Python3.x 如何将值映射到具有上限和下限的新列

一种 Select 可能是建立一个嵌套的字典，并映射每个组的值，最终使用cut来限制范围.如果你有很多词典，效率可能不会很高.

如果您稍微更改一下字典，另一个 Select 是创建一个DataFrame并使用merge_asof:

Inf = float('inf')

a_phase = {
-Inf:0.001,
-9:0.010,
-8:0.022,
-7:0.026,
-6:0.092,
-5:0.091,
-4:0.082,
-3:0.121,
-2:0.060,
-1:0.105,
0:0.018,
1:0.092,
2:0.092,
3:0.092,
4:0.092,
4.1:0.000,
}
b_phase = {
-Inf:0.016,
-9:0.011,
-8:0.021,
-7:0.028,
-6:0.052,
-5:0.075,
-4:0.057,
-3:0.102,
-2:0.238,
-1:0.270,
0:0.034,
1:0.014,
2:0.061,
2.1:0.000,
}
c_phase = {
-Inf:0.016,
-9:0.016,
-8:0.011,
-7:0.010,
-6:0.038,
-5:0.015,
-4:0.099,
-3:0.117,
-2:0.216,
-1:0.213,
0:0.008,
1:0.008,
2:0.008,
2.1:0.000,
}

ref = (pd.DataFrame({'a': a_phase, 'b': b_phase, 'c': c_phase})
         .melt(var_name='type', value_name='weight', ignore_index=False)
         .rename_axis('phase').reset_index()
         .sort_values(by='phase')
         .dropna(subset='weight')
       )

out = (pd.merge_asof(df_.reset_index()
                        .astype({'phase': 'float64'})
                        .sort_values(by='phase'),
                     ref, by='type', on='phase')#, direction='forward')
         .set_index('index').reindex(df_.index)
       )

输出:

  type  phase  weight
0    a  -10.0   0.001
1    b    3.0   0.000
2    c    2.0   0.008
3    a    1.0   0.092
4    b   -7.0   0.028
5    c   -3.0   0.117
6    a   -1.0   0.105
7    b   -5.0   0.075
8    c    4.0   0.000