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Getting multiple columns in Pandas DataFrame
schedule Aug 11, 2023
Last updated local_offer
Tags Python●Pandas
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To get multiple columns in Pandas, use either:
Consider the following DataFrame:
df
A B C0 3 5 71 4 6 8
Using square-brackets directly
To get columns A and B as a DataFrame:
cols = df[["A","B"]]cols
A B0 3 51 4 6
Since a copy is returned here, modifying cols would not mutate the original df, as demonstrated by:
df
A B C0 3 5 71 4 6 8
Here, we're using the iat property to modify the top-left entry of cols.
Getting multiple columns using labels
To get multiple columns using their column labels, use loc like so:
cols
A C0 3 71 4 8
The : before the comma indicates that we want to fetch all the rows. Similar to the [] case, a copy is returned here, so modifying this will not mutate the original df.
Getting multiple columns using integer index
To get multiple columns using integer indices, use iloc like so:
B C0 5 71 6 8
The : before the comma indicates that we want to fetch all the rows. Again, a copy of the data is returned here, so modifying this will not mutate the original df.
Related
Pandas DataFrame | loc property
Access and update values of the DataFrame using row and column labels.
Pandas DataFrame | iloc property
Access and update specific rows/columns of the DataFrame using integer indices.
Published by Isshin Inada
Edited by 0 others
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