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 d = {'ID': ['H1', 'H1', 'H2', 'H2', 'H3', 'H3'], 'Year': ['2012', '2013', '2014', '2013', '2014', '2015'], 'Unit': [5, 10, 15, 7, 15, 20]} df_input= pd.DataFrame(data=d) df_input
  • Group By the above df_input and wanted to get 'lag' and 'lag_u' columns. 'lag' is the number of row sequence at 'ID' and 'Year' group by level.
  • 'lag_u' is just get the first Unit value at 'ID' and 'Year' group by level.

Expected Output:

d = {'ID': ['H1', 'H1', 'H2', 'H2', 'H3', 'H3'], 'Year': ['2012', '2013', '2014', '2013', '2014', '2015'], 'Unit': [5, 10, 15, 7, 15, 20], 'lag': [0, 1, 2, 0, 1, 2], 'lag_u': [5, 5, 5, 7, 7, 7]} df_output= pd.DataFrame(data=d) df_output
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    Please explain the logic for new columns properly. Commented Mar 30, 2021 at 5:47
  • I think problem here is groups from input data not match in ouput data. Commented Mar 30, 2021 at 5:50

1 Answer 1

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IIUC need GroupBy.cumcount with GroupBy.transform and GroupBy.first:

g = df_input.groupby('ID') #if need group by both columns #g = df_input.groupby(['ID','Year']) df_input['lag'] = g.cumcount() df_input['lag_u'] = g['Unit'].transform('first')
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