Considering a simple df:
HeaderA | HeaderB | HeaderC
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Is there a way to rename all columns, for example to add to all columns an "X" in the end?
HeaderAX | HeaderBX | HeaderCX
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I am concatenating multiple dataframes and want to easily differentiate the columns dependent on which dataset they came from.
Or is this the only way?
df.rename(columns={'HeaderA': 'HeaderAX'}, inplace=True)
I have over 50 column headers and ten files; so the above approach will take a long time.
Thank You
df.add_suffix('X')
HeaderAX HeaderBX HeaderCX
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And the sister method
pd.DataFrame.add_prefix
df.add_prefix('X')
XHeaderA XHeaderB XHeaderC
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You can also use the pd.DataFrame.rename
method and pass a function. To accomplish the same thing:
df.rename(columns='{}X'.format)
HeaderAX HeaderBX HeaderCX
0 476 4365 457
In this example, '{}X'.format
is a function that takes a single argument and appends an 'X'
The advantage of this method is that you can use inplace=True
if you chose to.
From SO post
. Let's try using a lambda function in rename:
df.rename(columns = lambda x: x+'X', inplace = True)
df.columns = [column + 'X' for column in df.columns]
df.columns = list(map(lambda s: s+'X', df.columns))
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