Tutorial: Reading and Writing .csv Files in Python

Today, we will be learning how to read and write .csv files in Python. This skill can be especially useful for data collection — say you have a series of .csv files and want to join them together, or a .txt file that you need to pull data from to place in a .csv file. Python allows you to avoid copy-and-pasting huge amounts of data in excel, or parsing through a plain-text file manually. In this tutorial, we will learn how to open a .csv file, read its contents, and add more data to it.

import csv

import csv

with open('mvp_winners.csv', 'r') as file:
  # Text goes here

Year,Name,Nationality,Club Team
 2000,Luis Figo,Portugal,Real Madrid
 2001,Michael Owen,England,Liverpool
 2002,Ronaldo,Brazil,Real Madrid
 2003,Pavel Nedved,Czech Republic,Juventus
 2004,Andriy Shevchenko,Ukraine,Milan
 2005,Ronaldinho,Brazil,Barcelona
 2006,Fabio Cannavaro,Italy,Real Madrid
 2007,Kaka,Brazil,Milan

import csv

with open('mvp_winners.csv', 'r') as file:
    mvp_winners_reader = csv.reader(file, delimiter=',')

Reading the File

The csv reader allows us to index the values. For each row, row[0] is the year, row[1] is the name, and so on. Using this indexing, we can write a script to output some information. Say we want to know the winner of the award in 2004:

import csv

with open('mvp_winners.csv', 'r') as file:
    mvp_winners_reader = csv.reader(file, delimiter=',')
    
    target_year = "2004"
    for row in mvp_winners_reader:
         if row[0] == target_year:
              print(f'The winner in {row[0]} was {row[1]}, from {row[2]}')

This script iterates through each row until it finds the year we are looking for. It will return:

The winner in 2004 was  Andriy Shevchenko, from  Ukraine

Step 3: Writing to the File

Instead of reading the file, let’s say we want to add to it. There are a few things to keep in mind as we try to do this.

(Over)writing the file

We can easily write a new .csv file using “write” mode and creating a writer object:

import csv

with open('new_file.csv', 'w') as file:
    file_writer = csv.writer(file, delimiter=',')

However, for our current csv file, this will not work. You must be very careful when using the “write” mode in Python, because it will automatically overwrite anything that is already in the file. We’ll need to find a different solution.

NOTE: the “write” mode is only for files that already exist on your computer. If you want Python to create a new file, use “x” in place of “w.”

Append Mode

Instead of writing a new file, we can add to it using “append” mode. We simply replace the “w” with an “a”:

import csv

with open('mvp_winners.csv', 'a') as file:
    mvp_winners_writer = csv.writer(file, delimiter=',')

Once we are in append mode, we can use the already-built-in indices to easily create a new entry:

import csv

with open('mvp_winners.csv', 'a') as file:
    mvp_winners_writer = csv.writer(file, delimiter=',')
    
    mvp_winners_writer.writerow(["2008", "Cristiano Ronaldo", "Portugal", "Manchester United"])

And sure enough, it appears in our file:

...
2005,Ronaldinho,Brazil,Barcelona
2006,Fabio Cannavaro,Italy,Real Madrid
2007,Kaka,Brazil,Milan
2008,Cristiano Ronaldo,Portugal,Manchester United

Step 4: Putting it All Together

Let’s say we want to make a new .csv file, with only Brazilian winners of the award. We can do so using the tools that we have learned:

import csv

with open('mvp_winners.csv', 'r') as read_file, open('brazilian_winners.csv', 'x') as write_file:
	mvp_winners_reader = csv.reader(read_file, delimiter=',')
	brazilian_winners_writer = csv.writer(write_file, delimiter=',')
	
	for row in mvp_winners_reader:
		if row[2] == "Brazil":
			brazilian_winners_writer.writerow(row)

Here’s our result:

Year,Name,Nationality,Club Team
2002,Ronaldo,Brazil,Real Madrid
2005,Ronaldinho,Brazil,Barcelona
2007,Kaka,Brazil,Milan

Conclusion

This tutorial has outlined the very basics of reading and writing .csv files using Python’s built-in libraries. Hopefully, it will give you a good idea of the syntax and functionality, so that you can tailor the functions to your own needs! I encourage you to check out the Documentation for the csv library, as well as pandas, which allows for more advanced manipulation of csv files. Thank you for reading!