这种不同的行为怎么可能？类型错误： 不可哈希 类型： 'Point'

Question

我试图找到并过滤GeoDataFrame(df1(中与第二个GDF(df2(中的点接近的点，反之亦然。我用这段代码：

ps1 = []
ps2 = []
for p1 in df1.geometry:
for p2 in df2.geometry:
dist = haversine(p1.y,p1.x,p2.y,p2.x)
if dist < 100:
ps1.append(p1)
ps2.append(p2)
df1 = df1[df1.geometry.isin(ps1)]
df2 = df2[df2.geometry.isin(ps2)]

然而，我在最后一行遇到了一个错误：TypeError: unhashable type: 'Point'

但它上面的行就像一个符咒，两行的数据类型(df1/df2和ps1/ps2(完全相同。

这怎么可能？如何解决？

编辑：

变量类型：

df1         :  <class 'geopandas.geodataframe.GeoDataFrame'>
df1.geometry:  <class 'geopandas.geoseries.GeoSeries'>
ps1         :  <class 'list'>
val1        :  <class 'pandas.core.series.Series'>
df2         :  <class 'geopandas.geodataframe.GeoDataFrame'>
df2.geometry:  <class 'geopandas.geoseries.GeoSeries'>
ps2         :  <class 'list'>

编辑2:

df1.dtypes
Out[301]: 
lat                     float64
lon                     float64
time        datetime64[ns, UTC]
geometry               geometry
dtype: object
df2.dtypes
Out[302]: 
lat                     float64
lon                     float64
time        datetime64[ns, UTC]
geometry               geometry
dtype: object

MWE:

import pandas as pd
from pandas import Timestamp
import geopandas as gpd
import numpy as np
def haversine(lat1, lon1, lat2, lon2, to_radians=True, earth_radius=6371000):
"""
slightly modified version: of http://stackoverflow.com/a/29546836/2901002
Calculate the great circle distance between two points
on the earth (specified in decimal degrees or in radians)
All (lat, lon) coordinates must have numeric dtypes and be of equal length.
"""
if to_radians:
lat1, lon1, lat2, lon2 = np.radians([lat1, lon1, lat2, lon2])
a = np.sin((lat2-lat1)/2.0)**2 + 
np.cos(lat1) * np.cos(lat2) * np.sin((lon2-lon1)/2.0)**2
return earth_radius * 2 * np.arcsin(np.sqrt(a))
df1 = pd.DataFrame.from_dict({'lat': {0: 52.378851603519905,
1: 52.37896949048437,
2: 52.378654032960824,
3: 52.37818902922923},
'lon': {0: 4.88585622453752,
1: 4.886671616078047,
2: 4.886413945242339,
3: 4.885995520636016},
'time': {0: Timestamp('2019-11-05 11:31:42+0000', tz='UTC'),
1: Timestamp('2019-11-05 11:32:22+0000', tz='UTC'),
2: Timestamp('2019-11-05 11:32:49+0000', tz='UTC'),
3: Timestamp('2019-11-05 11:33:31+0000', tz='UTC')}})
df2 = pd.DataFrame.from_dict({'lat': {0: 52.378851603519905,
1: 52.369466977365214,
2: 52.36923115238693,
3: 52.36898222465506},
'lon': {0: 4.88585622453752,
1: 4.9121331184582,
2: 4.912723204441477,
3: 4.913505393878495},
'time': {0: Timestamp('2019-11-05 08:54:32+0000', tz='UTC'),
1: Timestamp('2019-11-05 08:55:06+0000', tz='UTC'),
2: Timestamp('2019-11-05 08:55:40+0000', tz='UTC'),
3: Timestamp('2019-11-05 08:56:22+0000', tz='UTC')}})
df1 = gpd.GeoDataFrame(df1, geometry=gpd.points_from_xy(df1.lat, df1.lon))
df2 = gpd.GeoDataFrame(df2, geometry=gpd.points_from_xy(df2.lat, df2.lon))
ps1 = []
ps2 = []
for p1 in df1.geometry:
for p2 in df2.geometry:
dist = haversine(p1.y,p1.x,p2.y,p2.x)
if dist < 100:
ps1.append(p1)
ps2.append(p2)
val1 = gpd.GeoDataFrame(df1)
val2 = gpd.GeoDataFrame(df2)
# print(type(df1))
# print(type(df2))
# print(type(ps1))
# print(type(ps2))
print('df1         : ', type(df1))
print('df1.geometry: ', type(df1.geometry))
print('ps1         : ', type(ps1))
val1 = df1.geometry.isin(ps1)
print('val1        : ', type(val1))
print('df2         : ', type(df2))
print('df2.geometry: ', type(df2.geometry))
print('ps2         : ', type(ps2))
val2 = df2.geometry.isin(ps2)
print('val2        : ', type(val2))
# df1 = df1[df1.geometry.isin(ps1)]
# df2 = df2[df2.geometry.isin(ps2)]

Answer 1

正如错误所说，Point是不可哈希的(因为这个？(。

事实证明，出于我忽略的原因，pandas.Series.isin函数似乎要求数据是可散列的。看看我刚刚发布的问题。

至于你的问题，一个变通办法是使用列表，并将其再次转换为系列，如：

val2 = pd.Series([v in ps2 for v in df2.geometry])