python – 访问最后一个非null值的Pandas

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),'values': randy.sample(np.repeat([np.NaN,1],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

我想要计算的列如下所示：

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.

解决方法

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

要对每个组执行此操作,您必须先按groupby类别,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))

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