如何使用pandas Grouper获取每小时内的值总和

Question

I have the following table:

         Hora_Retiro  count_uses
0         00:00:18           1
1         00:00:34           1
2         00:02:27           1
3         00:03:13           1
4         00:06:45           1
...            ...         ...
748700    23:58:47           1
748701    23:58:49           1
748702    23:59:11           1
748703    23:59:47           1
748704    23:59:56           1

And I want to group all values within each hour, so I can see the total number of uses per hour (00:00:00 - 23:00:00)

I have the following code:

hora_pico_aug= hora_pico.groupby(pd.Grouper(key="Hora_Retiro",freq='H')).count()

Hora_Retiro column is of timedelta64[ns] type Which gives the following output:

                count_uses
Hora_Retiro            
00:00:02           2566
01:00:02            602
02:00:02            295
03:00:02              5
04:00:02             10
05:00:02           4002
06:00:02          16075
07:00:02          39410
08:00:02          76272
09:00:02          56721
10:00:02          36036
11:00:02          32011
12:00:02          33725
13:00:02          41032
14:00:02          50747
15:00:02          50338
16:00:02          42347
17:00:02          54674
18:00:02          76056
19:00:02          57958
20:00:02          34286
21:00:02          22509
22:00:02          13894
23:00:02           7134

However, the index column starts at 00:00:02, and I want it to start at 00:00:00, and then go from one hour intervals. Something like this:

                count_uses
Hora_Retiro            
00:00:00           2565
01:00:00            603
02:00:00            295
03:00:00              5
04:00:00             10
05:00:00           4002
06:00:00          16075
07:00:00          39410
08:00:00          76272
09:00:00          56721
10:00:00          36036
11:00:00          32011
12:00:00          33725
13:00:00          41032
14:00:00          50747
15:00:00          50338
16:00:00          42347
17:00:00          54674
18:00:00          76056
19:00:00          57958
20:00:00          34286
21:00:00          22509
22:00:00          13894
23:00:00           7134

How can i make it to start at 00:00:00??

Thanks for the help!

Answer 1

You can create an hour column from Hora_Retiro column.

df['hour'] = df['Hora_Retiro'].dt.hour

And then groupby on the basis of hour

gpby_df = df.groupby('hour')['count_uses'].sum().reset_index()
gpby_df['hour'] = pd.to_datetime(gpby_df['hour'], format='%H').dt.time
gpby_df.columns = ['Hora_Retiro', 'sum_count_uses']
gpby_df

gives

Hora_Retiro sum_count_uses
0   00:00:00    14
1   09:00:00    1
2   10:00:00    2
3   20:00:00    2

Answer 2

I assume that Hora_Retiro column in your DataFrame is of Timedelta type. It is not datetime , as in this case there would be printed also the date part.

Indeed, your code creates groups starting at the minute / second taken from the first row.

To group by "full hours":

• round each element in this column to hour ,
• then group (just by this rounded value).

The code to do it is:

hora_pico.groupby(hora_pico.Hora_Retiro.apply(
    lambda tt: tt.round('H'))).count_uses.count()

However I advise you to make up your mind, what do you want to count: rows or values in count_uses column. In the second case replace count function with sum .