Note
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:
[1]:
# Importing Libraries import pandas as pd import geopandas import folium import matplotlib.pyplot as plt from shapely.geometry import Point
[2]:
df1 = pd.read_csv('volcano_data_2010.csv') 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
[3]:
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()
[4]:
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)
[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')
Text(0.5, 1.0, 'Volcanoes')
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.
[6]:
# Stamen Terrain map = folium.Map(location = [13.406,80.110], tiles = "Stamen Terrain", zoom_start = 9) map
[7]:
# OpenStreetMap map = folium.Map(location = [13.406,80.110], tiles='OpenStreetMap' , zoom_start = 9) map
[8]:
# Stamen Toner map = folium.Map(location = [13.406,80.110], tiles='Stamen Toner', zoom_start = 9) map
We can use other tiles for the visualization, these are just a few examples.
Now, let’s look at different volcanoes on the map using different Markers to represent the volcanoes.
[9]:
#use terrain map layer to actually see volcano terrain map = folium.Map(location = [4,10], tiles = "Stamen Terrain", zoom_start = 3)
[10]:
# 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
[11]:
map
Folium is well known for it’s heatmap which create a heatmap layer. To plot a heat map in folium, one needs a list of Latitude, Longitude.
[12]:
# 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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