folium: create leaflet.js maps in Python

Folium enables you to combine the Python ecosystem's data manipulation power with the leaflet.js library's strengths in map visualization.

Folium makes it simple to view Python-manipulated data as an interactive leaflet map. Data can be linked to a map for choropleth visualizations, and sophisticated vector, raster, and HTML visualizations can be passed as markers on the map.

In the following example we load a geojson file containing the shapes of all zip codes in Massachusetts and join it with a dataframe that has population estimates per ZIP code.

import pandas as pd
import folium
from urllib.request import urlopen
import json

# load massachusetts zip geo json
with urlopen('https://raw.githubusercontent.com/OpenDataDE/State-zip-code-GeoJSON/master/ma_massachusetts_zip_codes_geo.min.json') as response:
    state_geo = json.load(response)

# load data with zip to population mapping
df = pd.read_csv('https://raw.githubusercontent.com/einblick-ai/data/main/mass-zip-population.csv?token=GHSAT0AAAAAABXSA5TXACHN7AD57IDWHVR2Y3D2QWQ')
df['zip'] = df['zip'].astype(str).str.pad(width=5, side='left', fillchar='0')

# setup map layer
m = folium.Map(location=[42.371824, -71.080926], zoom_start=8, tiles="cartodbpositron")
cp = folium.Choropleth(
    geo_data=state_geo,
    name="choropleth",
    data=df,
    columns=["zip", "population"],
    key_on="properties.ZCTA5CE10",
    fill_color="YlGn",
    fill_opacity=0.5,
    line_opacity=0.2,
    legend_name="population",
).add_to(m)

# add 'population' to geodata by looking it up by zip code in the df
df_index = df.set_index('zip')
for s in cp.geojson.data['features']:
    if s['properties']['ZCTA5CE10'] in df_index.index:
        s['properties']['population'] = f"{float(df_index.loc[s['properties']['ZCTA5CE10'], 'population']):,}" 
  
# adding a tooltip to the map
folium.GeoJsonTooltip(['ZCTA5CE10', 'population']).add_to(cp.geojson)
folium.LayerControl().add_to(m)
m

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Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.

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