How do you rename a column index in python?

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Prerequisites: Pandas

A column of a data frame can be changed using the position it is in known as its index. Just by the use of index a column can be renamed. This article discusses all such possible methods. 

Approach:

• Import required python library.
• Create data
• Provide index of the column to be renamed as argument to rename() function.

Pandas rename() method is used to rename any index, column or row.

Syntax: rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None)

Parameters:

• mapper, index and columns: Dictionary value, key refers to the old name and value refers to new name. Only one of these parameters can be used at once.
• axis: int or string value, 0/’row’ for Rows and 1/’columns’ for Columns
• copy: Copies underlying data if True.
• inplace: Makes changes in original Data Frame if True.
• level: Used to specify level in case data frame is having multiple level index.

Return Type: Data frame with new names

Given below are various implementation to achieve our required functionality:

Example 1: Switching both column names with each other using column index.

Python3

import pandas as pd

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})

df.columns.values[0] = "b"

df.columns.values[1] = "a"

display(df)

Output:

Example 2: Using another method to rename the column with index.

Python3

import pandas as pd

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})

su = df.rename(columns={df.columns[1]: 'new'})

display(su)

Output:

Example 3: Renaming two/more columns in a single command using an index number.

Python3

import pandas as pd

df = pd.DataFrame({'a': [1, 2], 'b': [3, 4], 'c': [7, 8]})

mapping = {df.columns[0]: 'new0', df.columns[1]: 'new1'}

su = df.rename(columns=mapping)

display(su)

Output:

Example 4: Renaming column name with an index number of the CSV file.

File in use: Data1.csv

Link: Clickhere

Python3

import pandas as pd

df1 = pd.read_csv("data1.csv")

df1.columns.values[2] = "city"

display(df1)

Output: