Add Suffix to Each Column Name in Pandas DataFrame

You may use add_suffix in order to add a suffix to each column name in Pandas DataFrame:

df = df.add_suffix('your suffix')

In the next section, you’ll see the steps to apply the above syntax in practice.

Steps to Add Suffix to Each Column Name in Pandas DataFrame

Step 1: Create a DataFrame

To start with a simple example, let’s say that you have the following dataset that contains 3 columns:

Product	Price	Discount
ABC	750	Yes
DDD	430	No
XYZ	980	No
AAA	250	Yes
CCC	620	No

You can then create a DataFrame as follows:

import pandas as pd

data = {'Product': ['ABC','DDD','XYZ','AAA','CCC'],
          'Price': [750,430,980,250,620],
       'Discount': ['Yes','No','No','Yes','No']
        }

df = pd.DataFrame(data, columns = ['Product','Price','Discount'])
print (df)

Once you run the code in Python, you’ll get the following DataFrame:

  Product  Price  Discount
0     ABC    750       Yes
1     DDD    430        No
2     XYZ    980        No
3     AAA    250       Yes
4     CCC    620        No

Step 2: Add Suffix to Each Column Name in Pandas DataFrame

Let’s suppose that you’d like to add a suffix to each column name in the above DataFrame.

For example, let’s say that you want to add the suffix of ‘_Sold‘ at the end of each column name.

In that case, you’ll need to apply this syntax in order to add the suffix:

df = df.add_suffix('_Sold')

So for our example, the complete Python code would look as follows:

import pandas as pd

data = {'Product': ['ABC','DDD','XYZ','AAA','CCC'],
          'Price': [750,430,980,250,620],
       'Discount': ['Yes','No','No','Yes','No']
        }

df = pd.DataFrame(data, columns = ['Product','Price','Discount'])
df = df.add_suffix('_Sold')
print (df)

As you can see, the suffix of ‘_Sold‘ is now added to each column in the DataFrame:

  Product_Sold    Price_Sold   Discount_Sold
0         ABC           750             Yes
1         DDD           430              No
2         XYZ           980              No
3         AAA           250             Yes
4         CCC           620              No

But what if you want to add a suffix to a single column (or a subset of columns) in the DataFrame?

In such a case, you may consider to rename the column/s instead.

For instance, let’s say that you want to add the suffix of ‘_Sold’ to the ‘Price’ column only. You can therefore apply the following code in Python to rename the Price column:

import pandas as pd

data = {'Product': ['ABC','DDD','XYZ','AAA','CCC'],
          'Price': [750,430,980,250,620],
       'Discount': ['Yes','No','No','Yes','No']
        }

df = pd.DataFrame(data, columns = ['Product','Price','Discount'])
df = df.rename(columns = {'Price':'Price_Sold'})
print (df)

You’ll now see that the suffix of ‘_Sold‘ is applied to the Price column only:

  Product   Price_Sold   Discount
0     ABC          750        Yes
1     DDD          430         No
2     XYZ          980         No
3     AAA          250        Yes
4     CCC          620         No

You can check the Pandas Documentation to learn more about add_suffix.