How to do groupby max to create new columns in pandas dataframe

debugcn 投稿 Dev

Rahul rajan

I have dataframe like below to which I want apply an sql logic which mentioned below

df.head(25)

ORDER_ID    CODE    STATUS_DATE                       RNK
19837715    0400    22/10/19 08:11:08.000000000 AM GMT  2
19837715    0400    22/10/19 10:00:03.000000000 AM GMT  1
19837715    0400    22/10/19 10:47:08.000000000 AM GMT  3
19837715    0500    22/10/19 10:00:00.000000000 AM GMT  1
19837715    1100    01/11/19 10:02:00.000000000 AM GMT  1
19837715    1240    02/11/19 08:00:00.000000000 AM GMT  1
19837833    0400    22/10/19 08:13:09.000000000 AM GMT  3
19837833    0400    22/10/19 08:22:09.000000000 AM GMT  4
19837833    0400    23/10/19 04:30:10.000000000 AM GMT  1
19837833    0400    23/10/19 09:30:07.000000000 PM GMT  2
19837833    0500    23/10/19 01:08:00.000000000 AM GMT  1
19837833    0500    23/10/19 04:30:00.000000000 AM GMT  3
19840750    0500    23/10/19 12:30:00.000000000 PM GMT  1
19840750    1100    01/11/19 10:06:02.000000000 AM GMT  1
19840750    1240    02/11/19 08:40:05.000000000 AM GMT  1
19840750    1305    05/11/19 07:21:03.000000000 AM GMT  2
19840750    1305    05/11/19 08:22:03.000000000 AM GMT  1
19840750    1400    09/11/19 06:13:12.000000000 AM GMT  3

I want to apply the below sql logic on this dataframe.

select
    order_id
    , TRUNC(MAX(decode(df.code, '0400', STATUS_DATE, Null))) act_0400
    , TRUNC(MAX(decode(df.code, '0500', STATUS_DATE, Null))) act_0500
from
    dataframe df  
where 
      df.rnk =1 
group by    
    order_id

Here I am trying to create new columns act_0400 and act_0500 by taking maximum date value from the status date column for condition rank =1 and grouping them based on order id

Expected Output

ORDER_ID    ACT_0400    ACT_0500
19837715    22/10/2019  22/10/2019
19837833    23/10/2019  23/10/2019
19840750                23/10/2019

How could this be done in pandas

jezrael

You can first convert STATUS_DATE to datetimes by to_datetime with Series.dt.date, then filter by boolean indexing with Series.isin and last reshape by DataFrame.pivot_table with aggregate max, last some data cleaning by DataFrame.rename_axis, DataFrame.rename_axis and DataFrame.reset_index:

df['STATUS_DATE'] = pd.to_datetime(df['STATUS_DATE']).dt.date
df = (df[(df['RNK'] == 1) & df['CODE'].isin([400,500])]
        .pivot_table(index="ORDER_ID", columns="CODE", values="STATUS_DATE", aggfunc='max')
        .rename_axis(None, axis=1)
        .add_prefix('ACT_')
        .reset_index())
print (df)
   ORDER_ID     ACT_400     ACT_500
0  19837715  2019-10-22  2019-10-22
1  19837833  2019-10-23  2019-10-23
2  19840750         NaN  2019-10-23

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