python - Pandas:从数据框的一行中取一个标签并将其转换为列名

Question

我有以下示例:

import numpy as np
import pandas as pd

feature_labels = ["A", "B", "C"]
n_days = 3
n_persons = 2
n_features = len(feature_labels)

data = pd.DataFrame({
    "day": np.repeat(list(np.arange(3))*n_persons, n_days),
    "person": np.repeat(np.arange(2), n_days*n_features),
    "feature": feature_labels*(n_days*n_persons),
    "value": np.random.rand(n_features*n_days*n_persons)
})
data

它返回:

    day feature  person     value
0     0       A       0  0.519279
1     0       B       0  0.243156
2     0       C       0  0.093231
3     1       A       0  0.046888
4     1       B       0  0.775699
5     1       C       0  0.757114
6     2       A       0  0.983894
7     2       B       0  0.709877
8     2       C       0  0.256220
9     0       A       1  0.823253
10    0       B       1  0.014050
11    0       C       1  0.740373
12    1       A       1  0.554485
13    1       B       1  0.828009
14    1       C       1  0.398025
15    2       A       1  0.033659
16    2       B       1  0.904537
17    2       C       1  0.649851

我需要得到一个包含以下列的数据表:day、person、A、B 和 C，并包含相应的值。如果您能告诉我如何使用 pandas 的 API 来做到这一点，我将不胜感激。

Answer 1

In [325]: data.set_index(["day", "person", "feature"])['value'] \
              .unstack('feature').reset_index().rename_axis(None, 1)
Out[325]:
   day  person         A         B         C
0    0       0  0.852395  0.975006  0.884853
1    0       1  0.044862  0.505431  0.376252
2    1       0  0.359508  0.598859  0.354796
3    1       1  0.592805  0.629942  0.142600
4    2       0  0.340190  0.178081  0.237694
5    2       1  0.933841  0.946380  0.602297

解释:

如果我们在执行 .unstack() 之前不指定 ['value']，我们将得到多级列，因为通常我们可以有多个非-“取消堆叠”时的索引列，因此 Pandas 用列名“标记”它:

In [328]: data.set_index(["day", "person", "feature"]).unstack('feature')
Out[328]:
               value
feature            A         B         C
day person
0   0       0.852395  0.975006  0.884853
    1       0.044862  0.505431  0.376252
1   0       0.359508  0.598859  0.354796
    1       0.592805  0.629942  0.142600
2   0       0.340190  0.178081  0.237694
    1       0.933841  0.946380  0.602297

In [329]: data.set_index(["day", "person", "feature"])['value'].unstack('feature')
Out[329]:
feature            A         B         C
day person
0   0       0.852395  0.975006  0.884853
    1       0.044862  0.505431  0.376252
1   0       0.359508  0.598859  0.354796
    1       0.592805  0.629942  0.142600
2   0       0.340190  0.178081  0.237694
    1       0.933841  0.946380  0.602297

.rename_axis(None, axis=1) 帮助我们摆脱 feature(“列”轴的名称):

In [334]: x = data.set_index(["day", "person", "feature"])['value'].unstack('feature').reset_index()

In [335]: x.columns
Out[335]: Index(['day', 'person', 'A', 'B', 'C'], dtype='object', name='feature')
# NOTE:                                                                 ^^^^^^^

In [336]: x = x.rename_axis(None, axis=1)

In [337]: x.columns
Out[337]: Index(['day', 'person', 'A', 'B', 'C'], dtype='object')