pandas -在缺少数据的地方插入行 [英] Pandas - insert rows where data is missing

问题描述

我有一个数据集，下面是一个示例:

I have a dataset, here is an example:

df = DataFrame({"Seconds_left":[5,10,15,25,30,35,5,10,15,30], "Team":["ATL","ATL","ATL","ATL","ATL","ATL","SAS","SAS","SAS","SAS"], "Fouls": [1,2,3,3,4,5,5,4,1,1]})


   Fouls  Seconds_left Team
0      1             5  ATL
1      2            10  ATL
2      3            15  ATL
3      3            25  ATL
4      4            30  ATL
5      5            35  ATL
6      5             5  SAS
7      4            10  SAS
8      1            15  SAS
9      1            30  SAS

现在，我想在Seconds_left列中的数据丢失的地方插入行:

Now I would like to insert rows where data in the Seconds_left column is missing:

Id Fouls Seconds_left   Team
0      1            5    ATL
1      2           10    ATL
2      3           15    ATL
3    NaN           20    ATL
4      3           25    ATL
5      4           30    ATL
6      5           35    ATL
7      5            5    SAS
8      4           10    SAS
9      1           15    SAS
10   NaN           20    SAS
11   NaN           25    SAS
12     1           30    SAS
13   NaN           35    SAS

我已经尝试过使用重新索引等方法，但是显然它不起作用，因为存在重复项.

I tried already with reindexing etc. but obviously it does not function as there are duplicates.

有人知道如何解决这个问题吗?

Has somebody got any idea how to solve this?

谢谢！

推荐答案

创建MultiIndex并重新索引+ reset_index:

Create a MultiIndex and reindex + reset_index:

idx = pd.MultiIndex.from_product([df['Team'].unique(), 
                                  np.arange(5, df['Seconds_left'].max()+1, 5)],
                                 names=['Team', 'Seconds_left'])

df.set_index(['Team', 'Seconds_left']).reindex(idx).reset_index()
Out: 
   Team  Seconds_left  Fouls
0   ATL             5    1.0
1   ATL            10    2.0
2   ATL            15    3.0
3   ATL            20    NaN
4   ATL            25    3.0
5   ATL            30    4.0
6   ATL            35    5.0
7   SAS             5    5.0
8   SAS            10    4.0
9   SAS            15    1.0
10  SAS            20    NaN
11  SAS            25    NaN
12  SAS            30    1.0
13  SAS            35    NaN

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