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flxai/illionaires.ipynb

Created July 22, 2024 22:22
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Illionaires
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illionaires.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "061dc8a4-1d43-4abf-bc95-2248704c603c",
"metadata": {},
"source": [
"# Billionaires and millionaires from wikidata\n",
"## Queries"
]
},
{
"cell_type": "markdown",
"id": "d51befb2-440b-4c8c-91a6-32d049cc7ffd",
"metadata": {},
"source": [
"```SPARQL\n",
"#Distinct billionaires\n",
"#title: Distinct billionaires\n",
"SELECT ?locationLabel ?item ?itemLabel ?yob (MAX(?billion) as ?billions)\n",
"WHERE\n",
"{\n",
" ?item wdt:P2218 ?worth;\n",
" wdt:P569 ?dob.\n",
" ?item wdt:P19 ?location.\n",
" \n",
" FILTER(?worth>1000000000).\n",
" BIND(YEAR(?dob) AS ?yob).\n",
" BIND(?worth/1000000000 AS ?billion).\n",
" SERVICE wikibase:label { bd:serviceParam wikibase:language \"en,de\".}\n",
" MINUS {?item wdt:P31 wd:Q15632617.}\n",
"}\n",
"GROUP BY ?locationLabel ?item ?itemLabel ?yob\n",
"ORDER BY DESC(?billions)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "226b278b-f7e4-40bf-9616-1003b5bd95d5",
"metadata": {},
"source": [
"```SPARQL\n",
"#Distinct millionaires\n",
"#title: Distinct millionaires\n",
"SELECT ?locationLabel ?item ?itemLabel ?yob (MAX(?million) as ?illions)\n",
"WHERE\n",
"{\n",
" ?item wdt:P2218 ?worth;\n",
" wdt:P569 ?dob.\n",
" ?item wdt:P19 ?location.\n",
" \n",
" FILTER(?worth<1000000000).\n",
" FILTER(?worth>1000000).\n",
" BIND(YEAR(?dob) AS ?yob).\n",
" BIND(?worth/1000000 AS ?million).\n",
" SERVICE wikibase:label { bd:serviceParam wikibase:language \"en,de\".}\n",
" MINUS {?item wdt:P31 wd:Q15632617.}\n",
"}\n",
"GROUP BY ?locationLabel ?item ?itemLabel ?yob\n",
"ORDER BY DESC(?millions)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "f76385e1-a4a1-4c4c-a015-8d1831953fe2",
"metadata": {},
"source": [
"## Billionaires"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "95099e44-498f-47e9-adb0-e3e26d39beee",
"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>locationLabel</th>\n",
" <th>item</th>\n",
" <th>itemLabel</th>\n",
" <th>yob</th>\n",
" <th>billions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Pretoria</td>\n",
" <td>http://www.wikidata.org/entity/Q317521</td>\n",
" <td>Elon Musk</td>\n",
" <td>1971</td>\n",
" <td>270.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albuquerque</td>\n",
" <td>http://www.wikidata.org/entity/Q312556</td>\n",
" <td>Jeff Bezos</td>\n",
" <td>1964</td>\n",
" <td>204.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Jakarta</td>\n",
" <td>http://www.wikidata.org/entity/Q72845646</td>\n",
" <td>Hana Hasanah Shahab</td>\n",
" <td>1969</td>\n",
" <td>182.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Ahmedabad</td>\n",
" <td>http://www.wikidata.org/entity/Q5527865</td>\n",
" <td>Gautam Adani</td>\n",
" <td>1962</td>\n",
" <td>148.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Roubaix</td>\n",
" <td>http://www.wikidata.org/entity/Q32055</td>\n",
" <td>Bernard Arnault</td>\n",
" <td>1949</td>\n",
" <td>143.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" locationLabel item \\\n",
"0 Pretoria http://www.wikidata.org/entity/Q317521 \n",
"1 Albuquerque http://www.wikidata.org/entity/Q312556 \n",
"2 Jakarta http://www.wikidata.org/entity/Q72845646 \n",
"3 Ahmedabad http://www.wikidata.org/entity/Q5527865 \n",
"4 Roubaix http://www.wikidata.org/entity/Q32055 \n",
"\n",
" itemLabel yob billions \n",
"0 Elon Musk 1971 270.1 \n",
"1 Jeff Bezos 1964 204.6 \n",
"2 Hana Hasanah Shahab 1969 182.4 \n",
"3 Gautam Adani 1962 148.7 \n",
"4 Bernard Arnault 1949 143.0 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfb = pd.read_csv('query.tsv', sep='\\t')\n",
"dfb.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4b0a6b7a-0395-4121-938a-b33b5157cea9",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(dfb.yob, bins=40)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9f2b31a9-c61f-4ee5-acee-8d3851d14f85",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data=dfb, x='yob', y='billions', log_scale=(False, True))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "abe7f501-cd44-4cc4-8a8b-716c9855abfd",
"metadata": {},
"source": [
"## Millionaires"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "790bdff4-da1e-4f56-80ec-2f0def47206d",
"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>locationLabel</th>\n",
" <th>item</th>\n",
" <th>itemLabel</th>\n",
" <th>yob</th>\n",
" <th>millions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Berne</td>\n",
" <td>http://www.wikidata.org/entity/Q76906</td>\n",
" <td>Dieter Bohlen</td>\n",
" <td>1954</td>\n",
" <td>250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Grand Prairie</td>\n",
" <td>http://www.wikidata.org/entity/Q83287</td>\n",
" <td>Selena Gomez</td>\n",
" <td>1992</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Chicago</td>\n",
" <td>http://www.wikidata.org/entity/Q83338</td>\n",
" <td>Robin Williams</td>\n",
" <td>1951</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>New York City</td>\n",
" <td>http://www.wikidata.org/entity/Q103939</td>\n",
" <td>Charlie Sheen</td>\n",
" <td>1965</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Greater London</td>\n",
" <td>http://www.wikidata.org/entity/Q106377</td>\n",
" <td>Adé Bantu</td>\n",
" <td>1971</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" locationLabel item itemLabel \\\n",
"0 Berne http://www.wikidata.org/entity/Q76906 Dieter Bohlen \n",
"1 Grand Prairie http://www.wikidata.org/entity/Q83287 Selena Gomez \n",
"2 Chicago http://www.wikidata.org/entity/Q83338 Robin Williams \n",
"3 New York City http://www.wikidata.org/entity/Q103939 Charlie Sheen \n",
"4 Greater London http://www.wikidata.org/entity/Q106377 Adé Bantu \n",
"\n",
" yob millions \n",
"0 1954 250.0 \n",
"1 1992 50.0 \n",
"2 1951 100.0 \n",
"3 1965 10.0 \n",
"4 1971 1.5 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfm = pd.read_csv('query(1).tsv', sep='\\t').rename({'illions': 'millions'}, axis=1)\n",
"dfm = dfm[dfm['yob'] > 1900]\n",
"dfm.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "65cb664d-71e3-42cf-a822-f67a167f1e8e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(dfm.yob, bins=40)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "71dbee95-65b0-4a03-9bb9-7240e733ccab",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data=dfm, x='yob', y='millions', log_scale=(False, True))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "9be02246-4561-4d78-9b27-588199385b8f",
"metadata": {},
"source": [
"## Both billionaires and millionaires"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "573b626a-ea6e-4ce1-8494-98895749d6ad",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(dfb.yob, bins=40, histtype='step', label='billionaires')\n",
"plt.hist(dfm.yob, bins=40, histtype='step', label='millionaires')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "47cfbd5a-bbde-4372-a5ad-bcd9a90e210e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2500x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.histplot(data=dfb, x='yob', )\n",
"# sns.histplot(data=dfm, x='yob')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "a61c0ae9-7f6a-4422-8a83-6a21087d6f72",
"metadata": {},
"outputs": [],
"source": [
"_dfb = dfb.rename({'billions': 'illions'}, axis=1)\n",
"_dfb['type'] = 'billionaire'\n",
"_dfm = dfm.rename({'millions': 'illions'}, axis=1)\n",
"_dfm['type'] = 'millionaire'\n",
"df = pd.concat([_dfb, _dfm])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d40eb875-156d-41eb-8694-350a0383b1b4",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.10.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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