缺少数据，在Pandas中插入行并用NAN填充 [英] Missing data, insert rows in Pandas and fill with NAN

问题描述

我是Python和Pandas的新手，所以可能有一个我看不到的简单解决方案.

I'm new to Python and Pandas so there might be a simple solution which I don't see.

我有许多不连续的数据集，如下所示:

I have a number of discontinuous datasets which look like this:

ind A    B  C  
0   0.0  1  3  
1   0.5  4  2  
2   1.0  6  1  
3   3.5  2  0  
4   4.0  4  5  
5   4.5  3  3  

我现在正在寻找一种解决方案，以实现以下目标:

I now look for a solution to get the following:

ind A    B  C  
0   0.0  1  3  
1   0.5  4  2  
2   1.0  6  1  
3   1.5  NAN NAN  
4   2.0  NAN NAN  
5   2.5  NAN NAN  
6   3.0  NAN NAN  
7   3.5  2  0  
8   4.0  4  5  
9   4.5  3  3  

问题在于，A中的间隙在数据集的位置和长度上各不相同...

The problem is,that the gap in A varies from dataset to dataset in position and length...

推荐答案

set_index和reset_index是您的朋友.

df = DataFrame({"A":[0,0.5,1.0,3.5,4.0,4.5], "B":[1,4,6,2,4,3], "C":[3,2,1,0,5,3]})

首先将A列移至索引:

In [64]: df.set_index("A")
Out[64]: 
     B  C
 A        
0.0  1  3
0.5  4  2
1.0  6  1
3.5  2  0
4.0  4  5
4.5  3  3

然后使用新索引重新索引，此处使用nans填充缺少的数据.我们使用Index对象是因为我们可以命名它.下一步将使用它.

Then reindex with a new index, here the missing data is filled in with nans. We use the Index object since we can name it; this will be used in the next step.

In [66]: new_index = Index(arange(0,5,0.5), name="A")
In [67]: df.set_index("A").reindex(new_index)
Out[67]: 
      B   C
0.0   1   3
0.5   4   2
1.0   6   1
1.5 NaN NaN
2.0 NaN NaN
2.5 NaN NaN
3.0 NaN NaN
3.5   2   0
4.0   4   5
4.5   3   3

最后使用reset_index将索引移回到列中.自从我们为索引命名以来，它就神奇地起作用了:

Finally move the index back to the columns with reset_index. Since we named the index, it all works magically:

In [69]: df.set_index("A").reindex(new_index).reset_index()
Out[69]: 
       A   B   C
0    0.0   1   3
1    0.5   4   2
2    1.0   6   1
3    1.5 NaN NaN
4    2.0 NaN NaN
5    2.5 NaN NaN
6    3.0 NaN NaN
7    3.5   2   0
8    4.0   4   5
9    4.5   3   3

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