如何（使用）根据索引或另一列选择和复制数据框中的特定列

Question

I already asked my question but it was not enough accurate in its description. Smart people in this forum already proposed solutions, but I forgot(sorry) to precise that if there were zeros in the relevant columns, they should be kept.

Hello I have a dataframe like below

              2014  2015  2016  2017  2018  2019  

         2014   10    20    30    40    0      5
         2015   0     0    200    0    100     0       
         2016   0     0    200   140    35    10       
         2017   0     0     0     20     0    12       

I need to have a result like this:

    yearStart  yearStart+1  yearStart+2  yearStart+3  yearStart+4  
0      10          20            30          40          0
1      0          200             0          100         0       
2     200         140            35          10          0
3      20          0             12           0          0

The idea is to select in each row, the columns between two dates:

index and index +delta,with delta a parameter (in this example 4) to put them in a dataframe.

With iterrows(), it takes too much time.

I tried with

 df1 = df.apply(lambda x: pd.Series(x[x.keys()>=x.index],1)).fillna(0).astype(int)

but it doesn't work:

TypeError: ('Index(...) must be called with a collection of some kind,
1 was passed', 'occurred at index 2014')

Thank you

Answer 1

One of the ways would be

In [1010]: def yearmove(x):
      ...:     idx = x.index.astype(int)
      ...:     idx = idx - x.name
      ...:     mask = idx >= 0
      ...:     idx = 'yearStart' + idx.astype(str)
      ...:     return pd.Series(x.values[mask], index=idx[mask])
      ...:

In [1011]: df.apply(yearmove, 1).fillna(0).astype(int)
Out[1011]:
      yearStart0  yearStart1  yearStart2  yearStart3  yearStart4  yearStart5
2014          10          20          30          40           0           5
2015           0         200           0         100           0           0
2016         200         140          35          10           0           0
2017          20           0          12           0           0           0