Question

我想用给定组的最后一个有效值填充数据帧NaN。例如：

import pandas as pd
import random as randy
import numpy as np

df_size = int(1e1)                
df = pd.DataFrame({'category': randy.sample(np.repeat(['Strawberry','Apple',],df_size),df_size), 'values': randy.sample(np.repeat([np.NaN,0,1],df_size),df_size)}, index=randy.sample(np.arange(0,10),df_size)).sort_index(by=['category'], ascending=[True])

提供：

     category   value
7       Apple     NaN
6       Apple       1
4       Apple       0
5       Apple     NaN
1       Apple     NaN
0  Strawberry       1
8  Strawberry     NaN
2  Strawberry       0
3  Strawberry       0
9  Strawberry     NaN

我想要计算的列看起来像这样：

     category   value  last_value
7       Apple     NaN         NaN
6       Apple       1         NaN
4       Apple       0           1
5       Apple     NaN           0
1       Apple     NaN           0
0  Strawberry       1         NaN
8  Strawberry     NaN           1
2  Strawberry       0           1
3  Strawberry       0           0
9  Strawberry     NaN           0

尝试shift()和iterrows()，但无济于事。

Answer 1

看起来你想先做一个ffill然后做一个shift：

In [11]: df['value'].ffill()
Out[11]:
7   NaN
6     1
4     0
5     0
1     0
0     1
8     1
2     0
3     0
9     0
Name: value, dtype: float64

In [12]: df['value'].ffill().shift(1)
Out[12]:
7   NaN
6   NaN
4     1
5     0
1     0
0     0
8     1
2     1
3     0
9     0
Name: value, dtype: float64

要在每个组上执行此操作，您必须先分组，然后应用此功能：

In [13]: g = df.groupby('category')

In [14]: g['value'].apply(lambda x: x.ffill().shift(1))
Out[14]:
7   NaN
6   NaN
4     1
5     0
1     0
0   NaN
8     1
2     1
3     0
9     0
dtype: float64

In [15]: df['last_value'] = g['value'].apply(lambda x: x.ffill().shift(1))

Pandas访问最后一个非空值

1 个答案: