How to rename columns in Pandas DataFrame?

In this article, we will study how to rename columns in Pandas DataFrame using Python. Let’s understand first what is Pandas and DataFrame.

Pandas is an opensource library that is provided by Python. Pandas perform data manipulation and data analysis.

DataFrame is a two-dimensional Data Structure. Data in DataFrame is aligned in a tabular fashion in rows and columns. Hence, DataFrame is used to store data.

Rename Columns in Pandas DataFrame

Step 1: Import Pandas

Importing Pandas is the first step for using DataFrame. Following is the code:

import pandas as pd

Step 2: Create DataFrame

Since we are learning how to rename columns of DataFrame, we need to create DataFrame.

details = {'Name' : ['Rani','Teju','Bhushan','Roshan'],
           'Age'   : [29,26,34,67],
           'Salary' : [23000,67000,80000,56000],
           'Designation' : ['C.A','Accountant','Data Scientist','Data Analyst']}

df = pd.DataFrame(details)

print(df)

OUTPUT

   Name     Age    Salary      Designation
0  Rani     29     23000       C.A
1  Teju     26     67000       Accountant
2  Bhushan  34     80000       Data Scientist
3  Roshan   67     56000       Data Analyst

Different Techniques used to rename columns of DataFrame:

(i) DataFrame.rename()

rename() is the method of pandas.DataFrame. It changes the name of rows and columns individually. It takes Dataframe name. The “index” renames rows and “column” renames columns. If we want to change the name of column, following is the code:

df.rename(columns = {'Name':'NAME'})

OUTPUT

    NAME       Age      Salary      Designation
0   Rani       29       23000       C.A
1   Teju       26       67000       Accountant
2   Bhushan    34       80000       Data Scientist
3   Roshan     67       56000       Data Analyst

rename() can also take more than one column. Let’s look at following code.

df.rename(columns = {'Age':'AGE','Salary':'SALARY'})

OUTPUT

    NAME       AGE    SALARY      Designation
0   Rani       29     23000       C.A
1   Teju       26     67000       Accountant
2   Bhushan    34     80000       Data Scientist
3   Roshan     67     56000       Data Analyst

(ii) By passing list of columns

In this method, we will pass the new column names into list. This list would be assigned to the column attribute of DataFrame. Let’s look at the following code:

df.columns = ['Name','Age','Income','Occupation']
print(df.columns)

OUTPUT

Index(['Name', 'Age', 'Income', 'Occupation'], dtype='object')

Let’s print the dataframe with new column names:

print(df)

OUTPUT:

  Name      Age    Income      Occupation
0 Rani      29     23000       C.A
1 Teju      26     67000       Accountant
2 Bhushan   34     80000       Data Scientist
3 Roshan    67     56000       Data Analyst

Let us now try to update any one column name instead of all columns. Look at the following code:

df.columns = ['Income']

If we run the above code, it will raise an ValueError. It says :

ValueError: Length mismatch: Expected axis has 4 elements, new values have 1 elements

Hence, this method has one drawback. It will accept all columns even if we want to update few columns.

(iii) Using axis = 1

DataFrame is a two dimensional Data Structure. It has rows and columns. axis = 1 indicates column and axis = 0 indicates row. If we want to update column name, it is done using axis = 1. Let’s look at the following code:

df.rename({'Income':'Salary','Occupation':'Designation'},axis = 1)

OUTPUT

    Name     Age     Salary     Designation 
0   Rani     29      23000      C.A 
1   Teju     26      67000      Accountant 
2   Bhushan  34      80000      Data Scientist 
3   Roshan   67      56000      Data Analyst

(iv) Using axis = “columns”

Updating name of the column can also be done by setting axis parameter to “columns”. This can be done using axis = “columns”. Let’s look at the following code:

df.rename({'Salary':'Payment'},axis = "columns")

OUTPUT

  Name    Age   Payment   Designation 
0 Rani    29    23000     C.A 
1 Teju    26    67000     Accountant 
2 Bhushan 34    80000     Data Scientist 
3 Roshan  67    56000     Data Analyst

Thank You.

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