T00:00:00Z  43.0   \n",
       "\n",
       "             birth_placeLabel  birth_place_lon  birth_place_lat  \n",
       "capitalLabel                                                     \n",
       "Tallinn               Tallinn        24.745000        59.437222  \n",
       "Brussels                Namur         4.866667        50.466667  \n",
       "Sofia                 Samokov        23.560000        42.338056  \n",
       "Warsaw                 Warsaw        21.033333        52.216667  \n",
       "Stockholm               Ekerö        17.799879        59.274446  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://query.wikidata.org/sparql'\n",
    "query=\"\"\"\n",
    "SELECT DISTINCT \n",
    "  ?countryLabel ?capitalLabel ?population ?capital_lon ?capital_lat\n",
    "  ?mayorLabel ?birth_date ?age ?birth_place ?birth_placeLabel ?birth_place_lon ?birth_place_lat\n",
    "WHERE {\n",
    "  ?country wdt:P463 wd:Q458.\n",
    "  ?country wdt:P36 ?capital.\n",
    "  OPTIONAL { ?capital wdt:P1082 ?population. }\n",
    "  \n",
    "  # Get latitude longitude coordinates of capital\n",
    "  ?capital p:P625/psv:P625 ?capital_node.\n",
    "  ?capital_node wikibase:geoLatitude ?capital_lat.\n",
    "  ?capital_node wikibase:geoLongitude ?capital_lon.\n",
    "\n",
    "  ?capital p:P6 ?statement.\n",
    "  ?statement ps:P6 ?mayor.\n",
    "  FILTER NOT EXISTS { ?statement pq:P582 ?end_date }\n",
    "\n",
    "  ?mayor wdt:P569 ?birth_date.\n",
    "  BIND(year(now()) - year(?birth_date) AS ?age)\n",
    "  ?mayor wdt:P19 ?birth_place.\n",
    "  ?birth_place wdt:P625 ?birth_place_coordinates.\n",
    "  \n",
    "  # Get latitude longitude coordinates of birth place\n",
    "  ?birth_place p:P625/psv:P625 ?birth_place_node.\n",
    "  ?birth_place_node wikibase:geoLatitude ?birth_place_lat.\n",
    "  ?birth_place_node wikibase:geoLongitude ?birth_place_lon.\n",
    "  \n",
    "  SERVICE wikibase:label { \n",
    "    bd:serviceParam wikibase:language \"[AUTO_LANGUAGE],en\". \n",
    "  }\n",
    "}\n",
    "\"\"\"\n",
    "\n",
    "r = requests.get(url, params = {'format': 'json', 'query': query})\n",
    "data = r.json()\n",
    "\n",
    "countries = []\n",
    "for item in data['results']['bindings']:\n",
    "    countries.append(OrderedDict({\n",
    "        label : item[label]['value'] if label in item else None\n",
    "        for label in ['countryLabel', 'capitalLabel',  'capital_lon', 'capital_lat', 'population', \n",
    "                       'mayorLabel', 'birth_date', 'age', \n",
    "                       'birth_placeLabel', 'birth_place_lon', 'birth_place_lat']}))\n",
    "\n",
    "df = pd.DataFrame(countries)\n",
    "df.set_index('capitalLabel', inplace=True)\n",
    "df = df.astype({'population': float, 'age': float,\n",
    "                'capital_lon': float, 'capital_lat': float, \n",
    "                'birth_place_lon': float, 'birth_place_lat': float})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking this data set we can explore the age of the mayors and the population of the capital they are serving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "df['age'].sort_values().plot(kind='barh', color='C0', title='Mayors Age')\n",
    "plt.ylabel('')\n",
    "plt.subplot(1, 2, 2)\n",
    "df['population'].sort_values().plot(kind='barh', color='C0', title='Population')\n",
    "plt.ylabel('')\n",
    "plt.ticklabel_format(style='plain', axis='x')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's take a look at how far mayors are born from the capital. For this, we will use the [geopy](https://geopy.readthedocs.io/en/stable/) package to calculate the distance between the coordinates. This [tutorial](https://janakiev.com/blog/gps-points-distance-python/) covers this topic if you are curious why we can't just use euclidean distance on GPS coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from geopy.distance import distance\n",
    "\n",
    "coordinates = df[['capital_lon', 'capital_lat', 'birth_place_lon', 'birth_place_lat']]\n",
    "df['distance'] = [distance((lat0, lon0), (lat1, lon1)).m \n",
    "                for lon0, lat0, lon1, lat1 in coordinates.values]\n",
    "\n",
    "df['distance'].sort_values().plot(kind='barh', color='C0', logx=True, figsize=(12, 7))\n",
    "plt.xlabel('Distance (m)')\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that most mayors tend to be born in or close to the city where they later serve (note, that the chart is in log-scale). We can see that Athens leads the exceptions with their current mayor (Giorgos Kaminis) born in New York, USA and Paris with their current mayor (Anne Hidalgo) born in San Fernando, Spain. From there the distances drop significantly.\n",
    "\n",
    "To get a list of all mayors in Europe, take a look at this [script](https://github.com/njanakiev/wikidata-mayors/blob/master/load_european_mayors.py), which is more involved as it has to deal with some exceptions (like mayors born in countries that do not exist anymore) and the queries need to be done for each country separately because there is a limit on the queries. The final interactive visualization can be found [here](https://janakiev.com/wikidata-mayors/) and the complete code including this notebook can be found in this [repository](https://github.com/njanakiev/wikidata-mayors)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "We have learned how to work with Wikidata and SPARQL and also how to integrate it with Python and Pandas.  Wikidata is a great database that enables queries and discoveries that would not be possible with ordinary searches on your favorite search engine. This opens up exciting new possibilities to do data science and exploratory data analysis and a fascinating new way to learn about relationships and curious findings in our accumulated human knowledge.\n",
    "\n",
    "## Resources and a Pile of Links\n",
    "\n",
    "A good read covering the history and an overview of Wikidata can be found in the article [Wikidata: a free collaborative knowledge base.](http://delivery.acm.org/10.1145/2630000/2629489/p78-vrandecic.pdf?ip=188.23.116.132&id=2629489&acc=OA&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2EB8CD93A2515AABD1&__acm__=1533116941_da1ef0b41c8a6ea97e62187e7e72de90) by Vrandečić, D., & Krötzsch, M. (2014). There is a great [SPARQL tutorial](https://www.wikidata.org/wiki/Wikidata:SPARQL_tutorial) covering many of the things mentioned here and goes into much more depth into understanding SPARQL. If you are excited about Wikidata and want to contribute, there are [Wikidata Tours](https://www.wikidata.org/wiki/Wikidata:Tours) that can guide you through the process. If you plan on doing large queries, make sure to take a look at the publicly downloadable [Wikidata dumps](https://dumps.wikimedia.org/wikidatawiki/entities/) which are regularly updated dumps of the whole Wikidata data set and here is a documentation on the [Wikibase data model](https://www.mediawiki.org/wiki/Wikibase/DataModel). Wikidata provides also a list of [Tools for programmers](https://www.wikidata.org/wiki/Wikidata:Tools/For_programmers).\n",
    "\n",
    "Here is an unstructured list of resources that contain useful documentation, tutorials or examples that use Wikidata. If you are aware of more useful resources or if you generally have some feedback feel free to contact me or add a push request to the [notebook](https://github.com/njanakiev/wikidata-mayors/blob/master/wikidata-mayors.ipynb) of this article.\n",
    "\n",
    "- [RDF Exports from Wikidata](https://tools.wmflabs.org/wikidata-exports/rdf/)\n",
    "- [Wikidata:List of properties](https://www.wikidata.org/wiki/Wikidata:List_of_properties)\n",
    "- [Wikidata:Database reports](https://www.wikidata.org/wiki/Wikidata:Database_reports)\n",
    "- [Wikidata:Database reports/List of properties/Top100](https://www.wikidata.org/wiki/Wikidata:Database_reports/List_of_properties/Top100)\n",
    "- [Wikidata:Introduction](https://www.wikidata.org/wiki/Wikidata:Introduction)\n",
    "- [Wikidata:SPARQL queries](https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries)\n",
    "- [Wikidata:SPARQL queries examples](https://www.wikidata.org/wiki/Wikidata:SPARQL_query_service/queries/examples)\n",
    "- [Wikidata:Creating a bot](https://www.wikidata.org/wiki/Wikidata:Creating_a_bot)\n",
    "- [Querying Wikidata About Vienna’s Tram Lines: An Introduction to SPARQL](https://noniq.at/0016/wikidata-sparql-introduction/)\n",
    "- [How US Presidents Died According to Wikidata](http://ramiro.org/notebook/us-presidents-causes-of-death/)\n",
    "- [An Ambitious Wikidata Tutorial - SlideShare](https://de.slideshare.net/_Emw/an-ambitious-wikidata-tutorial)\n",
    "- [Wikidata Graph Builder](https://angryloki.github.io/wikidata-graph-builder/), great tool to visualize Wikidata graphs\n",
    "- [Histropedia](http://histropedia.com), visualization of historical timelines"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}