Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- comp1 = pd.date_range('1/31/2016', periods=8, freq='3M')
- comp2 = pd.date_range('1/31/2016', periods=8, freq='Q')
- quarters = pd.DataFrame([1] * 8 + [2] * 8, index=comp1.append(comp2), columns=['company'])
- values = np.transpose([np.arange(1, 25), np.arange(1, 25) * 11])
- dates = ['2016-01-14', '2016-02-03', '2016-03-15', '2016-04-04',
- '2016-05-30', '2016-06-11', '2016-07-18', '2016-08-08',
- '2016-09-09', '2016-10-10', '2016-11-01', '2016-12-24',
- '2017-01-30', '2017-02-19', '2017-03-13', '2017-04-24',
- '2017-05-31', '2017-06-02', '2017-07-28', '2017-08-23',
- '2017-09-04', '2017-10-30', '2017-11-11', '2017-12-06']
- df = pd.DataFrame(values, index=pd.DatetimeIndex(dates), columns=['A', 'B'])
- A B
- 2016-01-14 1 11
- 2016-02-03 2 22
- 2016-03-15 3 33
- 2016-04-04 4 44
- 2016-05-30 5 55
- 2016-06-11 6 66
- 2016-07-18 7 77
- 2016-08-08 8 88
- 2016-09-09 9 99
- 2016-10-10 10 110
- 2016-11-01 11 121
- 2016-12-24 12 132
- 2017-01-30 13 143
- 2017-02-19 14 154
- 2017-03-13 15 165
- 2017-04-24 16 176
- 2017-05-31 17 187
- 2017-06-02 18 198
- 2017-07-28 19 209
- 2017-08-23 20 220
- 2017-09-04 21 231
- 2017-10-30 22 242
- 2017-11-11 23 253
- 2017-12-06 24 264
- company A B
- 2016-01-31 1 1 11
- 2016-04-30 1 3 33
- 2016-07-31 1 6 66
- 2016-10-31 1 9 99
- 2017-01-31 1 12 132
- 2017-04-30 1 15 165
- 2017-07-31 1 18 198
- 2017-10-31 1 21 231
- 2016-03-31 2 2 22
- 2016-06-30 2 5 55
- 2016-09-30 2 8 88
- 2016-12-31 2 11 121
- 2017-03-31 2 14 154
- 2017-06-30 2 17 187
- 2017-09-30 2 20 220
- 2017-12-31 2 23 253
Add Comment
Please, Sign In to add comment