[英]How to add Cluster markers to Choropleth with Folium
I've been working for a while with Choropleth and Cluster marker maps in Folium (which are great).我在 Folium 中使用 Choropleth 和 Cluster 标记图已经有一段时间了(这很棒)。 My question is whether it is possible to combine them in one map, which is so that I can see how much one variable affects another.我的问题是是否可以将它们组合在一张地图中,这样我就可以看到一个变量对另一个变量的影响程度。 I can get both map types to work individually so no problems there.我可以让两种地图类型单独工作,所以那里没有问题。 This is my attempted code to combine the two so far:到目前为止,这是我尝试将两者结合起来的代码:
import pandas as pd
import folium
from folium.plugins import MarkerCluster
input_filename="input_filename.csv"
df = pd.read_csv(input_filename,encoding='utf8')
geo = 'blah.json'
comparison = 'comparison.csv'
comparison_data = pd.read_csv(comparison)
m = folium.Map(location=[Lat,Lon], zoom_start=12)
folium.Choropleth(
geo_data=geo,
name='choropleth',
data=comparison_data,
columns=['col1','col2'],
key_on='feature.properties.ID',
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=0.5,
legend_name='Blah (%)'
).add_to(m)
folium.LayerControl().add_to(m)
marker_cluster = MarkerCluster().add_to(m)
for row in df.itertuples():
folium.Marker(location=[row.Lat,row.Lon],popup=row.Postcode).add_to(marker_cluster)
m
Ok so I've solved it, really pleased!!好的,我已经解决了,真的很高兴!! The solution was to do the marker cluster first, and then follow-up with the Choropleth:解决方案是先做标记集群,然后用 Choropleth 进行跟进:
import pandas as pd
import folium
from folium.plugins import MarkerCluster
m = folium.Map(location=[Lat,Lon], zoom_start=12)
input_filename="input_filename.csv"
df = pd.read_csv(input_filename,encoding='utf8')
geo = 'blah.json'
comparison = 'comparison.csv'
comparison_data = pd.read_csv(comparison)
folium.LayerControl().add_to(m)
marker_cluster = MarkerCluster().add_to(m)
for row in df.itertuples():
folium.Marker(location=[row.Lat,row.Lon],popup=row.Postcode).add_to(marker_cluster)
folium.Choropleth(
geo_data=geo,
name='choropleth',
data=comparison_data,
columns=['col1','col2'],
key_on='feature.properties.ID',
fill_color='OrRd',
fill_opacity=0.5,
line_opacity=0.5,
legend_name='Blah (%)'
).add_to(m)
m
from random import randint
import folium
def rgb_to_hex(rgb):
return '#%02x%02x%02x' % rgb
mp = folium.Map(location=[40.6, -73.7], scale = 10)
colors = []
while len(colors) != 50:
r = randint(0, 255)
g = randint(0, 255)
b = randint(0, 255)
if rgb_to_hex((r, g, b)) not in colors:
colors.append(rgb_to_hex((r, g, b)))
for j in range(df.shape[0]):
lat = df.iloc[j]['pickup_latitude']
lon = df.iloc[j]['pickup_longitude']
color = colors[int(df.iloc[j]['p_clust'])]
folium.Circle(location=[lat, lon], radius=8, color = color).add_to(mp)
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