Quickstart

Getting Started

To create a base map, simply pass your starting coordinates to Folium:



In [1]:

    
import folium


m = folium.Map(location=[45.5236, -122.6750])

To display it in a Jupyter notebook, simply ask for the object representation:



In [2]:

    
m









    Out[2]:

to save it in a file,



In [3]:

    
m.save('index.html')

The default tiles are set to OpenStreetMap, but Stamen Terrain, Stamen Toner, Mapbox Bright, and Mapbox Control Room, and many others tiles are built in.



In [4]:

    
folium.Map(
    location=[45.5236, -122.6750],
    tiles='Stamen Toner',
    zoom_start=13
)









    Out[4]:

One can use Cloudmade or Mapbox custom tilesets--simply pass your key to the API_key keyword:

folium.Map(location=[45.5236, -122.6750],
           tiles='Mapbox',
           API_key='your.API.key')

Lastly, Folium supports passing any leaflet.js compatible custom tileset:

folium.Map(location=[45.372, -121.6972],
           zoom_start=12,
           tiles='http://{s}.tiles.yourtiles.com/{z}/{x}/{y}.png',
           attr='My Data Attribution')

Markers

There are numerous marker types, starting with a simple Leaflet style location marker with a popup and tooltip HTML.



In [5]:

    
m = folium.Map(
    location=[45.372, -121.6972],
    zoom_start=12,
    tiles='Stamen Terrain'
)

tooltip = 'Click me!'

folium.Marker([45.3288, -121.6625], popup='<i>Mt. Hood Meadows</i>', tooltip=tooltip).add_to(m)
folium.Marker([45.3311, -121.7113], popup='<b>Timberline Lodge</b>', tooltip=tooltip).add_to(m)

m









    Out[5]:

There is built in support for colors and marker icon types from bootstrap.



In [6]:

    
m = folium.Map(
    location=[45.372, -121.6972],
    zoom_start=12,
    tiles='Stamen Terrain'
)

folium.Marker(
    location=[45.3288, -121.6625],
    popup='Mt. Hood Meadows',
    icon=folium.Icon(icon='cloud')
).add_to(m)

folium.Marker(
    location=[45.3311, -121.7113],
    popup='Timberline Lodge',
    icon=folium.Icon(color='green')
).add_to(m)

folium.Marker(
    location=[45.3300, -121.6823],
    popup='Some Other Location',
    icon=folium.Icon(color='red', icon='info-sign')
).add_to(m)


m









    Out[6]:

Leaflet's Circle and CircleMarker, implemented to reflect radii in units of meters and pixels respectively, are available as features. See the features.py for more options.



In [7]:

    
m = folium.Map(
    location=[45.5236, -122.6750],
    tiles='Stamen Toner',
    zoom_start=13
)

folium.Circle(
    radius=100,
    location=[45.5244, -122.6699],
    popup='The Waterfront',
    color='crimson',
    fill=False,
).add_to(m)

folium.CircleMarker(
    location=[45.5215, -122.6261],
    radius=50,
    popup='Laurelhurst Park',
    color='#3186cc',
    fill=True,
    fill_color='#3186cc'
).add_to(m)


m









    Out[7]:

a convenience function to enable lat/lng popovers:



In [8]:

    
m = folium.Map(
    location=[46.1991, -122.1889],
    tiles='Stamen Terrain',
    zoom_start=13
)

m.add_child(folium.LatLngPopup())


m









    Out[8]:

and click-for-marker functionality for on-the-fly placement of markers:



In [9]:

    
m = folium.Map(
    location=[46.8527, -121.7649],
    tiles='Stamen Terrain',
    zoom_start=13
)

folium.Marker(
    [46.8354, -121.7325],
    popup='Camp Muir'
).add_to(m)

m.add_child(folium.ClickForMarker(popup='Waypoint'))


m









    Out[9]:

Vincent/Vega and Altair/VegaLite Markers

folium enables passing any HTML object as a popup, including bokeh plots, but there is a built-in support for vincent and altair visualizations to any marker type, with the visualization as the popover.



In [10]:

    
import os

vis1 = os.path.join('data', 'vis1.json')
vis2 = os.path.join('data', 'vis2.json')
vis3 = os.path.join('data', 'vis3.json')



In [11]:

    
import json


m = folium.Map(
    location=[46.3014, -123.7390],
    zoom_start=7,
    tiles='Stamen Terrain'
)

folium.Marker(
    location=[47.3489, -124.708],
    popup=folium.Popup(max_width=450).add_child(
        folium.Vega(json.load(open(vis1)), width=450, height=250))
).add_to(m)

folium.Marker(
    location=[44.639, -124.5339],
    popup=folium.Popup(max_width=450).add_child(
        folium.Vega(json.load(open(vis2)), width=450, height=250))
).add_to(m)

folium.Marker(
    location=[46.216, -124.1280],
    popup=folium.Popup(max_width=450).add_child(
        folium.Vega(json.load(open(vis3)), width=450, height=250))
).add_to(m)


m









    Out[11]:

For more information about popups, please visit Popups.ipynb

GeoJSON/TopoJSON Overlays

Both GeoJSON and TopoJSON layers can be passed to the map as an overlay, and multiple layers can be visualized on the same map:



In [12]:

    
antarctic_ice_edge = os.path.join('data', 'antarctic_ice_edge.json')
antarctic_ice_shelf_topo = os.path.join('data', 'antarctic_ice_shelf_topo.json')

m = folium.Map(
    location=[-59.1759, -11.6016],
    tiles='Mapbox Bright',
    zoom_start=2
)

folium.GeoJson(
    antarctic_ice_edge,
    name='geojson'
).add_to(m)

folium.TopoJson(
    open(antarctic_ice_shelf_topo),
    'objects.antarctic_ice_shelf',
    name='topojson'
).add_to(m)

folium.LayerControl().add_to(m)


m









    Out[12]:

Choropleth maps

Choropleth can be easily created by binding the data between Pandas DataFrames/Series and Geo/TopoJSON geometries. Color Brewer sequential color schemes are built-in to the library, and can be passed to quickly visualize different combinations.



In [13]:

    
import pandas as pd

state_geo = os.path.join('data', 'us-states.json')

state_unemployment = os.path.join('data', 'US_Unemployment_Oct2012.csv')
state_data = pd.read_csv(state_unemployment)

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

m.choropleth(
    geo_data=state_geo,
    name='choropleth',
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='YlGn',
    fill_opacity=0.7,
    line_opacity=0.2,
    legend_name='Unemployment Rate (%)'
)


folium.LayerControl().add_to(m)

m









    Out[13]:

The legend on the upper right based on a D3 threshold scale, and makes the best-guess at values via quantiles. Passing your own threshold values is simple:



In [14]:

    
from branca.utilities import split_six

threshold_scale = split_six(state_data['Unemployment'])

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

m.choropleth(
    geo_data=state_geo,
    data=state_data,
    columns=['State', 'Unemployment'],
    key_on='feature.id',
    fill_color='BuPu',
    fill_opacity=0.7,
    line_opacity=0.5,
    legend_name='Unemployment Rate (%)',
    threshold_scale=threshold_scale,
    reset=True
)


m









    Out[14]:

By binding data via the Pandas DataFrame, different datasets can be quickly visualized.

Styling function

GeoJson and TopoJson features accepts style_function to allow for further custimization of the map. Take a look at the use examples below.



In [15]:

    
import branca

county_data = os.path.join('data', 'us_county_data.csv')
county_geo = os.path.join('data', 'us_counties_20m_topo.json')

df = pd.read_csv(county_data, na_values=[' '])


colorscale = branca.colormap.linear.YlOrRd_09.scale(0, 50e3)
employed_series = df.set_index('FIPS_Code')['Employed_2011']


def style_function(feature):
    employed = employed_series.get(int(feature['id'][-5:]), None)
    return {
        'fillOpacity': 0.5,
        'weight': 0,
        'fillColor': '#black' if employed is None else colorscale(employed)
    }


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

folium.TopoJson(
    open(county_geo),
    'objects.us_counties_20m',
    style_function=style_function
).add_to(m)


m









    Out[15]:



In [16]:

    
colorscale = branca.colormap.linear.YlGnBu_09.scale(0, 30)

employed_series = df.set_index('FIPS_Code')['Unemployment_rate_2011']


def style_function(feature):
    employed = employed_series.get(int(feature['id'][-5:]), None)
    return {
        'fillOpacity': 0.5,
        'weight': 0,
        'fillColor': '#black' if employed is None else colorscale(employed)
    }


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

folium.TopoJson(
    open(county_geo),
    'objects.us_counties_20m',
    style_function=style_function
).add_to(m)


m









    Out[16]:



In [17]:

    
colorscale = branca.colormap.linear.PuRd_09.scale(0, 100000)

employed_series = df.set_index('FIPS_Code')['Median_Household_Income_2011'].dropna()


def style_function(feature):
    employed = employed_series.get(int(feature['id'][-5:]), None)
    return {
        'fillOpacity': 0.5,
        'weight': 0,
        'fillColor': '#black' if employed is None else colorscale(employed)
    }


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


folium.TopoJson(
    open(county_geo),
    'objects.us_counties_20m',
    style_function=style_function
).add_to(m)


m









    Out[17]:

For more examples and use cases please take a look at the gallery:

https://nbviewer.jupyter.org/github/python-visualization/folium_contrib/tree/master/notebooks/