pandas -在 pandas 中为每个组插入空白行 [英] Pandas - Insert blank row for each group in pandas

问题描述

我有一个数据框

 将pandas导入为pd将numpy导入为npdf1 = pd.DataFrame({'group':[1,1,2,2,2]，'value':[2,3，np.nan，5,4]})df1团体价值0 1 21 1 32 2 NaN3 2 54 2 4 

我想在其中 value 的值为 NaN 的每个组之后添加一行.期望输出为:

 组值0 1 21 1 32 1 NaN3 2 NaN4 2 55 2 46 2 NaN 

在我的真实数据集中，除了 value 之外，我还有很多组和更多列，我希望所有这些行在新添加的行中都是 NaN .

非常感谢您的帮助

解决方案

concat 与 append

  s = df1.groupby('group')out = pd.concat([_的i，append({'value':np.nan}，ignore_index = True)，i in s])out.group = out.group.ffill().astype(int) 

应用与追加 ^[1]

  df1.groupby('group').apply(lambda d:d.append({'group':d.name}，ignore_index = True).astype({'group':int})).reset_index(drop = True) 

两种产品:

 组值0 1 2.01 1 3.02 1 NaN3 2 NaN4 2 5.05 2 4.06 2 NaN 

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^[1] 此解决方案由您当地的 @piRSquared

I have a dataframe

import pandas as pd
import numpy as np
df1=pd.DataFrame({'group':[1,1,2,2,2],
             'value':[2,3,np.nan,5,4]})
df1

    group   value
0   1       2
1   1       3
2   2       NaN
3   2       5
4   2       4

I want to add a row after each group in which the value of value is NaN . The desire output is:

   group   value
0   1       2
1   1       3
2   1       NaN
3   2       NaN
4   2       5
5   2       4
6   2       NaN

In my real dataset I have a lot of groups and more columns besides value, I want all of them to be NaN in newly added row.

Thanks a lot for the help

解决方案

concat with append

s = df1.groupby('group')
out = pd.concat([i.append({'value': np.nan}, ignore_index=True) for _, i in s])
out.group = out.group.ffill().astype(int)

apply with append^[1]

df1.groupby('group').apply(
    lambda d: d.append({'group': d.name}, ignore_index=True).astype({'group': int})
).reset_index(drop=True)

Both produce:

   group  value
0      1    2.0
1      1    3.0
2      1    NaN
3      2    NaN
4      2    5.0
5      2    4.0
6      2    NaN

---

^[1] This solution brought to you by your local @piRSquared

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