I have a dataframe that has many rows per combination of the 'PROGRAM', 'VERSION' and 'RELEASE_DATE' columns. I want to get a dataframe with all of the combinations of just those three columns. Would this be a job for groupby or distinct?
thx
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rchitect-of-info
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Since you are not aggregating anything, use unique
df.select(['PROGRAM','VERSION','RELEASE_DATE']).unique()
If you are not using the Lazy functionality of Polars, this can also be written as:
df[['PROGRAM','VERSION','RELEASE_DATE']].unique()
Anton Daneyko
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Can the select version posted above be iterated on? – rchitect-of-info Mar 08 '22 at 12:27
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`for prog, vers, rel in df.select(['PROGRAM','VERSION','RELEASE_DATE']).distinct().rows(): ...` – Mar 08 '22 at 19:22
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1`distinct()` has been deprecated, you should use `unique()` instead – magomar Dec 07 '22 at 09:31