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Removing duplicate columns in Pandas DataFrame

schedule Aug 12, 2023
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Dropping columns with the same label

Consider the following DataFrame:

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

Here, we had to use the rename(~) method since the DataFrame(~) constructor automatically eliminates columns with duplicate labels.

Keeping first occurrence of duplicates

To drop columns with duplicate labels except the first occurrence, use duplicated(~) like so:

df.loc[:, ~df.columns.duplicated()]
A B
0 3 5
1 4 6

Explanation

Here, we first fetch the column labels (Index object) using the columns property. We then call duplicated(~), which returns a NumPy array of booleans where True indicates the presence of a duplicate label:

df.columns.duplicated()
array([False, False, True])

By default, keep="first" for duplicated(~) and so the first occurrence of a non-unique value will be marked as False.

Dropping duplicate columns is equivalent to keeping all non-duplicate columns, and so we invert the booleans using ~:

~df.columns.duplicated()
array([ True, True, False])

Finally, we pass this boolean mask into loc to get the columns that correspond to True in the mask:

df.loc[:, ~df.columns.duplicated()]
A B
0 3 5
1 4 6

Here, the : before the comma indicates that we want to fetch all the rows.

Dropping all occurrences of duplicates

Consider the same df as above:

df
A B A
0 3 5 7
1 4 6 8

To drop all occurrences of duplicates:

df.loc[:, ~df.columns.duplicated(keep=False)]
B
0 5
1 6

Explanation

The only difference between this and the previous case is that we set the parameter keep=False, and so duplicated(~) here returns True for all occurrences of non-unique values (as opposed to the default behaviour of returning False for the first occurrence):

df.columns.duplicated(keep=False)
array([ True, False, True])

Dropping columns with same values

Consider the following DataFrame:

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

Here, columns A and C contain the same values.

To drop duplicate columns:

df.T.drop_duplicates().T
A B
0 3 5
1 4 6

By default, keep="first" for drop_duplicates(~), which means that the first occurrence of the duplicates (column A) is kept. To remove all occurrences instead, set keep=False.

Explanation

There is no direct way of removing duplicate columns, but Pandas does offer the method drop_duplicates(), which removes duplicate rows. Therefore, we take the transpose of df using the T property:

df.T
0 1
A 3 4
B 5 6
C 3 4

We then call drop_duplicates() to remove the duplicate rows:

df.T.drop_duplicates()
0 1
A 3 4
B 5 6

Finally, we take the transpose again to get back the original shape.

robocat
Published by Isshin Inada
Edited by 0 others
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