Updated On : Apr-07,2020 Time Investment : ~30 mins

Interactive Maps using Folium¶

Table of Contents¶

Introduction
1. Loading Maps
2. Maps with Marker, Points, Polygon, Line, etc.
3. Choropleth Maps
4. Saving Maps as HTML
References

Introduction ¶

folium is a python map plotting library based on leaflet.js. After manipulating data in python, we can visualize it on an interactive map using folium. folium has a number of rich tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.

Dependencies¶

folium is mainly dependent on below-mentioned libraries:

branca, jinja2, and requests.

Installation¶

pip install folium
conda install folium -c conda-forge

We'll explain basic usage of folium like plotting scatter map and choropleth map using geospatial datasets from kaggle. We'll also explain various tilesets with examples as well as few basic functionalities.

Let’s start loading required libraries.

import pandas as pd
import numpy as np
import folium

Loading Datasets¶

We'll be loading 4 datasets that have longitude and latitude data for various locations on earth. Please download all datasets from kaggle.com to follow along.

district_wise_census = pd.read_csv("datasets/indian-census-data-with-geospatial-indexing/district wise population and centroids.csv")
district_wise_census.head()

	State	District	Latitude	Longitude	Population in 2001	Population in 2011
0	Andhra Pradesh	Anantapur	14.312066	77.460158	3640478	4081148
1	Andhra Pradesh	Chittoor	13.331093	78.927639	3745875	4174064
2	Andhra Pradesh	East Godavari	16.782718	82.243207	4901420	5154296
3	Andhra Pradesh	Guntur	15.884926	80.586576	4465144	4887813
4	Andhra Pradesh	Krishna	16.143873	81.148051	4187841	4517398

state_wise_centroid = pd.read_csv("datasets/indian-census-data-with-geospatial-indexing/state wise centroids_2011.csv")
state_wise_census = district_wise_census.groupby(by="State").sum()[["Population in 2001","Population in 2011"]].reset_index()
state_wise_census = state_wise_centroid.merge(state_wise_census, left_on="State", right_on="State")
state_wise_census.head()

	State	Longitude	Latitude	Population in 2001	Population in 2011
0	Andhra Pradesh	79.916203	16.554124	36120039	39250184
1	Arunachal Pradesh	94.545327	27.725765	1072160	1354556
2	Assam	92.657310	26.321341	19963729	23415900
3	Bihar	85.636774	25.771394	74151567	92737812
4	Chandigarh	76.758725	30.742600	900635	1055450

starbucks_locations = pd.read_csv("datasets/starbucks_store_locations.csv")
starbucks_locations = starbucks_locations.dropna().reset_index()
starbucks_locations.head()

	index	Brand	Store Number	Store Name	Ownership Type	Street Address	City	State/Province	Country	Postcode	Phone Number	Timezone	Longitude	Latitude
0	0	Starbucks	47370-257954	Meritxell, 96	Licensed	Av. Meritxell, 96	Andorra la Vella	7	AD	AD500	376818720	GMT+1:00 Europe/Andorra	1.53	42.51
1	11	Starbucks	1579-122101	HCT Abu Dhabi Women's College Block	Licensed	Najda Street, Higher Colleges of Technology	Abu Dhabi	AZ	AE	3167	26426280	GMT+04:00 Asia/Dubai	54.37	24.49
2	12	Starbucks	32595-122105	Standard Chartered Building	Licensed	Khalidiya St., Beside Union Cooperative Society	Abu Dhabi	AZ	AE	3167	26359275	GMT+04:00 Asia/Muscat	55.69	24.19
3	20	Starbucks	32767-131566	Shangri-La Souq	Licensed	Shangri-La Souk, Um Al Nar	Abu Dhabi	AZ	AE	3167	25581641	GMT+04:00 Asia/Dubai	54.51	24.42
4	45	Starbucks	32640-131563	Tawam Hospital	Licensed	Al Ain Abu Dhabi Rd, Khalifa Bin Zayed, Al Mak...	Al Ain	AZ	AE	3167	37677581	GMT+04:00 Asia/Muscat	55.65	24.19

happiness_report = pd.read_csv("datasets/world_happiness_2019.csv")
happiness_report.head()

	Overall rank	Country or region	Score	GDP per capita	Social support	Healthy life expectancy	Freedom to make life choices	Generosity	Perceptions of corruption
0	1	Finland	7.769	1.340	1.587	0.986	0.596	0.153	0.393
1	2	Denmark	7.600	1.383	1.573	0.996	0.592	0.252	0.410
2	3	Norway	7.554	1.488	1.582	1.028	0.603	0.271	0.341
3	4	Iceland	7.494	1.380	1.624	1.026	0.591	0.354	0.118
4	5	Netherlands	7.488	1.396	1.522	0.999	0.557	0.322	0.298

1. Loading Maps ¶

We'll start by showing a few examples of how to load maps with just single latitude and longitude. Below we are loading Surat city from the Gujarat State of India from where coderzcolumn developers belong.

surat_city_long = district_wise_census[district_wise_census["District"] == "Surat"].Longitude.values[0]
surat_city_lat = district_wise_census[district_wise_census["District"] == "Surat"].Latitude.values[0]

m = folium.Map(location=[surat_city_lat, surat_city_long])
m

Folium provides a list of tiles as mentioned below. We'll need APIs for using Cloudmade and Mapbox tiles.

"OpenStreetMap" (Default)
"Mapbox Bright" (Limited levels of zoom for free tiles)
"Mapbox Control Room" (Limited levels of zoom for free tiles)
"Stamen" (Terrain, Toner, and Watercolor)
"Cloudmade" (Must pass API key)
"Mapbox" (Must pass API key)
"CartoDB" (positron and dark_matter)

Below we are loading again same city map with tile Stamen Toner.

m = folium.Map(location=[surat_city_lat, surat_city_long], tiles="Stamen Toner",zoom_start=10)
m

Another map with Stamen Terrain tile. We have also added LatLongPopup to map so that it brings in a popup at a location in a graph where you click displaying Latitude and Longitude of that location.

m = folium.Map(location=[surat_city_lat, surat_city_long], tiles="Stamen Terrain",zoom_start=10)

folium.LatLngPopup().add_to(m)

m

Below we have loaded another map by taking center latitude and longitude for India and then have set bounds to cover in a map using FitBounds.

center_lat = state_wise_census.mean().Latitude
center_long = state_wise_census.mean().Longitude

m = folium.Map(location=[center_lat, center_long], tiles="Stamen Terrain")

folium.FitBounds([(center_lat-5,center_long-5), (center_lat+5,center_long+5)]).add_to(m)

m

2. Maps with Marker, Points, Polygon, Line, etc. ¶

We'll start adding data to above-described maps with points, markers, polygon, lines plotted on it. We'll explain a few examples describing various features of folium.

2.1. Indian State Population Bubble Chart ¶

We are plotting a state-wise census bubble chart for India using CircleMarker. We also have used tooltip and popup for displaying a count of the population for states. We also have taken population count to calculate the radius of the bubble by dividing it by 1e7 so that it stays within bounds and does not flood on map.

center_lat = state_wise_census.mean().Latitude
center_long = state_wise_census.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=4.5)

for state in state_wise_census["State"].unique():
    state_census = state_wise_census[state_wise_census["State"]==state]
    folium.CircleMarker(
        location=[state_census.Latitude.values[0], state_census.Longitude.values[0]],
        radius = float(state_census["Population in 2011"].values[0]/1e7),
        popup="Population 2011 : %s"%state_census["Population in 2011"].values[0],
        tooltip = state_census.State.values[0],
        color="black",
        fill_color="black"
    ).add_to(m)

m

2.2 Indian States Census Scatter Map ¶

We have again plotted a map with a statewide census of India. We have used rectangle this time instead of circles.

center_lat = state_wise_census.mean().Latitude
center_long = state_wise_census.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=4.5, tiles="Stamen Toner")

for state in state_wise_census["State"].unique():
    state_census = state_wise_census[state_wise_census["State"]==state]
    lat, lon = state_census.Latitude.values[0], state_census.Longitude.values[0]
    population = state_census["Population in 2011"].values[0]
    folium.Rectangle(
        bounds=[(lat, lon), (lat+(population/1e8), lon+(population/1e8))],
        popup="Population 2011 : %s"%population,
        tooltip = state_census.State.values[0],
        color="red",
        fill_color="red"
    ).add_to(m)

m

2.3 US Statewise Starbucks Store Counts Scatter Map ¶

We have first calculated the US states Starbucks store counts. We have then taken an average of latitude and longitude for the location of stores in each state. We are then plotting a scatter plot on it using CircleMarker where circle size represents a number of stores.

starbucks_us = starbucks_locations[starbucks_locations["Country"] == "US"].reset_index()
starbucks_us_state_count = starbucks_us.groupby(by="State/Province").count()[["index"]].rename(columns={"index":"Count"}).reset_index()
starbucks_us_lat_lon = starbucks_us.groupby(by="State/Province").mean()[["Latitude", "Longitude"]].reset_index()
starbucks_us = starbucks_us_state_count.merge(starbucks_us_lat_lon, right_on="State/Province", left_on="State/Province").reset_index()

center_lat = starbucks_us.mean().Latitude
center_long = starbucks_us.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=4, tiles="Stamen Toner")

for idx in starbucks_us.index:
    row = starbucks_us.iloc[idx]
    lat, long = row.Latitude, row.Longitude
    folium.CircleMarker(
        location=[lat, long],
        radius = float(row.Count/100),
        draggable = False,
        color="red",
        popup = row["State/Province"],
        tooltip = str(row["State/Province"]) + ", " + str(row["Count"]),
        fill_color="yellow"
        ).add_to(m)
m

NOTE

Please make a note that if you are plotting too many points like more than 1k points and try to display it in jupyter notebook then it won't render due to some limitation of chrome/notebook. If you are plotting too many points and jupyter notebook is not displaying output then it's a better idea to save the map as HTML file and open it in chrome to see output.

2.4 Indian State (Gujarat) Districts Census Scatter Map ¶

We have created another scatter map that represents district wise census for the Gujarat state of India. We have used tile CartoDB dark_matter for this chart. Each bubble in the map represents the population of that districts. We have also used tooltip and popup for displaying census of the district.

gujarat_districts = district_wise_census[district_wise_census["State"]=="Gujarat"]

center_lat = gujarat_districts.mean().Latitude
center_long = gujarat_districts.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=6.5, tiles="CartoDB dark_matter")

for district in gujarat_districts["District"].unique():
    district_census = gujarat_districts[gujarat_districts["District"]==district]
    folium.Circle(
        location=[district_census.Latitude.values[0], district_census.Longitude.values[0]],
        radius = float(district_census["Population in 2011"].values[0]/100),
        color="orange",
        fill_color="orange",
        popup="Population 2011 : %s"%district_census["Population in 2011"].values[0],
        tooltip = district_census.District.values[0],
    ).add_to(m)

m

2.5 Indian State (Gujarat) Districts Census Scatter Map ¶

The below the map is almost the same as above map with bubbles replaced by RegularPolygonMarker.

gujarat_districts = district_wise_census[district_wise_census["State"]=="Gujarat"]

center_lat = gujarat_districts.mean().Latitude
center_long = gujarat_districts.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=6.5, tiles="CartoDB dark_matter")

for district in gujarat_districts["District"].unique():
    district_census = gujarat_districts[gujarat_districts["District"]==district]
    folium.RegularPolygonMarker(
        location=[district_census.Latitude.values[0], district_census.Longitude.values[0]],
        radius = float(district_census["Population in 2011"].values[0]/100000),
        rotation=0,
        color="beige",
        popup="Population 2011 : %s"%district_census["Population in 2011"].values[0],
        tooltip = district_census.District.values[0],
        fill_color="white"
    ).add_to(m)

m

2.6 Indian State (Gujarat) Districts Census with Markers ¶

We have plotted a map with markers on various districts of Gujarat state of India. We have also used popup and tooltip to display census for district in 2011.

gujarat_districts = district_wise_census[district_wise_census["State"]=="Gujarat"]

center_lat = gujarat_districts.mean().Latitude
center_long = gujarat_districts.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=6.5, tiles="CartoDB positron")

for district in gujarat_districts["District"].unique():
    district_census = gujarat_districts[gujarat_districts["District"]==district]
    folium.Marker(
        location=[district_census.Latitude.values[0], district_census.Longitude.values[0]],
        popup="Population 2011 : %s"%district_census["Population in 2011"].values[0],
        tooltip = district_census.District.values[0],
        icon=folium.Icon(icon='info-sign', color="red"),
        draggable=False
    ).add_to(m)

m

2.7 New York City Starbucks Locations Map with Markers ¶

Below we are using markers on the new york city map where each marker represents the Starbucks store. We have also used popup and tooltip which displays store address.

starbucks_ny = starbucks_locations[starbucks_locations["City"] == "New York"].reset_index()

center_lat = starbucks_ny.mean().Latitude
center_long = starbucks_ny.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=11.5, )#tiles="Stamen Toner")

for idx in starbucks_ny.index:
    row = starbucks_ny.iloc[idx]
    lat, long = row.Latitude, row.Longitude
    folium.Marker(
        location=[lat, long],
        draggable=False,
        popup= row["Store Name"],
        tooltip = str(row["Street Address"]) + ", " + str(row["City"]),
        icon=folium.Icon(icon='info-sign', color="green")
        ).add_to(m)


m

2.8 Indian State (Gujarat) Districts Census with Markers & Lines Connecting Districts ¶

Below we are plotting markers on the state of Gujarat (India) where each marker represents districts of the state. We have then tried to link all districts by using PolyLine. PolyLine lets us display lines on the map.

gujarat_districts = district_wise_census[district_wise_census["State"]=="Gujarat"]

center_lat = gujarat_districts.mean().Latitude
center_long = gujarat_districts.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=6.5, tiles="Stamen Terrain")

for district in gujarat_districts["District"].unique():
    district_census = gujarat_districts[gujarat_districts["District"]==district]
    folium.Marker(
        location=[district_census.Latitude.values[0], district_census.Longitude.values[0]],
        popup="Population 2011 : %s"%district_census["Population in 2011"].values[0],
        tooltip = district_census.District.values[0],
        icon=folium.Icon(icon='info-sign', color="orange")
    ).add_to(m)

folium.PolyLine(
    locations=sorted(zip(gujarat_districts.Latitude, gujarat_districts.Longitude)),
    popup="Path",
    tooltip = "Path",
    color="green",
).add_to(m)

m

2.9 Indian State (Gujarat) Districts Census with Markers & Polygons ¶

Below we are plotting markers on the state of Gujarat (India) where each marker represents districts of the state. We are then plotting polygons to identify a particular area by calling Polygon method of folium passing it list of locations. We are plotting 2 random polygons to display use of it.

gujarat_districts = district_wise_census[district_wise_census["State"]=="Gujarat"]

center_lat = gujarat_districts.mean().Latitude
center_long = gujarat_districts.mean().Longitude

m = folium.Map(location=[center_lat, center_long], zoom_start=6.5, tiles="Stamen Toner")

for district in gujarat_districts["District"].unique():
    district_census = gujarat_districts[gujarat_districts["District"]==district]
    folium.Marker(
        location=[district_census.Latitude.values[0], district_census.Longitude.values[0]],
        popup="Population 2011 : %s"%district_census["Population in 2011"].values[0],
        tooltip = district_census.District.values[0],
        icon=folium.Icon(icon='info-sign', color="darkpurple")
    ).add_to(m)

folium.Polygon(
    locations=list(zip(gujarat_districts.Latitude, gujarat_districts.Longitude))[:3],
    popup="Path",
    tooltip = "Path",
    color="green",
    fill_color="green"
).add_to(m)

folium.Polygon(
    locations=list(zip(gujarat_districts.Latitude, gujarat_districts.Longitude))[8:12],
    popup="Path",
    tooltip = "Path",
    color="red",
    fill_color="red"
).add_to(m)

m

3. Choropleth Maps ¶

We'll now explain how to plot choropleth maps using folium.

3.1 US States Starbucks Stores Counts Choropleth ¶

We need polygon (list of latitudes and longitudes) representing the region to plot choropleth maps with folium. Below we are downloading USA states geo JSON which has polygon for each states of US. We are then linking it to columns of Starbucks locations dataframe. The linking between polygon JSON and pandas dataframe happens based on one key column the same between them. We then pass another column to columns attribute of Choropleth() for which we need to create choropleth maps.

url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'
state_geo = f'{url}/us-states.json'

starbucks_us_state_count = starbucks_locations.groupby(by="State/Province").count()[["index"]].rename(columns={"index":"Count"}).reset_index()

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=state_geo,
    name='choropleth',
    data=starbucks_us_state_count,
    columns=['State/Province', 'Count'],
    key_on='feature.id',
    fill_color='YlGn',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='Starbucks Store Count'
).add_to(m)

folium.LayerControl().add_to(m)

m

3.2 World Happiness Score Choropleth ¶

For this map, we need to download geo JSON for a world with polygon representing each country of the earth. We suggest that you download it from here. We noticed that feature key of JSON has a key named properties which has a dictionary with a key named sovereignt that maintains country name. The country named mentioned in JSON at feature.properties.sovereignt matches with a country named mentioned in Country or region column of happiness report dataframe.

m = folium.Map(location=[0,0], zoom_start=1.5)

folium.Choropleth(
    geo_data="datasets/custom.geo.json",
    name='choropleth',
    data=happiness_report,
    columns=['Country or region', 'Score'],
    key_on='feature.properties.sovereignt',
    fill_color='BrBG',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='Happiness Score'
).add_to(m)

folium.LayerControl().add_to(m)

m

3.3 World GDP Per Capita Choropleth Map ¶

Below is another choropleth map linking GDP per capita column of happiness report dataframe with a world map.

m = folium.Map(location=[0,0], zoom_start=1.5)

folium.Choropleth(
    geo_data="datasets/custom.geo.json",
    name='choropleth',
    data=happiness_report,
    columns=['Country or region', 'GDP per capita'],
    key_on='feature.properties.sovereignt',
    fill_color='PuOr',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='GDP per capita'
).add_to(m)

folium.LayerControl().add_to(m)

m

3.4 World Perception of Corruption Choropleth Map ¶

Below is another choropleth map linking Perceptions of corruption column of happiness report dataframe with a world map.

m = folium.Map(location=[0,0], zoom_start=1.5)

folium.Choropleth(
    geo_data="datasets/custom.geo.json",
    name='choropleth',
    data=happiness_report,
    columns=['Country or region', 'Perceptions of corruption'],
    key_on='feature.properties.sovereignt',
    fill_color='PiYG',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='Perceptions of corruption'
).add_to(m)

folium.LayerControl().add_to(m)

m

3.5 World Freedom to Make Life Choices Choropleth Map ¶

Below is another choropleth map linking Freedom to make life choices column of happiness report dataframe with a world map.

m = folium.Map(location=[0,0], zoom_start=1.5)

folium.Choropleth(
    geo_data="datasets/custom.geo.json",
    name='choropleth',
    data=happiness_report,
    columns=['Country or region', 'Freedom to make life choices'],
    key_on='feature.properties.sovereignt',
    fill_color='RdYlGn',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='Freedom to make life choices'
).add_to(m)

folium.LayerControl().add_to(m)

m

3.6 World Healthy Life Expectancy Choropleth Map ¶

Below is another choropleth map linking Healthy life expectancy column of happiness report dataframe with a world map.

m = folium.Map(location=[0,0], zoom_start=1.5)

folium.Choropleth(
    geo_data="datasets/custom.geo.json",
    name='choropleth',
    data=happiness_report,
    columns=['Country or region', 'Healthy life expectancy'],
    key_on='feature.properties.sovereignt',
    fill_color='RdGy',
    fill_opacity=0.9,
    line_opacity=0.2,
    legend_name='Healthy life expectancy'
).add_to(m)

folium.LayerControl().add_to(m)

m

We can change various colormaps for choropleth maps. Please look at this link to try various colormaps.

4. Saving Maps as HTML ¶

We can save folium plots as HTML files by calling save() method on map objects.

m.save("Healthy life expectancy.html")

This ends our small tutorial on folium library usage. Please feel free to let us know your views in the comments.

References ¶

Sunny Solanki

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Interactive Maps using Folium¶

Table of Contents¶

Introduction ¶

Dependencies¶

Installation¶

Loading Datasets¶

1. Loading Maps ¶

2. Maps with Marker, Points, Polygon, Line, etc. ¶

2.1. Indian State Population Bubble Chart ¶

2.2 Indian States Census Scatter Map ¶

2.3 US Statewise Starbucks Store Counts Scatter Map ¶

NOTE

2.4 Indian State (Gujarat) Districts Census Scatter Map ¶

2.5 Indian State (Gujarat) Districts Census Scatter Map ¶

2.6 Indian State (Gujarat) Districts Census with Markers ¶

2.7 New York City Starbucks Locations Map with Markers ¶

2.8 Indian State (Gujarat) Districts Census with Markers & Lines Connecting Districts ¶

2.9 Indian State (Gujarat) Districts Census with Markers & Polygons ¶

3. Choropleth Maps ¶

3.1 US States Starbucks Stores Counts Choropleth ¶

3.2 World Happiness Score Choropleth ¶

3.3 World GDP Per Capita Choropleth Map ¶

3.4 World Perception of Corruption Choropleth Map ¶

3.5 World Freedom to Make Life Choices Choropleth Map ¶

3.6 World Healthy Life Expectancy Choropleth Map ¶

4. Saving Maps as HTML ¶

References ¶

Sunny Solanki

Comfortable Learning through Video Tutorials?

Stuck Somewhere? Need Help with Coding? Have Doubts About the Topic/Code?

Want to Share Your Views? Have Any Suggestions?

Sunny Solanki

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