Question

I would like to group a Pandas dataframe by hour disregarding the date.

My data:

id    opened_at             count   sum
      2016-07-01 07:02:05   1       46.14
154   2016-07-01 07:34:02   1       479
      2016-07-01 10:10:01   1       127.14
      2016-07-02 12:01:04   1       8.14
      2016-07-02 12:00:50   1       18.14

I am able to group by hour with date taken into account by using the following.

groupByLocationDay = df.groupby([df.id,
                                pd.Grouper(key='opened_at', freq='3h')])

I get the following

id    opened_at             count   sum
      2016-07-01 06:00:00   2       4296.14
154   2016-07-01 09:00:00   46      43716.79
      2016-07-01 12:00:00   169     150827.14
      2016-07-02 12:00:00   17      1508.14
      2016-07-02 09:00:00   10      108.14

How can I group by hour only, so that it would look like the following.

id    opened_at         count   sum
      06:00:00  2       4296.14
154   09:00:00  56      43824.93
      12:00:00  203     152335.28

The original data is on hourly basis, thus I need to get 3h frequency. Thanks!

Answer 1

you can do it this way:

In [134]: df
Out[134]:
    id           opened_at  count     sum
0  154 2016-07-01 07:02:05      1   46.14
1  154 2016-07-01 07:34:02      1  479.00
2  154 2016-07-01 10:10:01      1  127.14
3  154 2016-07-02 12:01:04      1    8.14
4  154 2016-07-02 12:00:50      1   18.14
5  154 2016-07-02 08:34:02      1  479.00

In [135]: df.groupby(['id', df.opened_at.dt.hour // 3 * 3]).sum()
Out[135]:
               count      sum
id  opened_at
154 6              3  1004.14
    9              1   127.14
    12             2    26.28

pandas grouper int by frequency

1 个答案: