What is Folium?
It builds on the data wrangling and a Python wrapper for leaflet.js. It makes it easy to visualize data in Python with minimal instructions.
Folium expands on the data wrangling properties utilized in Python language and the mapping characteristics of the Leaflet.js library. Folium enables us to make an intuitive map and are is visualized in a Leaflet map after manipulating data in Python. Folium results are intuitive which makes this library helpful for dashboard building and easier to work with.
Let's see the implementation of both GeoPandas and Folium:
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# Importing Libraries
import pandas as pd
import geopandas
import folium
import matplotlib.pyplot as plt
from shapely.geometry import Point
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df1 = pd.read_csv('volcano_data_2010.csv')
df = df1.loc[:, ("Year", "Name", "Country", "Latitude", "Longitude", "Type")]
df.info()
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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()
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In [4]:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
df.Type.unique()
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In [5]:
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')
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We will be using different icons to differentiate the types of Volcanoes using Folium. But before we start, we can see a few different tiles to choose from folium.
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# Stamen Terrain
map = folium.Map(location = [13.406,80.110], tiles = "Stamen Terrain", zoom_start = 9)
map
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In [7]:
# OpenStreetMap
map = folium.Map(location = [13.406,80.110], tiles='OpenStreetMap' , zoom_start = 9)
map
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In [8]:
# Stamen Toner
map = folium.Map(location = [13.406,80.110], tiles='Stamen Toner', zoom_start = 9)
map
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In [9]:
#use terrain map layer to actually see volcano terrain
map = folium.Map(location = [4,10], tiles = "Stamen Terrain", zoom_start = 3)
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# insert multiple markers, iterate through list
# add a different color marker associated with type of volcano
geo_df_list = [[point.xy[1][0], point.xy[0][0]] for point in geo_df.geometry ]
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"
#now 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
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map
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# In this example, with the hep of heat maps, we are able to perceive the density of volcanoes
# which is more in some part 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
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