ValueError：使用 geopandas 数据框时无法渲染缺少任何几何图形的对象

Question

我正在尝试使用 geopandas 数据框在大叶地图上添加一个 choropleth 图层，该数据框是通过将包含巴西所有区域（名为“map_1”）的 shapefile 与常规熊猫数据框（名为“amazon_state”）合并而构建的。 2个数据帧的合并给了我“map_2”，在清理（删除一些行）之后我称之为“map_3”。

'''

#importing shapefile map_1, which contains all regions in Brazil

map_1= gpd.read_file("/Users/alexandertankou/Desktop/python/bra_adm1/BRA_adm1.shp")

# creating amazon_state

amazon_state= amazon_data.groupby("state", as_index=False).sum().drop(columns=["year"])

# ensure the naming of regions e in map_1 and amazon_state is the same

map_1.NAME_1= amazon_state.state

#map_2: merging map_1 with amazon_state
map_2= pd.merge (left=map_1, right= amazon_state, left_on="NAME_1", right_on="state", how= "left")

#dropping none useful columns
map_2= map_2.drop(columns=["NAME_0",'HASC_1',"ID_1","ISO","CCN_1","CCA_1","ID_0","TYPE_1","ENGTYPE_1", "NL_NAME_1","VARNAME_1"])

#ploting map_2
map_2.plot(column="number", cmap="YlOrRd",legend=True, figsize= (13,10))

#setting the folium map

m=folium.Map(location= [-22.919882,-43.604392], zoom_start=10)

#making geomery type hashable in python
map_2['geometry'] = map_2['geometry'].apply(lambda x: str(x))

#cleaning up map_2 data
map_3= map_2.iloc[0:23,:]

#add the choropleth layer on folium map

m.choropleth(geo_data= map_3,name="geometry", data= map_3,key_on="feature.properties.NAME_1",columns=["geometry","number"],fill_color='YlGn')
folium.LayerControl().add_to(m)

'''

但我不断收到ValueError: Cannot render objects with any missing geometry 。 使用 isnan 和 is_empty 方法后，我确定 map_3 中没有缺失值（请参阅下面的数据），所以不确定我做错了什么：

              NAME_1                                           geometry  \
0               Acre  POLYGON ((-73.33251190185541 -7.32487916946411...   
1            Alagoas  MULTIPOLYGON (((-35.90152740478516 -9.86180496...   
2              Amapa  MULTIPOLYGON (((-50.02402877807612 0.859862029...   
3           Amazonas  POLYGON ((-67.32623291015625 2.029680967331046...   
4              Bahia  MULTIPOLYGON (((-38.69708251953125 -17.9790287...   
5              Ceara  MULTIPOLYGON (((-38.47541809082026 -3.70097303...   
6   Distrito Federal  POLYGON ((-48.03603363037109 -15.5002202987670...   
7     Espirito Santo  MULTIPOLYGON (((-40.88402938842768 -21.1612491...   
8              Goias  POLYGON ((-50.15817260742188 -12.4123792648315...   
9           Maranhao  MULTIPOLYGON (((-42.12374877929688 -2.80069398...   
10       Mato Grosso  POLYGON ((-56.1036376953125 -17.17354011535639...   
11      Minas Gerais  POLYGON ((-57.6052360534668 -8.662846565246525...   
12           Paraiba  POLYGON ((-44.20977783203119 -14.2366542816162...   
13              Par·  MULTIPOLYGON (((-46.43458175659174 -1.01708304...   
14        Pernambuco  MULTIPOLYGON (((-42.87873840332026 -9.29837322...   
15              Piau  MULTIPOLYGON (((-48.63069534301758 -25.8679161...   
16               Rio  MULTIPOLYGON (((-35.13597106933594 -8.83791732...   
17          Rondonia  POLYGON ((-41.81680679321283 -2.74375009536743...   
18           Roraima  MULTIPOLYGON (((-44.67124938964838 -23.3545837...   
19    Santa Catarina  MULTIPOLYGON (((-35.10902786254883 -6.19347190...   
20         Sao Paulo  MULTIPOLYGON (((-52.07069396972656 -32.0284729...   
21           Sergipe  POLYGON ((-63.53470230102539 -7.97433900833129...   
22         Tocantins  POLYGON ((-60.16886138916004 5.226301193237362...   

               state     number  
0               Acre  18464.030  
1            Alagoas   4644.000  
2              Amapa  21831.576  
3           Amazonas  30650.129  
4              Bahia  44746.226  
5              Ceara  30428.063  
6   Distrito Federal   3561.000  
7     Espirito Santo   6546.000  
8              Goias  37695.520  
9           Maranhao  25129.131  
10       Mato Grosso  96246.028  
11      Minas Gerais  37475.258  
12           Paraiba  52435.918  
13              Par·  24512.144  
14        Pernambuco  24498.000  
15              Piau  37803.747  
16               Rio  45160.865  
17          Rondonia  20285.429  
18           Roraima  24385.074  
19    Santa Catarina  24359.852  
20         Sao Paulo  51121.198  
21           Sergipe   3237.000  
22         Tocantins  33707.885

Answer 1

我有同样的问题。 map_3 是地理数据框？ 如果没有，您必须先转换：

gdf = gpd.GeoDataFrame(map_3 , geometry = map_3.geometry)
gdf.crs = {'init' :'epsg:4326'}

Answer 2

@Thomas 是对的：您的问题是您的map_2和map_3变量对应于<class 'pandas.core.frame.DataFrame'> ，但您需要<class 'geopandas.geodataframe.GeoDataFrame'> 。

原因在这里：

map_2 = pd.merge(left=map_1, right=amazon_state, left_on="NAME_1",
                 right_on="state", how= "left")

当您调用pandas.merge()方法并且您的参数是GeoDataFrame和DataFrame ，结果将始终是DataFrame 。

如果您想在合并后获取GeoDataFrame ，请改用pandas.DataFrame.merge()方法。 确保从GeoDataFrame调用它， not DataFrame ：

map_2 = map_1.merge(right=amazon_state, left_on="NAME_1",
                    right_on="state", how="left")

您还可以查看 geopandas 的相关文档。