Python 如何使用唯一名称透视表并避免空值？

我有一个Pandas数据框，当我在一个具有唯一值（混合数据类型）的列上旋转时，它如下所示

In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame({'A' : ['one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight'],'B' : [0.815, 0.765, 'two', 'four', 0.981, 'six', 'seven', 'eight']})
In [4]: df
Out[4]: 
       A      B
0    one  0.815
1    two  0.765
2  three    two
3   four   four
4   five  0.981
5    six    six
6  seven  seven
7  eight  eight

In [5]: df.pivot(columns='A', values='B')
Out[5]: 
A  eight   five  four    one  seven   six three    two
0   None   None  None  0.815   None  None  None   None
1   None   None  None   None   None  None  None  0.765
2   None   None  None   None   None  None   two   None
3   None   None  four   None   None  None  None   None
4   None  0.981  None   None   None  None  None   None
5   None   None  None   None   None   six  None   None
6   None   None  None   None  seven  None  None   None
7  eight   None  None   None   None  None  None   None

---

有没有一种方法可以将列“a”上的此数据帧旋转到一行，而不使用“None”值？

我认为您建议转置数据帧

df.set_index('A').T

---

之所以会出现这种情况，是因为脚本会在B列中查找值，如果没有找到值，则只会返回

None

。如您所见，（7，8）=8，这是B列的第8行，（4，5）=0.981，这是B列的第5行，以此类推。这里真正需要的是

transpose（）

如果要更改列标题，可以执行以下操作：

df_t.column = df_t.iloc[0] #replaces the column headers with the first row
df_t = df_t[1:] #deletes the first line so you don't see a duplicate

>>> df_t
A    one    two three  four   five  six  seven  eight
B  0.815  0.765   two  four  0.981  six  seven  eight

df_t.column = df_t.iloc[0] #replaces the column headers with the first row
df_t = df_t[1:] #deletes the first line so you don't see a duplicate

>>> df_t
A    one    two three  four   five  six  seven  eight
B  0.815  0.765   two  four  0.981  six  seven  eight