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Adding prefix to column labels in Pandas DataFame
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
tocTable of Contents
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To prepend a prefix to column labels, use the DataFrame's add_prefix(~) method.
Example
Consider the following DataFrame:
df = pd.DataFrame({"A":[4,5],"B":[6,7]})df
A B0 4 61 5 7
Solution
To prepend the prefix "C" to each column label of df:
df.add_prefix("C")
CA CB0 4 61 5 7
Here, add_prefix(~) returns a new DataFrame with the added prefix and so the original df is kept intact.
WARNING
If your DataFrame is memory-heavy, then add_prefix(~) is not ideal since the method allocates new memory for the returned DataFrame. To perform the same operation in-place, that is, directly modifying the original DataFrame without creating a new DataFrame, refer to the section below.
Adding a prefix in-place
Consider the same df as before:
df
A B0 4 61 5 7
Solution
To add a prefix to the column labels in-place:
df.columns = "C" + df.columnsdf
CA CB0 4 61 5 7
Explanation
Here, df.columns returns a Series like so:
df.columns
Index(['A', 'B'], dtype='object')
We then use the broadcasting technique of adding a scalar (string) and a Series:
"C" + df.columns
Index(['CA', 'CB'], dtype='object')
We then finally assign this to be our new columns:
df.columns = "C" + df.columns
Index(['CA', 'CB'], dtype='object')
Published by Isshin Inada
Edited by 0 others
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