大 pandas 在一组内进行内插 [英] Pandas interpolate within a groupby

问题描述

我有一个包含以下信息的数据框：

 文件名val1 val2 
t 
 1 file1.csv 5 10 
 2 file1.csv NaN NaN 
 3 file1.csv 15 20 
 6 file2.csv NaN NaN 
 7 file2.csv 10 20 
 8 file2.csv 12 15 
  

我想根据索引插入数据帧中的值， strong>，但仅限于每个文件组

中。

为了插值，我通常会执行

 df = df.interpolate（method =index）
  

并且为了分组，我做了

$ p $ grouped = df.groupby（filename）


我想插入的数据框看起来像这样：

  filename val1 val2 
t 
 1 file1.csv 5 10 
 2 file1.csv 10 15 
 3 file1.csv 15 20 
 6 file2.csv NaN NaN 
 7 file2.csv 10 20 
 8 file2.csv 12 15 
  

如果NaN在t = 6时仍然存在，因为它们是file2组中的第一项。

我怀疑我需要使用apply ，但一直没能弄清楚......

  grouped.apply（interp1d）
 ... 
 TypeError：__init __（）至少需要3个参数（给出2个）
  

任何帮助将不胜感激。

解决方案

 >>> df.groupby（'filename'）。apply（lambda group：group.interpolate（method ='index'））
 filename val1 val2 
t 
 1 file1.csv 5 10 
 2 file1.csv 10 15 
 3 file1.csv 15 20 
 6 file2.csv NaN NaN 
 7 file2.csv 10 20 
 8 file2.csv 12 15 
  

I've got a dataframe with the following information:

    filename    val1    val2
t                   
1   file1.csv   5       10
2   file1.csv   NaN     NaN
3   file1.csv   15      20
6   file2.csv   NaN     NaN
7   file2.csv   10      20
8   file2.csv   12      15

I would like to interpolate the values in the dataframe based on the indices, but only within each file group.

To interpolate, I would normally do

df = df.interpolate(method="index")

And to group, I do

grouped = df.groupby("filename")

I would like the interpolated dataframe to look like this:

    filename    val1    val2
t                   
1   file1.csv   5       10
2   file1.csv   10      15
3   file1.csv   15      20
6   file2.csv   NaN     NaN
7   file2.csv   10      20
8   file2.csv   12      15

Where the NaN's are still present at t = 6 since they are the first items in the file2 group.

I suspect I need to use "apply", but haven't been able to figure out exactly how...

grouped.apply(interp1d)
...
TypeError: __init__() takes at least 3 arguments (2 given)

Any help would be appreciated.

解决方案

>>> df.groupby('filename').apply(lambda group: group.interpolate(method='index'))
    filename  val1  val2
t                       
1  file1.csv     5    10
2  file1.csv    10    15
3  file1.csv    15    20
6  file2.csv   NaN   NaN
7  file2.csv    10    20
8  file2.csv    12    15

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