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census-bubble-map.py
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census-bubble-map.py
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import plotly.graph_objects as go
import argparse
import pandas as pd
parser = argparse.ArgumentParser(description='Draw a bubble map from processed data')
parser.add_argument('input', metavar='I', help='input file')
parser.add_argument('type')
args = parser.parse_args()
df = pd.read_csv(args.input)
df.head()
df['text'] = df['name'] + '<br>Population ' + (df['pop']).astype(str)
cities = []
scale = 20
fig = go.Figure()
df_large_cities = df[df['pop'] >= 10000]
df_large_remain = df[df['pop'] < 10000]
df_medium_cities = df_large_remain[df_large_remain['pop'] >= 1000]
df_medium_remain = df[df['pop'] < 1000]
df_small_cities = df_medium_remain[df_medium_remain['pop'] >= 100]
df_tiny_cities = df[df['pop'] < 100]
def add_graph(figure, df_sub, color, lower, upper):
figure.add_trace(go.Scattergeo(
locationmode='USA-states',
lon=df_sub['lon'],
lat=df_sub['lat'],
text=df_sub['text'],
marker=dict(
size=df_sub['pop'] / scale,
color=color,
line_color='rgb(40,40,40)',
line_width=0.5,
sizemode='area'
),
name='{0} - {1}'.format(lower, upper)))
add_graph(fig, df_large_cities, "royalblue", 10000, 100000)
add_graph(fig, df_medium_cities, "crimson", 1000, 10000)
add_graph(fig, df_small_cities, "lightseagreen", 100, 1000)
add_graph(fig, df_tiny_cities, "orange", 0, 100)
fig.update_layout(
title_text='2020 US Taiwanese Census by ' + args.type + '<br>(Click legend to toggle traces)',
showlegend=True,
geo=dict(
scope='usa',
landcolor='rgb(217, 217, 217)',
)
)
fig.show()