Note

This page was generated from gallery/plotting_with_folium.ipynb.

Interactive online version:

Plotting with Folium

What is Folium?

Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the leaflet.js library. This allows you to manipulate your data in Geopandas and visualize it on a Leaflet map via Folium.

In this example, we will first use Geopandas to load the geometries (volcano point data), and then create the Folium map with markers representing the different types of volcanoes.

Load geometries

This example uses a freely available volcano dataset. We will be reading the csv file using pandas, and then convert the pandas DataFrame to a Geopandas GeoDataFrame.

# Import Libraries
import pandas as pd
import geopandas
import folium
import matplotlib.pyplot as plt

df1 = pd.read_csv('volcano_data_2010.csv')

# Keep only relevant columns
df = df1.loc[:, ("Year", "Name", "Country", "Latitude", "Longitude", "Type")]
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 63 entries, 0 to 62
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype
---  ------     --------------  -----
 0   Year       63 non-null     int64
 1   Name       63 non-null     object
 2   Country    63 non-null     object
 3   Latitude   63 non-null     float64
 4   Longitude  63 non-null     float64
 5   Type       63 non-null     object
dtypes: float64(2), int64(1), object(3)
memory usage: 3.1+ KB

# Create point geometries
geometry = geopandas.points_from_xy(df.Longitude, df.Latitude)
geo_df = geopandas.GeoDataFrame(df[['Year','Name','Country', 'Latitude', 'Longitude', 'Type']], geometry=geometry)

geo_df.head()

	Year	Name	Country	Latitude	Longitude	Type	geometry
0	2010	Tungurahua	Ecuador	-1.467	-78.442	Stratovolcano	POINT (-78.44200 -1.46700)
1	2010	Eyjafjallajokull	Iceland	63.630	-19.620	Stratovolcano	POINT (-19.62000 63.63000)
2	2010	Pacaya	Guatemala	14.381	-90.601	Complex volcano	POINT (-90.60100 14.38100)
3	2010	Sarigan	United States	16.708	145.780	Stratovolcano	POINT (145.78000 16.70800)
4	2010	Karangetang [Api Siau]	Indonesia	2.780	125.480	Stratovolcano	POINT (125.48000 2.78000)

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
df.Type.unique()

array(['Stratovolcano', 'Complex volcano', 'Shield volcano',
       'Subglacial volcano', 'Lava dome', 'Caldera'], dtype=object)

fig, ax = plt.subplots(figsize=(24,18))
world.plot(ax=ax, alpha=0.4, color='grey')
geo_df.plot(column='Type', ax=ax, legend=True)
plt.title('Volcanoes')

Text(0.5, 1.0, 'Volcanoes')

Create Folium map

Folium has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen. For example:

# Stamen Terrain
map = folium.Map(location = [13.406,80.110], tiles = "Stamen Terrain", zoom_start = 9)
map

Make this Notebook Trusted to load map: File -> Trust Notebook

# OpenStreetMap
map = folium.Map(location = [13.406,80.110], tiles='OpenStreetMap' , zoom_start = 9)
map

Make this Notebook Trusted to load map: File -> Trust Notebook

# Stamen Toner
map = folium.Map(location = [13.406,80.110], tiles='Stamen Toner', zoom_start = 9)
map

Make this Notebook Trusted to load map: File -> Trust Notebook

This example uses the Stamen Terrain map layer to visualize the volcano terrain.

# Use terrain map layer to see volcano terrain
map = folium.Map(location = [4,10], tiles = "Stamen Terrain", zoom_start = 3)

Add markers

To represent the different types of volcanoes, you can create Folium markers and add them to your map.

# Create a geometry list from the GeoDataFrame
geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]

# Iterate through list and add a marker for each volcano, color-coded by its type.
i = 0
for coordinates in geo_df_list:
    #assign a color marker for the type of volcano, Strato being the most common
    if geo_df.Type[i] == "Stratovolcano":
        type_color = "green"
    elif geo_df.Type[i] == "Complex volcano":
        type_color = "blue"
    elif geo_df.Type[i] == "Shield volcano":
        type_color = "orange"
    elif geo_df.Type[i] == "Lava dome":
        type_color = "pink"
    else:
        type_color = "purple"


    # Place the markers with the popup labels and data
    map.add_child(folium.Marker(location = coordinates,
                            popup =
                            "Year: " + str(geo_df.Year[i]) + '<br>' +
                            "Name: " + str(geo_df.Name[i]) + '<br>' +
                            "Country: " + str(geo_df.Country[i]) + '<br>'
                            "Type: " + str(geo_df.Type[i]) + '<br>'
                            "Coordinates: " + str(geo_df_list[i]),
                            icon = folium.Icon(color = "%s" % type_color)))
    i = i + 1

map

Make this Notebook Trusted to load map: File -> Trust Notebook

Folium Heatmaps

Folium is well known for its heatmaps, which create a heatmap layer. To plot a heatmap in Folium, you need a list of latitudes and longitudes.

# This example uses heatmaps to visualize the density of volcanoes
# which is more in some parts of the world compared to others.

from folium import plugins

map = folium.Map(location = [15,30], tiles='Cartodb dark_matter', zoom_start = 2)

heat_data = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]

heat_data
plugins.HeatMap(heat_data).add_to(map)

map

Make this Notebook Trusted to load map: File -> Trust Notebook

Adding a background map to plots Plotting with Geoplot and GeoPandas