You also can rename individual columns by using the .rename method. Pass a dictionary like the one below to the columns keyword argument:

{'old_column_name1': 'new_column_name1', 'old_column_name2': 'new_column_name2'}

Here’s an example:

df = pd.DataFrame({
    'name': ['John', 'Jane', 'Sue', 'Fred'],
    'age': [23, 29, 21, 18]
})
df.rename(columns={
    'name': 'First Name',
    'age': 'Age'},
    inplace=True)

The code above will rename name to First Name and age to Age.

Using rename with only the columns keyword will create a new DataFrame, leaving your original DataFrame unchanged. That’s why we also passed in the keyword argument inplace=True. Using inplace=True lets us edit the original DataFrame.

There are several reasons why .rename is preferable to .columns:

• You can rename just one column
• You can be specific about which column names are getting changed (with .column you can accidentally switch column names if you’re not careful)

Note: If you misspell one of the original column names, this command won’t fail. It just won’t change anything.