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February 5, 2021 11:12
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fill missing datetimes.
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| import pandas as pd | |
| rows = [{'dt' : '2020-01-01 12:00:00'}, | |
| {'dt' : '2020-01-04 13:00:00'}, | |
| {'dt' : '2020-01-08 14:00:00'}, | |
| {'dt' : '2020-01-09 15:00:00'}] | |
| dt = pd.DataFrame(rows) | |
| dt['dt'] = pd.to_datetime(dt['dt'],format='%Y-%m-%d') | |
| df = pd.DataFrame({'date' : pd.date_range(dt['dt'].min(), dt['dt'].max(),freq='D')}) | |
| df1 = pd.merge( | |
| dt.assign(date=dt['dt'].dt.normalize()), | |
| df.assign(date=df['date'].dt.normalize()), | |
| on=['date'], | |
| how='right' | |
| ).sort_values('date') | |
| df1['date'] = pd.to_datetime( | |
| df1["date"].dt.date.astype(str) + " " + df1["dt"].ffill().dt.time.astype(str) | |
| ) | |
| print(df1) | |
| """ | |
| dt date | |
| 0 2020-01-01 12:00:00 2020-01-01 12:00:00 | |
| 4 NaT 2020-01-02 12:00:00 | |
| 5 NaT 2020-01-03 12:00:00 | |
| 1 2020-01-04 13:00:00 2020-01-04 13:00:00 | |
| 6 NaT 2020-01-05 13:00:00 | |
| 7 NaT 2020-01-06 13:00:00 | |
| 8 NaT 2020-01-07 13:00:00 | |
| 2 2020-01-08 14:00:00 2020-01-08 14:00:00 | |
| 3 2020-01-09 15:00:00 2020-01-09 15:00:00 | |
| """ |
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