pandas - groupby 多个值？

Question

i have a dataframe that contains cell phone minutes usage logged by date of call and duration.

It looks like this (30 row sample):

          id  user_id  call_date  duration
0    1000_93     1000 2018-12-27      8.52
1   1000_145     1000 2018-12-27     13.66
2   1000_247     1000 2018-12-27     14.48
3   1000_309     1000 2018-12-28      5.76
4   1000_380     1000 2018-12-30      4.22
5   1000_388     1000 2018-12-31      2.20
6   1000_510     1000 2018-12-27      5.75
7   1000_521     1000 2018-12-28     14.18
8   1000_530     1000 2018-12-28      5.77
9   1000_544     1000 2018-12-26      4.40
10  1000_693     1000 2018-12-31      4.31
11  1000_705     1000 2018-12-31     12.78
12  1000_735     1000 2018-12-29      1.70
13  1000_778     1000 2018-12-28      3.29
14  1000_826     1000 2018-12-26      9.96
15  1000_842     1000 2018-12-27      5.85
16    1001_0     1001 2018-09-06     10.06
17    1001_1     1001 2018-10-12      1.00
18    1001_2     1001 2018-10-17     15.83
19    1001_4     1001 2018-12-05      0.00
20    1001_5     1001 2018-12-13      6.27
21    1001_6     1001 2018-12-04      7.19
22    1001_8     1001 2018-11-17      2.45
23    1001_9     1001 2018-11-19      2.40
24   1001_11     1001 2018-11-09      1.00
25   1001_13     1001 2018-12-24      0.00
26   1001_19     1001 2018-11-15     30.00
27   1001_20     1001 2018-09-21      5.75
28   1001_23     1001 2018-10-27      0.98
29   1001_26     1001 2018-10-28      5.90
30   1001_29     1001 2018-09-30     14.78

I want to group by user_id AND call_date with the ultimate goal of calculating the number of minutes used per month over the course of the year, per user.

I thought i could accomplish this by using:

calls.groupby(['user_id','call_date'])['duration'].sum()

but the results aren't what i expected:

  user_id  call_date 
1000     2018-12-26    14.36
         2018-12-27    48.26
         2018-12-28    29.00
         2018-12-29     1.70
         2018-12-30     4.22
         2018-12-31    19.29
1001     2018-08-14    13.86
         2018-08-16    23.46
         2018-08-17     8.11
         2018-08-18     1.74
         2018-08-19    10.73
         2018-08-20     7.32
         2018-08-21     0.00
         2018-08-23     8.50
         2018-08-24     8.63
         2018-08-25    35.39
         2018-08-27    10.57
         2018-08-28    19.91
         2018-08-29     0.54
         2018-08-31    22.38
         2018-09-01     7.53
         2018-09-02    10.27
         2018-09-03    30.66
         2018-09-04     0.00
         2018-09-05     9.09
         2018-09-06    10.06

i'd hoped that it would be grouped like user_id 1000, all calls for jan with duration summed, all calls for feb with duration summed, etc.

i am really new to python and programming in general and am not sure what my next step should be to get these grouped by user_id and month of the year?

Thanks in advance for any insight you can offer.

Regards,

Jared

Answer 1

Something is not quite right in your setup. First of all, both of your tables are the same, so I am not sure if this is a cut-and-paste error or something else. Here is what I do with your data. Load it up like so, note we explicitly convert call_date to Datetime`

from io import StringIO
import pandas as pd
df = pd.read_csv(StringIO(
"""
          id  user_id  call_date  duration
0    1000_93     1000 2018-12-27      8.52
1   1000_145     1000 2018-12-27     13.66
2   1000_247     1000 2018-12-27     14.48
3   1000_309     1000 2018-12-28      5.76
4   1000_380     1000 2018-12-30      4.22
5   1000_388     1000 2018-12-31      2.20
6   1000_510     1000 2018-12-27      5.75
7   1000_521     1000 2018-12-28     14.18
8   1000_530     1000 2018-12-28      5.77
9   1000_544     1000 2018-12-26      4.40
10  1000_693     1000 2018-12-31      4.31
11  1000_705     1000 2018-12-31     12.78
12  1000_735     1000 2018-12-29      1.70
13  1000_778     1000 2018-12-28      3.29
14  1000_826     1000 2018-12-26      9.96
15  1000_842     1000 2018-12-27      5.85
16    1001_0     1001 2018-09-06     10.06
17    1001_1     1001 2018-10-12      1.00
18    1001_2     1001 2018-10-17     15.83
19    1001_4     1001 2018-12-05      0.00
20    1001_5     1001 2018-12-13      6.27
21    1001_6     1001 2018-12-04      7.19
22    1001_8     1001 2018-11-17      2.45
23    1001_9     1001 2018-11-19      2.40
24   1001_11     1001 2018-11-09      1.00
25   1001_13     1001 2018-12-24      0.00
26   1001_19     1001 2018-11-15     30.00
27   1001_20     1001 2018-09-21      5.75
28   1001_23     1001 2018-10-27      0.98
29   1001_26     1001 2018-10-28      5.90
30   1001_29     1001 2018-09-30     14.78
"""), delim_whitespace = True, index_col=0)
df['call_date'] = pd.to_datetime(df['call_date'])

Then using

df.groupby(['user_id','call_date'])['duration'].sum()

does the expected grouping by user and by each date:

user_id  call_date 
1000     2018-12-26    14.36
         2018-12-27    48.26
         2018-12-28    29.00
         2018-12-29     1.70
         2018-12-30     4.22
         2018-12-31    19.29
1001     2018-09-06    10.06
         2018-09-21     5.75
         2018-09-30    14.78
         2018-10-12     1.00
         2018-10-17    15.83
         2018-10-27     0.98
         2018-10-28     5.90
         2018-11-09     1.00
         2018-11-15    30.00
         2018-11-17     2.45
         2018-11-19     2.40
         2018-12-04     7.19
         2018-12-05     0.00
         2018-12-13     6.27
         2018-12-24     0.00

If you want to group by month as you seem to suggest you can use the Grouper functionality:

df.groupby(['user_id',pd.Grouper(key='call_date', freq='1M')])['duration'].sum()

which produces

user_id  call_date 
1000     2018-12-31    116.83
1001     2018-09-30     30.59
         2018-10-31     23.71
         2018-11-30     35.85
         2018-12-31     13.46

Let me know if you are getting different results from following these steps