Replacing column value of a CSV file in Python

Last Updated : 02 Sep, 2020

Let us see how we can replace the column value of a CSV file in Python. CSV file is nothing but a comma-delimited file.

Method 1: Using Native Python way

Using replace() method, we can replace easily a text into another text. In the below code, let us have an input CSV file as "csvfile.csv" and be opened in "read" mode. The join() method takes all lines of a CSV file in an iterable and joins them into one string. Then, we can use replace() method on the entire string and can perform single/multiple replacements. In the entire string, the given text is searched and replaced with the specified text.

Example:

The input file will be:

Python3

# reading the CSV file
text = open("csvfile.csv", "r")

#join() method combines all contents of 
# csvfile.csv and formed as a string
text = ''.join([i for i in text]) 

# search and replace the contents
text = text.replace("EmployeeName", "EmpName") 
text = text.replace("EmployeeNumber", "EmpNumber") 
text = text.replace("EmployeeDepartment", "EmpDepartment") 
text = text.replace("lined", "linked") 

# output.csv is the output file opened in write mode
x = open("output.csv","w")

# all the replaced text is written in the output.csv file
x.writelines(text)
x.close()

Output:

Method 2: Using Pandas DataFrame

We can read the CSV file as a DataFrame and then apply the replace() method.

Python3

# importing the module
import pandas as pd 
  
# making data frame from the csv file 
dataframe = pd.read_csv("csvfile1.csv") 
  
# using the replace() method
dataframe.replace(to_replace ="Fashion", 
                 value = "Fashion industry", 
                  inplace = True)
dataframe.replace(to_replace ="Food", 
                 value = "Food Industry", 
                  inplace = True)
dataframe.replace(to_replace ="IT", 
                 value = "IT Industry", 
                  inplace = True)

# writing  the dataframe to another csv file
dataframe.to_csv('outputfile.csv', 
                 index = False)