Changing a specific column name in pandas DataFrame

Rename by column index: a one-liner stunt

What if you can do more with less? Here's a shortcut to rename a column by its index:

df.columns[2] = "new_name" # changes name of the 3rd column, keep counting!
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Be vigilant though: Remembering index numbers might trick you, especially when the column order changes.

Unpacking inplace and copy parameters

In pandas speak, inplace=True in rename causes the DataFrame to adopt the renaming immediately, requiring no new variable assignment. Pairing this with copy=False modifies the DataFrame right in its memory location, saving on system space. Consider this when you've a Titanic-sized data!

The manipulation toolbox: renaming multiple columns and indexes

When you've multiple columns to rename, use dictionary just like dealing with a single column:

df.rename(columns={'A': 'Alpha', 'B': 'Beta'}, inplace=True)
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And yes, this extends to indexes too:

df.rename(index={0: 'first', 1: 'second'}, inplace=True) # Who needs boring numbers when you can go 'first', 'second'!
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Not axis of evil: using axis parameter

Good coding practices often imply readability. Express your intent more clearly by specifying axis parameter:

df.rename(columns={'A': 'B'}, axis='columns', inplace=True) # # Having axis, smooth sailing!
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axis=1 corresponds to axis='columns', making your inner intention loud and clear.

Troubleshooter: dealing with errors and conflicts

Pandas is sweet, but it has its quirks. You can rename an absent column, still no error raised! Ensure the column exists to avoid silent failures. Also, renaming to an existing column name triggers a column name conflict.

Flex your muscles: rename with function

For the advanced user, rename supports function input, and this opens up a world of possibilities:

df.rename(columns=str.lower, inplace=True) # All lowercase, because shouting is just rude!
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This command lowers all column names. A string parameter function of your choice can be the new puppet master.

Method chaining for knights of the DataFrame

For aesthetically pleasing code, chain methods. Say goodbye to inplace=True, welcome neat transformations:

df = (df.rename(columns={'A': 'Alpha'}) # Started as 'A', sworn in as 'Alpha'
         .drop('B', axis=1)) # Adios 'B'!
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Vital stats: memory considerations

But beware warriors, inplace=True is deceptive. It might not spare memory as you expect. Possible deceit! Ensure to check memory usage in memory-critical scenarios.