UPDATE2:如果你想检查都x
和y
属于由同一行中的垃圾桶df_loc
(或loc_df
):
xstep = 10
ystep = 50
In [201]: (df.assign(bin=(pd.cut(df.loc_x, np.arange(-500, 500, xstep)).astype(str)
.....: +
.....: pd.cut(df.loc_y, np.arange(-500, 500, ystep)).astype(str)
.....: )
.....: )
.....:)['bin'].isin(df_loc.sum(axis=1))
Out[201]:
0 True
1 False
2 True
3 False
4 False
Name: bin, dtype: bool
说明:
In [202]: (df.assign(bin=(pd.cut(df.loc_x, np.arange(-500, 500, xstep)).astype(str)
.....: +
.....: pd.cut(df.loc_y, np.arange(-500, 500, ystep)).astype(str)
.....: )
.....: )
.....:)
Out[202]:
loc_x loc_y bin
0 -15 25 (-20, -10](0, 50]
1 30 35 (20, 30](0, 50]
2 5 -45 (0, 10](-50, 0]
3 -135 -200 (-140, -130](-250, -200]
4 5 25 (0, 10](0, 50]
In [203]: df_loc.sum(axis=1)
Out[203]:
0 (-20, -10](0, 50]
1 (-140, -130](100, 150]
2 (0, 10](-50, 0]
dtype: object
UPDATE:,如果你想检查x
是否属于loc_x_bin
和y
属于loc_y_bin
(来自同一行中df_loc
不一定):
如果df_loc.dtypes
不显示category
这两列,那么你可能想c onvert您的类别category
D类第一:
xstep = 10
ystep = 50
df['in_bins'] = ( (pd.cut(df.loc_x, np.arange(-500, 500, xstep)).isin(df_loc.loc_x_bin))
&
(pd.cut(df.loc_y, np.arange(-500, 500, ystep)).isin(df_loc.loc_y_bin))
)
测试:
In [130]: df['in_bins'] = ( (pd.cut(df.loc_x, np.arange(-500, 500, xstep)).isin(df_loc.loc_x_bin))
.....: &
.....: (pd.cut(df.loc_y, np.arange(-500, 500, ystep)).isin(df_loc.loc_y_bin))
.....: )
In [131]: df
Out[131]:
loc_x loc_y in_bins
0 -15 25 True
1 30 35 False
2 5 -45 True
3 -135 -200 False
哪些
那么你就可以 “飞” 归类在
df
你列'loc_df'中的对象?那些字符串? ''(-20,-10]'' – piRSquared他们是类别 – Hound
运行这个'type(loc_df.iloc [0,0])'并且回报你得到的结果 – piRSquared