distance - 非成对距离测量，同时保留原始 geopandas 数据帧中的所有列

Question

这里提供了一种在两个 geopandas 数据帧 (gdf) 之间进行非成对距离计算的解决方案。但是，结果距离矩阵仅保留两个 gdf ​​的索引，可能不可读。我在 gdf ​​中添加了一些列，如下所示，然后得到距离矩阵：

import pandas as pd
import geopandas as gpd

gdf_1 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0, 0], [0, 90, 120]))
gdf_2 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0], [0, -90]))

home = ['home_1', 'home_2', 'home_3']
shop = ['shop_1', 'shop_2']

gdf_1['home'] = home
gdf_2['shop'] = shop

gdf_1.geometry.apply(lambda g: gdf_2.distance(g))

如上表所示，除了索引之外，原始 gdf ​​中的任何内容都没有保留在结果中，这可能不直观和有用。我想知道如何在结果距离矩阵中保留 gdf ​​中的所有原始列，或者至少保留“home”、“shop”和“distance”列，如下所示：

请注意：“距离”是从家到商店的距离度量，其他“几何”列可能需要后缀

标签： distancegeopandaspreserve

Answer 1

您可以结合使用堆栈和合并来创建所需的输出。

import pandas as pd
import geopandas as gpd

gdf_1 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0, 0], [0, 90, 120]))
gdf_2 = gpd.GeoDataFrame(geometry=gpd.points_from_xy([0, 0], [0, -90]))

home = ['home_1', 'home_2', 'home_3']
shop = ['shop_1', 'shop_2']

gdf_1['home'] = home
gdf_2['shop'] = shop

# set indices so we can have them in gdf_3 
# you could also do this when making gdf_1 and gdf
gdf_1.index = gdf_1['home']
gdf_2.index = gdf_2['shop']


gdf_3 = gdf_1.geometry.apply(lambda g: gdf_2.distance(g))

# reshape our data, stack returns a series here, but we want a df
gdf_4 = pd.DataFrame(gdf_3.stack(level=- 1, dropna=True))
gdf_4.reset_index(inplace=True)

# merge the original columns over
df_merge_1 = pd.merge(gdf_4, gdf_2,
                        left_on='shop',
                        right_on=gdf_2.index,
                        how='outer').fillna('')

df_merge_2 = pd.merge(df_merge_1, gdf_1,
                        left_on='home',
                        right_on=gdf_1.index,
                        how='outer').fillna('')

# get rid of extra cols
df_merge_2 = df_merge_2[[ 'shop',  'home',   0, 'geometry_x',  'geometry_y']]

# rename cols
df_merge_2.columns = ['shop', 'home', 'distance', 'geometry_s', 'geometry_h']

df_merge_2 是 pandas df，但您可以轻松创建 gdf。

df_merge_2_gdf = gpd.GeoDataFrame(df_merge_2, geometry=df_merge_2['geometry_h'])