Explicit is better than implicit. df[boolean_mask] selects rows where boolean_mask is True, but there is a corner case when you might not want it to: when df has boolean-valued column labels: In [229]: df = pd.DataFrame({True:[1,2,3],False:[3,4,5]}); df Out[229]: False True 0 3 1 1 4 2 2 5 3 You might want to use df[[True]] … Read more
There is a df.isin(values) method wich tests whether each element in the DataFrame is contained in values. So, as @MaxU wrote in the comment, you can use df.loc[df[‘channel’].isin([‘sale’,’fullprice’])] to filter one column by multiple values.
Explicit is better than implicit. df[boolean_mask] selects rows where boolean_mask is True, but there is a corner case when you might not want it to: when df has boolean-valued column labels: In [229]: df = pd.DataFrame({True:[1,2,3],False:[3,4,5]}); df Out[229]: False True 0 3 1 1 4 2 2 5 3 You might want to use df[[True]] … Read more
Label vs. Location The main distinction between the two methods is: loc gets rows (and/or columns) with particular labels. iloc gets rows (and/or columns) at integer locations. To demonstrate, consider a series s of characters with a non-monotonic integer index: >>> s = pd.Series(list(“abcdef”), index=[49, 48, 47, 0, 1, 2]) 49 a 48 b 47 … Read more