Separate aggregated data in different rows

Asked Oct 25 '16 at 14:56

Active Oct 25 '16 at 14:56

Viewed 27 times

I have a panda's dataframe with the following structure:

    Date        product  count
0   2016-09-01  sugar    3   
1   2016-09-01  pasta    4   
2   2016-09-01  coffee   5   
3   2016-09-02  sugar    25  
4   2016-09-02  pasta    30   
5   2016-09-02  salad    5

I would like to obtain the unaggregated data (like the picture below), but I don't know how to achieve it using pandas. In apache spark it would be easier, I could use explode after some trivial transformations.

    Date        product  
0   2016-09-01  sugar
1   2016-09-01  sugar
2   2016-09-01  sugar
3   2016-09-01  pasta
.   .           .
.   .           .
.   .           .
?   2016-09-02  salad

asked Oct 25 '16 at 14:56

Alberto Bonsanto

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basically you can do `df.loc[np.repeat(df.index.values,df['count'])]` – EdChum Oct 25 '16 at 15:04
@EdChum what happens if I have more than one column? For example, I have a count for purchases, another for returns and I want to obtain those in different rows? – Alberto Bonsanto Oct 25 '16 at 15:08
1

You'd have to do it separately for each count and then merge/concat them after – EdChum Oct 25 '16 at 15:08
2

@EdChum, i would suggest one cosmetic correction: `df.loc[np.repeat(df.index.values, df.pop('count'))]` – MaxU - stand with Ukraine Oct 25 '16 at 15:15
@MaxU yes I guess that's actually what the OP is after – EdChum Oct 25 '16 at 15:16

Separate aggregated data in different rows

0 Answers0