如何将两个坐标列转换为一列 Shapely 点

Question

我正在尝试对整个列执行操作，但出现类型错误，我想创建一个包含Shapely Point 的列：

crime_df = crime_df[crime_df['Latitude'].notna()]
crime_df = crime_df[crime_df['Longitude'].notna()]

crime_df['Longitude'] = crime_df['Longitude'].astype(float)
crime_df['Latitude'] = crime_df['Latitude'].astype(float)

print (crime_df['Longitude'])
print (crime_df['Latitude'])

crime_df['point'] = Point(crime_df['Longitude'], crime_df['Latitude'])

输出：

18626    -87.647379
Name: Longitude, Length: 222, dtype: float64

18626    41.781100
Name: Latitude, Length: 222, dtype: float64

TypeError: cannot convert the series to <class 'float'>

Answer 1

我认为你需要分别处理每个点，所以需要DataFrame.apply使用 lambda 函数：

crime_df['point'] = crime_df.apply(lambda x: Point(x['Longitude'], x['Latitude'], axis=1)

或者谢谢@N。沃达：

crime_df["point"] = crime_df[["Longitude", "Latitude"]].apply(Point, axis=1)

或者列表理解替代方案是：

crime_df['point'] = [Point(lon, lat) 
                                 for lon, lat in crime_df[['Longitude','Latitude']].values]

编辑：我认为对于矢量化方式可以使用geopandas.points_from_xy如下：

gdf = geopandas.GeoDataFrame(df,geometry=geopandas.points_from_xy(df.Longitude,df.Latitude))