Python / Folium / Choropleth: TypeError: ufunc 'isnan' not supported
Python / Folium / Choropleth: TypeError: ufunc 'isnan' not supported
我正在尝试在洛杉矶的邮政编码上绘制等值线图，以显示/突出显示数据框特定列的值。到目前为止，我的代码收到以下错误消息：
'TypeError: ufunc 'isnan' 不支持输入类型，并且根据转换规则''safe'' 无法将输入安全地强制转换为任何支持的类型
'
经过几天的研究 google、堆栈、研究纪录片、查看 youtube 教程，我仍然无法修复它。到这里是不得已的办法。
请查看下面的代码，以及回溯：
!pip install geopandas
!pip install geopy
!pip install folium
import pandas as pd
import folium
import
from functools import reduce
from io import BytesIO
import requests
import os
import geopandas as gpd
LA_map = folium.Map(location= [34.052235, -118.243683], zoom_start= 10)
df_geojson = gpd.read_file(
r'https://raw.githubusercontent.com/tzick90/datasources/main/map.geojson'
)
LA_zipcodes = df_geojson['zipcode'].tolist()
CA_househould_income = '1Gfa2sG0SzDdgV9bztVZvZh8U9ti0ei_BpZr3swGY3mg'
CA_househould_income_file = f'https://docs.google.com/spreadsheets/d/{CA_househould_income}/export?format=csv'
r2 = requests.get(CA_househould_income_file)
CA_HI = pd.read_csv(BytesIO(r2.content))
LA_avg_income = CA_HI['zip_code'].isin(LA_zipcodes)
LA_avg_income_clean = CA_HI[LA_avg_income].reset_index()
LA_avg_income_clean.rename(columns = {'zip_code':'zipcode'}, inplace= True)
LA_avg_income_clean['zipcode'] = LA_avg_income_clean['zipcode'].astype('str')
LA_avg_income_clean_list = LA_avg_income_clean['zipcode'].tolist()
LA_zipcode_clean = df_geojson['zipcode'].isin(LA_avg_income_clean_list)
LA_zipcode_clean_final = df_geojson[LA_zipcode_clean].reset_index()
LA_zipcode_clean_final['zipcode_'] = LA_zipcode_clean_final['zipcode']
LA_avg_income_clean['zipcode_'] = LA_avg_income_clean['zipcode']
LA_avg_income_clean['zipcode'] = LA_avg_income_clean['zipcode'].astype('str')
LA_zipcode_clean_final1 = LA_zipcode_clean_final.sort_values(by= 'zipcode', ascending = True).set_index('zipcode_')
LA_avg_income_clean1 = LA_avg_income_clean.sort_values(by= 'zipcode', ascending = True).set_index('zipcode_')
zip_boundries1 = LA_zipcode_clean_final1.to_json()
folium.Choropleth(
geo_data= zip_boundries1,
name= 'choropleth',
data= LA_avg_income_clean1,
columns= ['zipcode','Avg. Income/H/hold'],
key_on= 'feature.properties.zipcode',
fill_color= 'YlGn',
#nan_fill_opacity= 0.1,
fill_opacity=0.3,
line_opacity=0.9,
legend_name= "Average Income per Household in USD",
).add_to(LA_map)
display(LA_map)
以下是我几乎经常收到的错误消息：
TypeError Traceback (most recent call last)
<ipython-input-185-62a5660e5e7c> in <module>
----> 1 folium.Choropleth(
2 geo_data= zip_boundries1,
3 name= 'choropleth',
4 data= LA_avg_income_clean1,
5 columns= ['zipcode','Avg. Income/H/hold'],
~/opt/anaconda3/lib/python3.8/site-packages/folium/features.py in __init__(self, geo_data, data, columns, key_on, bins, fill_color, nan_fill_color, fill_opacity, nan_fill_opacity, line_color, line_weight, line_opacity, name, legend_name, overlay, control, show, topojson, smooth_factor, highlight, **kwargs)
1211 if color_data is not None and key_on is not None:
1212 real_values = np.array(list(color_data.values()))
-> 1213 real_values = real_values[~np.isnan(real_values)]
1214 _, bin_edges = np.histogram(real_values, bins=bins)
1215
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
关于如何修复/解决错误的任何建议？
非常感谢每一个建议或解决方案。
此致
我们已经简化了您的作业并创建了代码。挑战在于邮政编码必须是每个数据中的字符串，否则 folium 将不支持它。另外，邮政编码区表示的值是美元表示法的字符串，因此需要将它们转换为数字。
import pandas as pd
import folium
from io import BytesIO
import requests
import geopandas as gpd
from re import sub
from decimal import Decimal
df_geojson = gpd.read_file(r'https://raw.githubusercontent.com/tzick90/datasources/main/map.geojson')
LA_zipcodes = df_geojson['zipcode'].tolist()
df_geojson['zipcode'] = df_geojson['zipcode'].astype(str)
CA_househould_income = '1Gfa2sG0SzDdgV9bztVZvZh8U9ti0ei_BpZr3swGY3mg'
CA_househould_income_file = f'https://docs.google.com/spreadsheets/d/{CA_househould_income}/export?format=csv'
r2 = requests.get(CA_househould_income_file)
CA_HI = pd.read_csv(BytesIO(r2.content))
CA_HI.rename(columns = {'zip_code':'zipcode'}, inplace= True)
CA_HI['zipcode'] = CA_HI['zipcode'].astype(str)
CA_HI['Avg. Income/H/hold'] = CA_HI['Avg. Income/H/hold'].apply(lambda x: Decimal(sub(r'[^\d.]', '', x)))
CA_HI['Avg. Income/H/hold'] = CA_HI['Avg. Income/H/hold'].astype(int)
LA_map = folium.Map(location= [34.052235, -118.243683], zoom_start= 10)
folium.Choropleth(
geo_data= df_geojson.to_json(),
name= 'choropleth',
data= CA_HI,
columns= ['zipcode','Avg. Income/H/hold'],
key_on= 'feature.properties.zipcode',
fill_color= 'YlGn',
#nan_fill_opacity= 0.1,
fill_opacity=0.3,
line_opacity=0.9,
legend_name= "Average Income per Household in USD",
).add_to(LA_map)
display(LA_map)
我正在尝试在洛杉矶的邮政编码上绘制等值线图，以显示/突出显示数据框特定列的值。到目前为止，我的代码收到以下错误消息：
'TypeError: ufunc 'isnan' 不支持输入类型，并且根据转换规则''safe'' 无法将输入安全地强制转换为任何支持的类型 '
经过几天的研究 google、堆栈、研究纪录片、查看 youtube 教程，我仍然无法修复它。到这里是不得已的办法。
请查看下面的代码，以及回溯：
!pip install geopandas
!pip install geopy
!pip install folium
import pandas as pd
import folium
import
from functools import reduce
from io import BytesIO
import requests
import os
import geopandas as gpd
LA_map = folium.Map(location= [34.052235, -118.243683], zoom_start= 10)
df_geojson = gpd.read_file(
r'https://raw.githubusercontent.com/tzick90/datasources/main/map.geojson'
)
LA_zipcodes = df_geojson['zipcode'].tolist()
CA_househould_income = '1Gfa2sG0SzDdgV9bztVZvZh8U9ti0ei_BpZr3swGY3mg'
CA_househould_income_file = f'https://docs.google.com/spreadsheets/d/{CA_househould_income}/export?format=csv'
r2 = requests.get(CA_househould_income_file)
CA_HI = pd.read_csv(BytesIO(r2.content))
LA_avg_income = CA_HI['zip_code'].isin(LA_zipcodes)
LA_avg_income_clean = CA_HI[LA_avg_income].reset_index()
LA_avg_income_clean.rename(columns = {'zip_code':'zipcode'}, inplace= True)
LA_avg_income_clean['zipcode'] = LA_avg_income_clean['zipcode'].astype('str')
LA_avg_income_clean_list = LA_avg_income_clean['zipcode'].tolist()
LA_zipcode_clean = df_geojson['zipcode'].isin(LA_avg_income_clean_list)
LA_zipcode_clean_final = df_geojson[LA_zipcode_clean].reset_index()
LA_zipcode_clean_final['zipcode_'] = LA_zipcode_clean_final['zipcode']
LA_avg_income_clean['zipcode_'] = LA_avg_income_clean['zipcode']
LA_avg_income_clean['zipcode'] = LA_avg_income_clean['zipcode'].astype('str')
LA_zipcode_clean_final1 = LA_zipcode_clean_final.sort_values(by= 'zipcode', ascending = True).set_index('zipcode_')
LA_avg_income_clean1 = LA_avg_income_clean.sort_values(by= 'zipcode', ascending = True).set_index('zipcode_')
zip_boundries1 = LA_zipcode_clean_final1.to_json()
folium.Choropleth(
geo_data= zip_boundries1,
name= 'choropleth',
data= LA_avg_income_clean1,
columns= ['zipcode','Avg. Income/H/hold'],
key_on= 'feature.properties.zipcode',
fill_color= 'YlGn',
#nan_fill_opacity= 0.1,
fill_opacity=0.3,
line_opacity=0.9,
legend_name= "Average Income per Household in USD",
).add_to(LA_map)
display(LA_map)
以下是我几乎经常收到的错误消息：
TypeError Traceback (most recent call last)
<ipython-input-185-62a5660e5e7c> in <module>
----> 1 folium.Choropleth(
2 geo_data= zip_boundries1,
3 name= 'choropleth',
4 data= LA_avg_income_clean1,
5 columns= ['zipcode','Avg. Income/H/hold'],
~/opt/anaconda3/lib/python3.8/site-packages/folium/features.py in __init__(self, geo_data, data, columns, key_on, bins, fill_color, nan_fill_color, fill_opacity, nan_fill_opacity, line_color, line_weight, line_opacity, name, legend_name, overlay, control, show, topojson, smooth_factor, highlight, **kwargs)
1211 if color_data is not None and key_on is not None:
1212 real_values = np.array(list(color_data.values()))
-> 1213 real_values = real_values[~np.isnan(real_values)]
1214 _, bin_edges = np.histogram(real_values, bins=bins)
1215
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
关于如何修复/解决错误的任何建议？ 非常感谢每一个建议或解决方案。
此致
我们已经简化了您的作业并创建了代码。挑战在于邮政编码必须是每个数据中的字符串，否则 folium 将不支持它。另外，邮政编码区表示的值是美元表示法的字符串，因此需要将它们转换为数字。
import pandas as pd
import folium
from io import BytesIO
import requests
import geopandas as gpd
from re import sub
from decimal import Decimal
df_geojson = gpd.read_file(r'https://raw.githubusercontent.com/tzick90/datasources/main/map.geojson')
LA_zipcodes = df_geojson['zipcode'].tolist()
df_geojson['zipcode'] = df_geojson['zipcode'].astype(str)
CA_househould_income = '1Gfa2sG0SzDdgV9bztVZvZh8U9ti0ei_BpZr3swGY3mg'
CA_househould_income_file = f'https://docs.google.com/spreadsheets/d/{CA_househould_income}/export?format=csv'
r2 = requests.get(CA_househould_income_file)
CA_HI = pd.read_csv(BytesIO(r2.content))
CA_HI.rename(columns = {'zip_code':'zipcode'}, inplace= True)
CA_HI['zipcode'] = CA_HI['zipcode'].astype(str)
CA_HI['Avg. Income/H/hold'] = CA_HI['Avg. Income/H/hold'].apply(lambda x: Decimal(sub(r'[^\d.]', '', x)))
CA_HI['Avg. Income/H/hold'] = CA_HI['Avg. Income/H/hold'].astype(int)
LA_map = folium.Map(location= [34.052235, -118.243683], zoom_start= 10)
folium.Choropleth(
geo_data= df_geojson.to_json(),
name= 'choropleth',
data= CA_HI,
columns= ['zipcode','Avg. Income/H/hold'],
key_on= 'feature.properties.zipcode',
fill_color= 'YlGn',
#nan_fill_opacity= 0.1,
fill_opacity=0.3,
line_opacity=0.9,
legend_name= "Average Income per Household in USD",
).add_to(LA_map)
display(LA_map)