Quickstart¶
Getting Started¶
To create a base map, simply pass your starting coordinates to Folium:
[1]:
import folium
m = folium.Map(location=[45.5236, -122.6750])
To display it in a Jupyter notebook, simply ask for the object representation:
[2]:
m
[2]:
to save it in a file,
[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.
[4]:
folium.Map(location=[45.5236, -122.6750], zoom_start=13)
[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.
[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
[5]:
There is built in support for colors and marker icon types from bootstrap.
[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
[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.
[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
[7]:
a convenience function to enable lat/lon popovers. This can help users to find a location by interactively browsing the map.
[8]:
m = folium.Map(location=[46.1991, -122.1889], tiles="Stamen Terrain", zoom_start=13)
m.add_child(folium.LatLngPopup())
m
[8]:
and click-for-marker functionality for on-the-fly placement of markers:
[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
[9]:
Polylines¶
folium can show linear elements on a map using PolyLine. This object can help put emphasis on a trail, a road, or a coastline.
[10]:
m = folium.Map(location=[-71.38, -73.9], zoom_start=11)
trail_coordinates = [
(-71.351871840295871, -73.655963711222626),
(-71.374144382613707, -73.719861619751498),
(-71.391042575973145, -73.784922248007007),
(-71.400964450973134, -73.851042243124397),
(-71.402411391077322, -74.050048183880477),
]
folium.PolyLine(trail_coordinates, tooltip="Coast").add_to(m)
m
[10]:
Vincent/Vega and Altair/VegaLite Markers¶
folium enables passing any HTML object as a popup, including `bokeh <https://docs.bokeh.org/en/latest/>`__ plots, but there is a built-in support for vincent and altair visualizations to any marker type, with the visualization as the popover.
[11]:
import json
import requests
url = (
"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
)
vis1 = json.loads(requests.get(f"{url}/vis1.json").text)
vis2 = json.loads(requests.get(f"{url}/vis2.json").text)
vis3 = json.loads(requests.get(f"{url}/vis3.json").text)
[12]:
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(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(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(vis3, width=450, height=250)
),
).add_to(m)
m
[12]:
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:
[13]:
url = (
"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
)
antarctic_ice_edge = f"{url}/antarctic_ice_edge.json"
antarctic_ice_shelf_topo = f"{url}/antarctic_ice_shelf_topo.json"
m = folium.Map(
location=[-59.1759, -11.6016],
tiles="cartodbpositron",
zoom_start=2,
)
folium.GeoJson(antarctic_ice_edge, name="geojson").add_to(m)
folium.TopoJson(
json.loads(requests.get(antarctic_ice_shelf_topo).text),
"objects.antarctic_ice_shelf",
name="topojson",
).add_to(m)
folium.LayerControl().add_to(m)
m
[13]:
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.
[14]:
import pandas as pd
url = (
"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
)
state_geo = f"{url}/us-states.json"
state_unemployment = f"{url}/US_Unemployment_Oct2012.csv"
state_data = pd.read_csv(state_unemployment)
m = folium.Map(location=[48, -102], zoom_start=3)
folium.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 (%)",
).add_to(m)
folium.LayerControl().add_to(m)
m
[14]:
The legend on the upper right is automatically generated for your values using 6 same sized bins. Passing your own bins (number or list) is simple:
[15]:
bins = list(state_data["Unemployment"].quantile([0, 0.25, 0.5, 0.75, 1]))
m = folium.Map(location=[48, -102], zoom_start=3)
folium.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 (%)",
bins=bins,
reset=True,
).add_to(m)
m
[15]:
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.
[16]:
import branca
url = (
"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data"
)
county_data = f"{url}/us_county_data.csv"
county_geo = f"{url}/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(
json.loads(requests.get(county_geo).text),
"objects.us_counties_20m",
style_function=style_function,
).add_to(m)
m
[16]:
[17]:
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(
json.loads(requests.get(county_geo).text),
"objects.us_counties_20m",
style_function=style_function,
).add_to(m)
m
[17]:
[18]:
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(
json.loads(requests.get(county_geo).text),
"objects.us_counties_20m",
style_function=style_function,
).add_to(m)
m
[18]:
For more examples and use cases please take a look at the gallery:
https://nbviewer.org/github/python-visualization/folium_contrib/tree/main/notebooks/