python - pandas fillna 在列中包含前几行的累加值(每个 nan 后重置)

Question

我找到了一个按行解决这个问题的解决方案，但是有没有一种快速的方法可以按列来解决这个问题？

这是数据框的快速示例:

import pandas as pd
import numpy as np

df = pd.DataFrame([['GB',43.76],
['TEN',17.3],
['ARI',0.2],
['ATL',12.3],
['HOU',21.1],
['ARI',1.7],
['ATL',12.6],
['SF',15.0],
['GB',5.7],
[1.0,np.nan],
['GB',43.76],
['TEN',17.3],
['ARI',0.2],
['ATL',12.3],
['HOU',21.1],
['ARI',1.7],
['ATL',12.6],
['BUF',7.0],
['GB',5.7],
[2.0,np.nan]], columns = ['team','points'])

我一直在尝试操作df['sum'] = df['points'].cumsum()。显然它会计算累积总和，但我需要它做的是当/如果达到 nan 时重新启动，而不是仅仅跳过它。

Answer 1

使用GroupBy.cumsum通过另一个 cumsum 检查缺失值创建的助手系列:

df['sum'] = df.groupby(df['points'].isna().cumsum())['points'].cumsum()
print (df)
   team  points     sum
0    GB   43.76   43.76
1   TEN   17.30   61.06
2   ARI    0.20   61.26
3   ATL   12.30   73.56
4   HOU   21.10   94.66
5   ARI    1.70   96.36
6   ATL   12.60  108.96
7    SF   15.00  123.96
8    GB    5.70  129.66
9     1     NaN     NaN
10   GB   43.76   43.76
11  TEN   17.30   61.06
12  ARI    0.20   61.26
13  ATL   12.30   73.56
14  HOU   21.10   94.66
15  ARI    1.70   96.36
16  ATL   12.60  108.96
17  BUF    7.00  115.96
18   GB    5.70  121.66
19    2     NaN     NaN