概述
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.
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))总结
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