perform pandas aggregation whiles keeping the date column intact

Question

user = {\'id\':[\'abab23\', \'abab21\', \'abab22\', \'abab25\', \'abab24\', \'abab30\', \'abab252\', \'abab15\'],
        \'dob\':[\'10-10-1990\',\'1-12-1993\', \'12-12-         


        

Answer 1

Use:

#seelct only necessary columns
activities = activities[['sentconn','receiveconj','sentdate','receivedDate']]

#set new columns names
activities.columns = ['sent_id','receive_id','sent_date','receive_date']

#ssplit columns names by _ to MultiIndex
activities.columns = activities.columns.str.split('_', expand=True)

#reshape DataFrame and filter by is with id in inner merge
activities = (activities.stack(0)
                        .rename_axis([None, 'type'])
                        .reset_index(level=1)
                        .merge(user['id']))
print (activities)
      type        date       id
0  receive   2-10-2020   abab24
1  receive   2-10-2020   abab24
2     sent   2-10-2020   abab15
3     sent  11-10-2020   abab15
4  receive   4-10-2020   abab21
5     sent   4-10-2020   abab25
6     sent   5-10-2020   abab23
7  receive  10-10-2020  abab252
8     sent  10-10-2020   abab22
9  receive  11-10-2020   abab30

---

#get counts by crosstab
df = pd.crosstab([activities['date'], activities['id']], activities['type'])
print (df)
type                receive  sent
date       id                    
10-10-2020 abab22         0     1
           abab252        1     0
11-10-2020 abab15         0     1
           abab30         1     0
2-10-2020  abab15         0     1
           abab24         2     0
4-10-2020  abab21         1     0
           abab25         0     1
5-10-2020  abab23         0     1