I retrieve data from a SQLITE Database (and transform it to a pandas dataframe) in the following format:
Driver | Date loading | Date unloading | Loading Adress | Unloading Address Peter | 02.05.2020 | 03.05.2020 | 12342, Berlin | 14221, Utrecht Peter | 03.05.2020 | 04.05.2020 | 14221, Utrecht | 13222, Amsterdam Franz | 03.05.2020 | 03.05.2020 | 11111, Somewher| 11221, Somewhere2 Franz | 03.05.2020 | 05.05.2020 | 11223, Upsalla | 14231, Berlin
The date range can be specified for the query, so that it gives an overview over which driver has which transports to deliver within the specified date range, ordered by date.
The goal of the transformation I want to do is a weekly plan for each driver, with the dates from the range sorted in the available columns. So for the data above, this would look like the following:
Driver | 02.05.2020 | 03.05.2020 | 04.05.2020 | 05.05.2020 | Peter | Loading: | Unloading: Unloading: 12342, Berlin 14221, Utrecht 13222, Amsterdam Loading: 14221, Utrecht Franz | | Loading: | | Unloading: 11111, Somewher 14231, Berlin Unloading: 11221, Somewhere2 Loading: 11223, Upsalla
Is there any way to achieve the described output with dataframe operations? Within the single data columns I will need to keep the order, which is loading first, unloading second, and then go to the next data row if the date is the same.