choropleth地图未显示

Question

我是Python的数据科学新手，这是我在这里的第一个帮助请求(请提前为一些错误道歉)。需要您的支持，以获得为什么这个(简单的数据帧为基础的) choropleth地图没有显示。读了很多关于争论的讨论，然后我验证了所有主要的东西:地区名称和NAAM (在geojson中)，都在str中，等等-但我仍然卡住了，我看不到地图(只有图例)。让我知道如果需要更多的信息，在下面你可以找到代码：

df_clo=dfrc.groupby(['District']).mean()
df_clo.reset_index(inplace=True)
df_clo=df_clo[['District','Rent']]
df_clo['District'] = df_clo['District'].str.upper()
df_clo

Out9

District    Rent
0   BINNENSTAD  1792.281250
1   NOORDOOST   1763.558824
2   OOST    1739.186047
3   ZUID    1562.142857
4   ZUIDWEST    1397.689655

10年内

latitude = 52.09083
longitude = 5.12222
print('The geograpical coordinate of Utrecht are {}, {}.'.format(latitude, longitude))# create map of Utrecht using latitude and longitude values
utrecht_geo = r'https://raw.githubusercontent.com/umbesallfi/Coursera_Capstone/master/wijk_.geojson'
# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration
threshold_scale = np.linspace(df_clo['Rent'].min(),
                              df_clo['Rent'].max(),
                              6, dtype=int)
threshold_scale = threshold_scale.tolist() # change the numpy array to a list
threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration
# let Folium determine the scale.
map_utr = folium.Map(location=[latitude, longitude], zoom_start=2, tiles='Mapbox Bright')
map_utr.choropleth(
    geo_data=utrecht_geo,
    data=df_clo,
    columns=['District', 'Rent'],
    key_on='feature.properties.NAAM',
    threshold_scale=threshold_scale,
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Price in Utrecht by Wijk',
    reset=True
)
map_utr

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Answer 1

地区名称不会以大写字母存储在wijk_.geojson文件中。因此，删除此行应该就足够了：

df_clo['District'] = df_clo['District'].str.upper()

我的代码：

import folium
import pandas as pd
import numpy as np

m = folium.Map(location=[52.09083, 5.12222],
               zoom_start=12,
               control_scale=True)

df_clo = pd.DataFrame({'District':['Binnenstad','Noordoost','Oost','Zuid','Zuidwest'],
                       'Rent':[1792.281250,
                               1763.558824,
                               1739.186047,
                               1562.142857,
                               1397.689655]})

threshold_scale = np.linspace(df_clo['Rent'].min(),
                              df_clo['Rent'].max(),
                              6, dtype=int)
threshold_scale = threshold_scale.tolist() # change the numpy array to a list
threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration

utrecht_geo = 'wijk_.geojson'

folium.Choropleth(geo_data=utrecht_geo,
                  name='choropleth',
                  data=df_clo,
                  columns=['District', 'Rent'],
                  key_on='feature.properties.NAAM',
                  threshold_scale=threshold_scale,
                  fill_color='YlOrRd',
                  fill_opacity=0.7,
                  line_opacity=0.2,
                  legend_name='Price in Utrecht by Wijk',).add_to(m)

folium.LayerControl().add_to(m)

m

返回此映射：

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