billionaries-wizualizacja/projekt2.ipynb

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"source": [
"import folium\n",
"import warnings\n",
"import pandas as pd \n",
"import plotly.express as px\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"\n",
"from difflib import get_close_matches\n",
"from plotly.subplots import make_subplots\n",
"\n",
"plt.style.use('ggplot')\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read and clean up data \n",
"\n",
"Remove nans value and replace them from: latitude country, longitude_country, country, city, gdp_country."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('data.csv')\n",
"\n",
"df.latitude_country = df.latitude_country.fillna(0.0)\n",
"df.longitude_country = df.longitude_country.fillna(0.0)\n",
"df.country = df.country.fillna('Unknown')\n",
"df.city = df.city.fillna('Unknown')\n",
"df.gdp_country = df.gdp_country.fillna(0.0)\n",
"df.finalWorth = df.finalWorth / 1000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactive plots using plotly"
]
},
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"### Plot made using ploty that dipslay on hover age, net worth and name of billionaire"
]
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"type": "scatterpolar"
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{
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"ticks": ""
}
},
"type": "scatterpolargl"
}
],
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{
"marker": {
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"ticks": ""
}
},
"type": "scatterternary"
}
],
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0.5555555555555556,
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0.6666666666666666,
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0.7777777777777778,
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"showland": true,
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"align": "left"
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"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
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"gridcolor": "white",
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"ticks": "",
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"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Distribution of Gender and SelfMade by Gender"
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gender_counts = df['gender'].str.split(',').explode().value_counts()\n",
"\n",
"df_women = df[df['gender'] == 'F']\n",
"selfMade_counts_women = df_women['selfMade'].value_counts()\n",
"\n",
"df_men = df[df['gender'] == 'M']\n",
"selfMade_counts_men = df_men['selfMade'].value_counts()\n",
"\n",
"fig1 = go.Figure(data=[go.Pie(labels=gender_counts.index, values=gender_counts, hole=0.3)])\n",
"fig1.update_traces(textinfo='percent+label')\n",
"\n",
"fig2 = go.Figure(data=[go.Pie(labels=selfMade_counts_men.index, values=selfMade_counts_men, hole=0.3)])\n",
"fig2.update_traces(textinfo='percent+label')\n",
"\n",
"fig3 = go.Figure(data=[go.Pie(labels=selfMade_counts_women.index, values=selfMade_counts_women, hole=0.3)])\n",
"fig3.update_traces(textinfo='percent+label')\n",
"\n",
"fig = make_subplots(rows=1, cols=3, specs=[[{'type': 'domain'}, {'type': 'domain'},{'type': 'sunburst'}]])\n",
"\n",
"fig.add_trace(fig1.data[0], row=1, col=1)\n",
"fig.add_trace(fig2.data[0], row=1, col=2)\n",
"fig.add_trace(fig3.data[0], row=1, col=3)\n",
"\n",
"fig.update_layout(height=400, showlegend=False, title_text=\"Distribution of Gender and SelfMade by Gender\")\n",
"fig.add_annotation(dict(x=0.14, y=-0.1, ax=0, ay=0, text=\"% Gender Distribution\"))\n",
"fig.add_annotation(dict(x=0.50, y=-0.1, ax=0, ay=0, text=\"% SelfMade (M)\"))\n",
"fig.add_annotation(dict(x=0.85, y=-0.1, ax=0, ay=0, text=\"% SelfMade (F)\"))\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Maps using plotly and folium"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Map made using folium on click show name of country and numbers of billionaries from that country"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" \n",
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" var html_bbb74b572e3deb3f19b1664d4b308bb6 = $(`&lt;div id=&quot;html_bbb74b572e3deb3f19b1664d4b308bb6&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Ukraine Num of billionaires 6&lt;/div&gt;`)[0];\n",
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"\n",
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" \n",
" \n",
" var html_ef577b6cac2ef8bbb8865276bcdac2c4 = $(`&lt;div id=&quot;html_ef577b6cac2ef8bbb8865276bcdac2c4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Liechtenstein Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_620852fbe54d639aa258e69bcef25bbc.setContent(html_ef577b6cac2ef8bbb8865276bcdac2c4);\n",
" \n",
" \n",
"\n",
" marker_ff958ca758de41eaaf9d9c870e503fc1.bindPopup(popup_620852fbe54d639aa258e69bcef25bbc)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_e46c14ef58d2a703655fb3bef68f1315 = L.marker(\n",
" [0.0, 0.0],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_eee136a0c2a42a7b6ac905ed2b27a23e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_7b059581fa0e9bb4cc5a1d498289cf85 = $(`&lt;div id=&quot;html_7b059581fa0e9bb4cc5a1d498289cf85&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Turks and Caicos Islands Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_eee136a0c2a42a7b6ac905ed2b27a23e.setContent(html_7b059581fa0e9bb4cc5a1d498289cf85);\n",
" \n",
" \n",
"\n",
" marker_e46c14ef58d2a703655fb3bef68f1315.bindPopup(popup_eee136a0c2a42a7b6ac905ed2b27a23e)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_9f9c72c10ac531962a29b79748b47a22 = L.marker(\n",
" [45.943161, 24.966760000000004],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_522b43e7e6f4465ddb4be305f5ff3e98 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_5d3de0b0d0647830d795ad8efea956cf = $(`&lt;div id=&quot;html_5d3de0b0d0647830d795ad8efea956cf&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Romania Num of billionaires 3&lt;/div&gt;`)[0];\n",
" popup_522b43e7e6f4465ddb4be305f5ff3e98.setContent(html_5d3de0b0d0647830d795ad8efea956cf);\n",
" \n",
" \n",
"\n",
" marker_9f9c72c10ac531962a29b79748b47a22.bindPopup(popup_522b43e7e6f4465ddb4be305f5ff3e98)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_2a8c40d13f4add62083bc867a6ae3f8c = L.marker(\n",
" [25.354826, 51.183884],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_62ee1351742968cf8640b6537400da03 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_347cdca68f87102685ca149db95e854d = $(`&lt;div id=&quot;html_347cdca68f87102685ca149db95e854d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Qatar Num of billionaires 2&lt;/div&gt;`)[0];\n",
" popup_62ee1351742968cf8640b6537400da03.setContent(html_347cdca68f87102685ca149db95e854d);\n",
" \n",
" \n",
"\n",
" marker_2a8c40d13f4add62083bc867a6ae3f8c.bindPopup(popup_62ee1351742968cf8640b6537400da03)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_845c7e6b7428be06c205a29e5f1f27aa = L.marker(\n",
" [-32.522779, -55.765835],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_708b3ee388bd18cd7ff18ddd9051b339 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_61d76dcc9528a14bfc4a174df84ea731 = $(`&lt;div id=&quot;html_61d76dcc9528a14bfc4a174df84ea731&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Uruguay Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_708b3ee388bd18cd7ff18ddd9051b339.setContent(html_61d76dcc9528a14bfc4a174df84ea731);\n",
" \n",
" \n",
"\n",
" marker_845c7e6b7428be06c205a29e5f1f27aa.bindPopup(popup_708b3ee388bd18cd7ff18ddd9051b339)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_5b87878641705f16f907cb2d6359c614 = L.marker(\n",
" [28.394857, 84.124008],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_1eb8198da25a93ab913635112cc4299b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_5185537c02391b72732956de911c3786 = $(`&lt;div id=&quot;html_5185537c02391b72732956de911c3786&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Nepal Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_1eb8198da25a93ab913635112cc4299b.setContent(html_5185537c02391b72732956de911c3786);\n",
" \n",
" \n",
"\n",
" marker_5b87878641705f16f907cb2d6359c614.bindPopup(popup_1eb8198da25a93ab913635112cc4299b)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_7f26b8f731b9f7a24bccb757d8a7e526 = L.marker(\n",
" [48.669026, 19.699024],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_0f5e3dce14b6bbe9f16a23db226cdf57 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_f19196ad875791d025056d3450e4a205 = $(`&lt;div id=&quot;html_f19196ad875791d025056d3450e4a205&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Slovakia Num of billionaires 2&lt;/div&gt;`)[0];\n",
" popup_0f5e3dce14b6bbe9f16a23db226cdf57.setContent(html_f19196ad875791d025056d3450e4a205);\n",
" \n",
" \n",
"\n",
" marker_7f26b8f731b9f7a24bccb757d8a7e526.bindPopup(popup_0f5e3dce14b6bbe9f16a23db226cdf57)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_0aaf3936188e7f97cc34ae5cbc4f6686 = L.marker(\n",
" [31.791702, -7.09262],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_1abee6470459358a62116e9e8165d7d3 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_bd234d3a9d9a9bcdc013ec1c16794539 = $(`&lt;div id=&quot;html_bd234d3a9d9a9bcdc013ec1c16794539&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Morocco Num of billionaires 2&lt;/div&gt;`)[0];\n",
" popup_1abee6470459358a62116e9e8165d7d3.setContent(html_bd234d3a9d9a9bcdc013ec1c16794539);\n",
" \n",
" \n",
"\n",
" marker_0aaf3936188e7f97cc34ae5cbc4f6686.bindPopup(popup_1abee6470459358a62116e9e8165d7d3)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_af8eb2c0bc4aa3eb886f0583f5cc874f = L.marker(\n",
" [47.162494, 19.503304],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_c21f83809f712887b57e7ddecc2432d5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_9aa1e8a60b0e70bc18c5f0db86bf11a7 = $(`&lt;div id=&quot;html_9aa1e8a60b0e70bc18c5f0db86bf11a7&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Hungary Num of billionaires 3&lt;/div&gt;`)[0];\n",
" popup_c21f83809f712887b57e7ddecc2432d5.setContent(html_9aa1e8a60b0e70bc18c5f0db86bf11a7);\n",
" \n",
" \n",
"\n",
" marker_af8eb2c0bc4aa3eb886f0583f5cc874f.bindPopup(popup_c21f83809f712887b57e7ddecc2432d5)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_cb7b4e98deedcd540228de1277f78c14 = L.marker(\n",
" [-6.369028, 34.888822],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_a8b270343905be467ce6cf7abd77369e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_6cdd5fc11f7c8ea0caf39130b1276345 = $(`&lt;div id=&quot;html_6cdd5fc11f7c8ea0caf39130b1276345&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Tanzania Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_a8b270343905be467ce6cf7abd77369e.setContent(html_6cdd5fc11f7c8ea0caf39130b1276345);\n",
" \n",
" \n",
"\n",
" marker_cb7b4e98deedcd540228de1277f78c14.bindPopup(popup_a8b270343905be467ce6cf7abd77369e)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_b67f391b4f1a27d2c3da5e30bcf4cc3c = L.marker(\n",
" [26.0667, 50.5577],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_7a4e14209cc7d2a00e5edba237e4dc68 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_8cc6fbe144ceab98fc3093c989073f5b = $(`&lt;div id=&quot;html_8cc6fbe144ceab98fc3093c989073f5b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Bahrain Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_7a4e14209cc7d2a00e5edba237e4dc68.setContent(html_8cc6fbe144ceab98fc3093c989073f5b);\n",
" \n",
" \n",
"\n",
" marker_b67f391b4f1a27d2c3da5e30bcf4cc3c.bindPopup(popup_7a4e14209cc7d2a00e5edba237e4dc68)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_737474b1f396e72762c451e5f7ee9572 = L.marker(\n",
" [-9.189967, -75.015152],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_f66e2f555ab16a36839902bd55dc548c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_eedc073c92ab5e1ee670fe1f6ada4034 = $(`&lt;div id=&quot;html_eedc073c92ab5e1ee670fe1f6ada4034&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Peru Num of billionaires 2&lt;/div&gt;`)[0];\n",
" popup_f66e2f555ab16a36839902bd55dc548c.setContent(html_eedc073c92ab5e1ee670fe1f6ada4034);\n",
" \n",
" \n",
"\n",
" marker_737474b1f396e72762c451e5f7ee9572.bindPopup(popup_f66e2f555ab16a36839902bd55dc548c)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_7c4662b0d9dbc056a737fba63f440044 = L.marker(\n",
" [42.506285, 1.521801],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_3936c8b9ae3543bf74726e90eb2ab738 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_968d843d589272afba16ae2dc452ee9d = $(`&lt;div id=&quot;html_968d843d589272afba16ae2dc452ee9d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Andorra Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_3936c8b9ae3543bf74726e90eb2ab738.setContent(html_968d843d589272afba16ae2dc452ee9d);\n",
" \n",
" \n",
"\n",
" marker_7c4662b0d9dbc056a737fba63f440044.bindPopup(popup_3936c8b9ae3543bf74726e90eb2ab738)\n",
" ;\n",
"\n",
" \n",
" \n",
" \n",
" var marker_173e13d77e850640b4f25624ed27763f = L.marker(\n",
" [40.069099, 45.038189],\n",
" {}\n",
" ).addTo(map_7ec748345796ae3818396c62d2451748);\n",
" \n",
" \n",
" var popup_1e134c2762435609d3c362262f96c41b = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
"\n",
" \n",
" \n",
" var html_5603262aad429c70755ff88acb01fedb = $(`&lt;div id=&quot;html_5603262aad429c70755ff88acb01fedb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;Armenia Num of billionaires 1&lt;/div&gt;`)[0];\n",
" popup_1e134c2762435609d3c362262f96c41b.setContent(html_5603262aad429c70755ff88acb01fedb);\n",
" \n",
" \n",
"\n",
" marker_173e13d77e850640b4f25624ed27763f.bindPopup(popup_1e134c2762435609d3c362262f96c41b)\n",
" ;\n",
"\n",
" \n",
" \n",
"&lt;/script&gt;\n",
"&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7421123a7370>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[40, -100], zoom_start=4)\n",
"\n",
"for country in df['country'].unique():\n",
" lat, lon = df[df['country'] == country]['latitude_country'].mean(), df[df['country'] == country]['longitude_country'].mean()\n",
" num_billionaires = len(df[df['country'] == country])\n",
" folium.Marker([lat, lon], popup=f\"{country} Num of billionaires {num_billionaires}\").add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adjust states names to display"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['TX', 'WA', 'HI', 'NE', 'NY', 'CA', 'KS', 'AR', 'OR', 'VA', 'WY',\n",
" 'NV', 'FL', 'PA', 'MA', 'IL', 'TN', 'CT', 'OK', 'MI', 'WI', 'CO',\n",
" 'IN', 'NH', 'GA', 'MO', 'NJ', 'NC', 'IA', 'KY', 'MD', 'MT', 'OH',\n",
" 'SC', 'AZ', 'LA', 'UT', 'RI', 'ID', 'VI', 'MN', 'ME', 'MS', 'SD',\n",
" 'AL'], dtype=object)"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dd= df.dropna(subset=[\"state\"])\n",
"\n",
"states = {\n",
" 'AK': 'Alaska', 'AL': 'Alabama', 'AR': 'Arkansas', 'AS': 'American Samoa',\n",
" 'AZ': 'Arizona', 'CA': 'California', 'CO': 'Colorado', 'CT': 'Connecticut',\n",
" 'DC': 'District of Columbia', 'DE': 'Delaware', 'FL': 'Florida', 'GA': 'Georgia',\n",
" 'GU': 'Guam', 'HI': 'Hawaii', 'IA': 'Iowa', 'ID': 'Idaho', 'IL': 'Illinois',\n",
" 'IN': 'Indiana', 'KS': 'Kansas', 'KY': 'Kentucky', 'LA': 'Louisiana', 'MA': 'Massachusetts',\n",
" 'MD': 'Maryland', 'ME': 'Maine', 'MI': 'Michigan', 'MN': 'Minnesota', 'MO': 'Missouri',\n",
" 'MP': 'Northern Mariana Islands', 'MS': 'Mississippi', 'MT': 'Montana', 'NA': 'National',\n",
" 'NC': 'North Carolina', 'ND': 'North Dakota', 'NE': 'Nebraska', 'NH': 'New Hampshire',\n",
" 'NJ': 'New Jersey', 'NM': 'New Mexico', 'NV': 'Nevada', 'NY': 'New York', 'OH': 'Ohio',\n",
" 'OK': 'Oklahoma', 'OR': 'Oregon', 'PA': 'Pennsylvania', 'PR': 'Puerto Rico', 'RI': 'Rhode Island',\n",
" 'SC': 'South Carolina', 'SD': 'South Dakota', 'TN': 'Tennessee', 'TX': 'Texas', 'UT': 'Utah',\n",
" 'VA': 'Virginia', 'VI': 'Virgin Islands', 'VT': 'Vermont', 'WA': 'Washington', 'WI': 'Wisconsin',\n",
" 'WV': 'West Virginia', 'WY': 'Wyoming'\n",
"}\n",
"\n",
"def best_match(x):\n",
" if len(x) == 2:\n",
" abbr = x.upper()\n",
" if abbr in states:\n",
" return abbr\n",
" else:\n",
" matches = get_close_matches(x, states.values(), n=1, cutoff=0.8)\n",
" if matches:\n",
" abbr = list(states.keys())[list(states.values()).index(matches[0])]\n",
" return abbr\n",
" return None\n",
"\n",
"dd['state_corrected'] = dd['state'].apply(lambda x: best_match(x))\n",
"\n",
"dd[\"state_corrected\"].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot make by plotly, displaying states in USA on hover: name of state, billionaries population in this state"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"coloraxis": "coloraxis",
"customdata": [
1,
6,
12,
178,
13,
14,
94,
17,
2,
1,
1,
24,
3,
2,
1,
1,
21,
10,
1,
10,
5,
7,
2,
4,
5,
3,
1,
5,
18,
128,
7,
6,
3,
19,
1,
2,
1,
12,
70,
6,
8,
1,
13,
7,
7
],
"geo": "geo",
"hovertemplate": "<b>%{text}</b><br>Billionaires Population: %{customdata}",
"locationmode": "USA-states",
"locations": [
"AL",
"AR",
"AZ",
"CA",
"CO",
"CT",
"FL",
"GA",
"HI",
"IA",
"ID",
"IL",
"IN",
"KS",
"KY",
"LA",
"MA",
"MD",
"ME",
"MI",
"MN",
"MO",
"MS",
"MT",
"NC",
"NE",
"NH",
"NJ",
"NV",
"NY",
"OH",
"OK",
"OR",
"PA",
"RI",
"SC",
"SD",
"TN",
"TX",
"UT",
"VA",
"VI",
"WA",
"WI",
"WY"
],
"marker": {
"line": {
"color": "white",
"width": 0.5
}
},
"name": "",
"text": [
"Alabama (AL)",
"Arkansas (AR)",
"Arizona (AZ)",
"California (CA)",
"Colorado (CO)",
"Connecticut (CT)",
"Florida (FL)",
"Georgia (GA)",
"Hawaii (HI)",
"Iowa (IA)",
"Idaho (ID)",
"Illinois (IL)",
"Indiana (IN)",
"Kansas (KS)",
"Kentucky (KY)",
"Louisiana (LA)",
"Massachusetts (MA)",
"Maryland (MD)",
"Maine (ME)",
"Michigan (MI)",
"Minnesota (MN)",
"Missouri (MO)",
"Mississippi (MS)",
"Montana (MT)",
"North Carolina (NC)",
"Nebraska (NE)",
"New Hampshire (NH)",
"New Jersey (NJ)",
"Nevada (NV)",
"New York (NY)",
"Ohio (OH)",
"Oklahoma (OK)",
"Oregon (OR)",
"Pennsylvania (PA)",
"Rhode Island (RI)",
"South Carolina (SC)",
"South Dakota (SD)",
"Tennessee (TN)",
"Texas (TX)",
"Utah (UT)",
"Virginia (VA)",
"Virgin Islands (VI)",
"Washington (WA)",
"Wisconsin (WI)",
"Wyoming (WY)"
],
"type": "choropleth",
"z": [
1,
6,
12,
178,
13,
14,
94,
17,
2,
1,
1,
24,
3,
2,
1,
1,
21,
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}
},
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],
"source": [
"d = dd[dd['country'] == 'United States']\n",
"d = dd.groupby('state_corrected')['personName'].count().reset_index(name='rich_pop_usa')\n",
"\n",
"fig = px.choropleth(d, locations=\"state_corrected\", locationmode='USA-states', \n",
" scope=\"usa\", color=\"rich_pop_usa\", \n",
" color_continuous_scale=\"Viridis\")\n",
"\n",
"fig.update_traces(customdata=d['rich_pop_usa'].values,\n",
" hovertemplate='<b>%{text}</b><br>Billionaires Population: %{customdata}',\n",
" \n",
" text=[f\"{states[abbr]} ({abbr})\" for abbr in d['state_corrected']],\n",
" marker_line_color='white', marker_line_width=0.5)\n",
"\n",
"fig.update_layout(\n",
" title_text='Rich Population by U.S. State',\n",
" geo_scope='usa',\n",
")\n",
"\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot make using plotly to display life expectancy in each conutry, on hover: name of country, life expectancy"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
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"United States",
"United States",
"United States",
"United States",
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