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  1. import pandas as pd
  2. df = pd.DataFrame(
  3.         [['A', '2020-02-02 20:31:00'],
  4.         ['A', '2020-02-03 00:52:00'],
  5.         ['A', '2020-02-07 23:45:00'],
  6.         ['A', '2020-02-08 13:19:00'],
  7.         ['A', '2020-02-18 13:16:00'],
  8.         ['A', '2020-02-27 12:16:00'],
  9.         ['A', '2020-02-28 12:16:00'],
  10.         ['B', '2020-02-07 18:57:00'],
  11.         ['B', '2020-02-07 21:50:00'],
  12.         ['B', '2020-02-12 19:03:00'],
  13.         ['C', '2020-02-01 13:50:00'],
  14.         ['C', '2020-02-11 15:50:00'],
  15.         ['C', '2020-02-21 10:50:00']],
  16.         columns = ['ID', 'Date'])
  17.  
  18. df['Date'] = pd.to_datetime(df['Date'])
  19.  
  20. delta = 7
  21.  
  22. print(df.set_index('Date')
  23.    .assign(count_last=1)
  24.    .groupby('ID')
  25.    .rolling(f'{delta}D')
  26.    .sum() - 1
  27. )
Success #stdin #stdout 0.56s 62116KB
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Standard input is empty
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                        count_last
ID Date                           
A  2020-02-02 20:31:00         0.0
   2020-02-03 00:52:00         1.0
   2020-02-07 23:45:00         2.0
   2020-02-08 13:19:00         3.0
   2020-02-18 13:16:00         0.0
   2020-02-27 12:16:00         0.0
   2020-02-28 12:16:00         1.0
B  2020-02-07 18:57:00         0.0
   2020-02-07 21:50:00         1.0
   2020-02-12 19:03:00         2.0
C  2020-02-01 13:50:00         0.0
   2020-02-11 15:50:00         0.0
   2020-02-21 10:50:00         0.0
https://ideone.com/PC545o
language:
Python 3 (python  3.9.5)
created:
2 years ago
visibility:
secret

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