pandas 根据上方的行向下填充缺失值 [英] Pandas Filling Missing Values Down Based on Row Above

问题描述

我有一个如下数据框:

import pandas as pd
data={'col1':[1,3,3,1,2,3,2,2, 1], 'col2':[np.nan, 1, np.nan, 1, np.nan, np.nan, np.nan, 2, np.nan]}
df=pd.DataFrame(data,columns=['col1', 'col2'])
print df

   col1  col2
0     1   NaN
1     3   1.0
2     3   NaN
3     1   1.0
4     2   NaN
5     3   NaN
6     2   NaN
7     2   2.0
8     1   NaN

如果col2的值等于1.0或col2中的上一行是1.0，则我试图在第三列中填充col2中的NaN值.最终的数据帧如下所示:

I am trying to make a third column that fills in the NaN vales in col2 if the value of col2 is equal to 1.0 or the row above in col2 is 1.0. The final dataframe would look like this:

 col1  col2  col3
0     1   NaN   NaN
1     3   1.0   1.0
2     3   NaN   1.0
3     1   1.0   1.0
4     2   NaN   1.0
5     3   NaN   1.0
6     2   NaN   1.0
7     2   2.0   2.0
8     1   NaN   NaN

我尝试的第一种方法是:

First approach I tried was:

df['col3'] = ((df['col2']== 1) | ((df['col2'].shift()== 1))).astype('int')

这让我有了这个数据框:

This leaves me with this dataframe:

col1  col2  col3
0     1   NaN     0
1     3   1.0     1
2     3   NaN     1
3     1   1.0     1
4     2   NaN     1
5     3   NaN     0
6     2   NaN     0
7     2   2.0     0
8     1   NaN     0

这将更正缺失值的第一个实例，但不会继续填充缺失值.我还尝试使用np.where()函数，但得到的结果相同.

Which corrects the first instance of a missing value, but does not continue to fill missing values. I also tried using the np.where() function and I get the same results.

有没有办法在大熊猫中编写它来连续修复多个实例?

Is there a way to write this in pandas where it fixes multiple instances in a row?

推荐答案

您可以使用 np.where ，方法是查看前向填充等于1的位置，在前向填充为True的位置填充1，然后在前向填充为False的情况下退回到'col2'的值:

You can use np.where by looking at where the forward-fill is equal to one, filling 1 where it's True, and falling back to the value of 'col2' when it's False:

df['col2'] = np.where(df['col2'].ffill() == 1, 1, df['col2'])

结果输出:

   col1  col2
0     1   NaN
1     3   1.0
2     3   1.0
3     1   1.0
4     2   1.0
5     3   1.0
6     2   1.0
7     2   2.0
8     1   NaN

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