python Pandas: transform dataframe adding grouped columns

Question

I have the following dataframe:

    n   startdate     enddate  count
0   1  2014-02-01  2014-02-01   6069
1   1  2014-02-01  2014-03-01   1837
2   1  2014-02-01  2014-04-01    107
3   1  2014-02-01  2014-05-01     54
4   1  2014-03-01  2014-03-01  10742
5   1  2014-03-01  2014-04-01   2709
6   1  2014-03-01  2014-05-01   1387
7   1  2014-04-01  2014-04-01   5584
8   1  2014-04-01  2014-05-01   1103
9   1  2014-05-01  2014-05-01   2970

Is it possible to transform it into something similar to:

    n  startdate   2014-02-01  2014-03-01 2014-04-01   2014-05-01
0   1  2014-02-01  6069        1837       107          54
1   1  2014-03-01  0           10742      2709         1387
2   1  2014-04-01  0           0          5584         1103
3   1  2014-05-01  0           0          0            2970

Thanks!

add how you created the dataframe. but some hints: play around with the functions groupby, unstack, and maybe sum — acushner
– acushner, Commented Jun 2, 2014 at 13:36

acushner · Accepted Answer · 2014-06-02 13:38:29Z

5

without recreating your dataframe, i think it's going to be something like:

df.groupby(['n', 'startdate', 'enddate']).sum().unstack()

you might need to fill some nan values as well:

df.fillna(0, inplace=True)

answered Jun 2, 2014 at 13:38

acushner

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