CODEWITHSUNDEEP
pythonpandas
dermen
dermen

Reputation: 5372

What to do when pandas column renaming creates column name duplicates

Why doesn't a pandas.DataFrame object complain when I rename a column if the new column name already exists?

This makes referencing the new column in the future return a pandas.DataFrame as opposed to a pandas.Series , which can cause further errors.

Secondly, is there a suggested way to handle such a situation?

Example:

import pandas

df = pd.DataFrame( {'A' : ['foo','bar'] ,'B'  : ['bar','foo'] } )
df.B.map( {'bar':'foo','foo':'bar'} )
# 0    foo
# 1    bar
# Name: B, dtype: object

df.rename(columns={'A':'B'},inplace=True)

Now, the following will fail:

df.B.map( {'bar':'foo','foo':'bar'} )
#AttributeError: 'DataFrame' object has no attribute 'map'

Upvotes: 0

Views: 7182

Answers (1)

Alexander
Alexander

Reputation: 109636

Let's say you had a dictionary mapping old columns to new column names. When renaming your DataFrame, you could use a dictionary comprehension to test if the new value v is already in the DataFrame:

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
d = {'a': 'B', 'b': 'B'}
df.rename(columns={k: v for k, v in d.iteritems() if v not in df}, inplace=True)
>>> df
    a   B
0   1   3
1   2   4

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
d = {'a': 'b'}
df.rename(columns={k: v for k, v in d.iteritems() if v not in df}, inplace=True)
>>> df
    a   b
0   1   3
1   2   4

Upvotes: 3

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