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Getting multiple columns in Pandas DataFrame

schedule Aug 11, 2023
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To get multiple columns in Pandas, use either:

  • the [] syntax directly

  • use properties like loc and iloc

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,4], "B":[5,6], "C":[7,8]})
df
A B C
0 3 5 7
1 4 6 8

Using square-brackets directly

To get columns A and B as a DataFrame:

cols = df[["A","B"]]
cols
A B
0 3 5
1 4 6

Since a copy is returned here, modifying cols would not mutate the original df, as demonstrated by:

cols.iat[0,0] = 20
df
A B C
0 3 5 7
1 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 = df.loc[:,["A","C"]]
cols
A C
0 3 7
1 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:

df.iloc[:,[1,2]]
B C
0 5 7
1 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.

robocat
Published by Isshin Inada
Edited by 0 others
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