You could simply use groupby in this case, which will create the multi-index automatically when it sums the sales along the requested columns. df.groupby([‘user_id’, ‘account_num’, ‘dates’]).sales.sum().to_frame() You should also be able to simply do this: df.set_index([‘user_id’, ‘account_num’, ‘dates’]) Although you probably want to avoid any duplicates (e.g. two or more rows with identical user_id, account_num … Read more
Re-indexing means to read the data, delete the data in elasticsearch and ingest the data again. There is no such thing like “change the mapping of existing data in place.” All the re-indexing tools you mentioned are just wrappers around read->delete->ingest. You can always adjust the mapping for new indices and add fields later. All … Read more
You can use reindex of index of Series created by len with sort_values: print (df.name.str.len()) 0 5 1 2 2 6 3 4 Name: name, dtype: int64 print (df.name.str.len().sort_values()) 1 2 3 4 0 5 2 6 Name: name, dtype: int64 s = df.name.str.len().sort_values().index print (s) Int64Index([1, 3, 0, 2], dtype=”int64″) print (df.reindex(s)) name score … Read more
If you call array_values on your array, it will be reindexed from zero.
To reset the keys of all arrays in an array: $arr = array_map(‘array_values’, $arr); In case you just want to reset first-level array keys, use array_values() without array_map.