I have a large time series data set where only changes are returned after the first row (which always has a value). Is there an efficient way in Pandas to "rehydrate" this data to have a value in every row for all columns?
| TIME | Sensor 1 | Sensor 2 | Sensor 3 | Sensor 4 |
|---|---|---|---|---|
| 3/28/2022 12:50:00 | FALSE | 95 | TRUE | 105.6 |
| 3/28/2022 12:50:01 | 107.6 | |||
| 3/28/2022 12:50:02 | 108.8 | |||
| 3/28/2022 12:50:03 | 106.8 | |||
| 3/28/2022 12:50:04 | 106.1 | |||
| 3/28/2022 12:50:05 | 106.6 | |||
| 3/28/2022 12:50:06 | 106.3 | |||
| 3/28/2022 12:50:07 | 106 | |||
| 3/28/2022 12:50:08 | ||||
| 3/28/2022 12:50:09 | 106.7 | |||
| 3/28/2022 12:50:10 | TRUE | 109.6 | ||
| 3/28/2022 12:50:11 | FALSE | 108.8 | ||
| 3/28/2022 12:50:12 | 105.4 | |||
| 3/28/2022 12:50:13 | ||||
| 3/28/2022 12:50:14 | ||||
| 3/28/2022 12:50:15 | 104.4 | |||
| 3/28/2022 12:50:16 | FALSE | 105.7 | ||
| 3/28/2022 12:50:17 | 108.2 | |||
| 3/28/2022 12:50:18 | 108.7 | |||
| 3/28/2022 12:50:19 | 106.7 |
