I have a pandas dataframe with data like: +-----------+-----------------+---------+ | JOB-NAME | Status | SLA | +-----------+-----------------+---------+ | job_1 | YET_TO_START | --- | | job_3 | COMPLETED | MET | | job_4 | RUNNING | MET | | job_2 | YET_TO_START | LATE | | job_6 | RUNNING | LATE | | job_5 | FAILED | LATE | | job_7 | YET_TO_START | --- | | job_8 | COMPLETED | NOT_MET | +-----------+-----------------+---------+ I need to sort this table based on the Status and SLA states, like for Status: FAILED will be top on the table, then YET_TO_START, then RUNNING, and finally COMPLETED. Similarly for SLA the order will be LATE, ---, NOT_MET, and MET. Like this: +-----------+-----------------+---------+ | JOB-NAME | Status | SLA | +-----------+-----------------+---------+ | job_5 | FAILED | LATE | | job_2 | YET_TO_START | LATE | | job_1 | YET_TO_START | --- | | job_7 | YET_TO_START | --- | | job_6 | RUNNING | LATE | | job_4 | RUNNING | MET | | job_8 | COMPLETED | NOT_MET | | job_3 | COMPLETED | MET | +-----------+-----------------+---------+ I am able to do this custom sorting priority-based only on single column Status, but unable to do for multiple columns. sort_order_dict = {"FAILED":0, "YET_TO_START":1, "RUNNING":2, "COMPLETED":3} joined_df = joined_df.sort_values(by=['status'], key=lambda x: x.map(sort_order_dict)) A solution is given here, but its for single column, not multiple column. Continue reading...
