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The easiest way to convert a tab-delimited values (TSV) file to a comma-separated values (CSV) file is to use the following three lines of code:

1. import pandas as pd
2. df = pd.read_csv('my_file.txt', sep='\t', header=None)
3. df.to_csv('my_file.csv', header=None)

We’ll explain this and other approaches in more detail next—scroll down to Method 3 for this exact method.

Problem Formulation

Given a tab-delimited file with one tab character '\t' between two values in a given column.

Input: 'my_file.tsv'

Alice	DataScience	$100000

Bob	Programmer	$90000

Carl	Manager	$122000

Dave	Freelancer	$144000

How to convert the tab-delimited values (TSV) to a comma-separated values (CSV) file?

Output: 'my_file.csv'

0,Alice,DataScience,$100000
1,Bob,Programmer,$90000
2,Carl,Manager,$122000
3,Dave,Freelancer,$144000

We’ll also look at slight variations of this problem. Let’s go!

Method 1: String Replace Single Tab

The most straightforward way to convert a tab-delimited (TSV) to a comma-separated (CSV) file in Python is to replace each tabular character '\t' with a comma ',' character using the string.replace() method. This works if two values are separated by exactly one tabular character.

Here’s an example input file 'my_file.tsv':

Here’s an example of some code to convert the tab-delimited file to the CSV file:

with open('my_file.tsv') as f: # Read space-delimited file and replace all empty spaces by commas data = f.read().replace('\t', ',') # Write the CSV data in the output file print(data, file=open('my_file.csv', 'w'))

Output file 'my_file.csv':

If you have any doubts, feel free to dive into our related tutorials:

• Python String Replace Method
• Python open() Function
• Python print() to File
• Python print() Function
• Python Context Managers

Method 2: Regex Replace Arbitrary Tabs

To replace one '\t' or more tabs '\t\t\t' between two column values with a comma ',' and obtain a CSV, use the regular expressions operation re.sub('[\t]+', ',', data) on the space-separated data.

If you have any doubts, feel free to dive into our related tutorials:

• Python Regex re.sub()
• Python Regex [] Character Class
• Python Regex + Quantifier
• Python Regex Superpower

Here’s an example input file 'my_file.tsv', notice the additional tabular characters that may separate two column values:

Here’s an example of some code to convert the TSV to the CSV file:

import re with open('my_file.txt') as infile: # Read space-delimited file and replace all empty spaces by commas data = re.sub('[ ]+', ',', infile.read()) # Write the CSV data in the output file print(data, file=open('my_file.csv', 'w'))

Output file 'my_file.csv':

Method 3: Pandas read_csv() and to_csv()

To convert a tab-delimited file to a CSV, first read the file into a Pandas DataFrame using pd.read_csv(filename, sep='\t+', header=None) and then write the DataFrame to a file using df.to_csv(outfilename, header=None).

Here’s an example input file 'my_file.tsv':

Here’s an example of some code to convert the tab-delimited file to the CSV file:

import pandas as pd # Read space-delimited file

df = pd.read_csv('my_file.tsv', sep='\t+', header=None) # Write DataFrame to file

df.to_csv('my_file.csv', header=None)

Output file 'my_file.csv':

You can also use the simpler sep='\t' if you are sure that only a single tabular character separates two column values.

If you have any doubts, feel free to dive into our related tutorials:

• Pandas read_csv()
• Pandas to_csv()

Summary

We examined three great ways to convert a space-delimited to a comma-separated CSV file:

• Method 1: String Replace Single Tab
• Method 2: Regex Replace Arbitrary Tabs
• Method 3: Pandas read_csv() and to_csv()

Thanks for taking the time to read this article, my friend!

Regex Humor

Problem: How to convert a list of dictionaries to a csv file?

Example: Given is a list of dicts—for example salary data of employees in a given company:

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}]

Your goal is to write the content of the list of dicts into a comma-separated-values (CSV) file format. Your out file should look like this:

my_file.csv:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Ann,Manager,119000

Solution: There are four simple ways to convert a list of dicts to a CSV file in Python.

1. Pandas: Import the pandas library, create a Pandas DataFrame, and write the DataFrame to a file using the DataFrame method DataFrame.to_csv('my_file.csv').
2. CSV: Import the csv module in Python, create a CSV DictWriter object, and write the list of dicts to the file in using the writerows() method on the writer object.
3. Python: Use a pure Python implementation that doesn’t require any library by using the Python file I/O functionality.
4. Reduce Problem: You can first convert the list of dicts to a list of lists and then use our related tutorial’s methods to write the list of lists to the CSV.

My preference is Method 1 (Pandas) because it’s simplest to use, concise, and most robust for different input types (numerical or textual).

Method 1: Pandas DataFrame to_csv()

You can convert a list of lists to a Pandas DataFrame that provides you with powerful capabilities such as the to_csv() method. This is the easiest method and it allows you to avoid importing yet another library (I use Pandas in many Python projects anyways).

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}] # Method 1

import pandas as pd

df = pd.DataFrame(salary)

df.to_csv('my_file.csv', index=False, header=True)

Output:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Carl,Manager,119000

You create a Pandas DataFrame—which is Python’s default representation of tabular data. Think of it as an Excel spreadsheet within your code (with rows and columns).

The DataFrame is a very powerful data structure that allows you to perform various methods. One of those is the to_csv() method that allows you to write its contents into a CSV file.

• You set the index argument of the to_csv() method to False because Pandas, per default, adds integer row and column indices 0, 1, 2, …. Again, think of them as the row and column indices in your Excel spreadsheet. You don’t want them to appear in the CSV file so you set the arguments to False.
• You set the and header argument to True because you want the dict keys to be used as headers of the CSV.

If you want to customize the CSV output, you’ve got a lot of special arguments to play with. Check out this article for a comprehensive list of all arguments.

Related article: Pandas Cheat Sheets to Pin to Your Wall

Method 2: Python CSV Module DictWriter

You can convert a list of dicts to a CSV file in Python easily—by using the csv library. This is the most customizable of all four methods.

Here are the six easy steps to convert a list of dicts to a CSV with header row:

1. Import the CSV library with import csv.
2. Open the CSV file using the expression open('my_file.csv', 'w', newline=''). You need the newline argument because otherwise, you may see blank lines between the rows in Windows.
3. Create a csv.DictWriter() object passing the file and the fieldnames argument.
4. Set the fieldnames argument to the first dictionary’s keys using the expression salary[0].keys().
5. Write the header using writer.writeheader()
6. Write the list of dicts using writer.writerows()

Here’s the full code for copy&paste:

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}] # Method 2

import csv

with open('my_file.csv', 'w', newline='') as f: writer = csv.DictWriter(f, fieldnames=salary[0].keys()) writer.writeheader() writer.writerows(salary)

Output file named 'my_file.csv' and located in the same folder:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Carl,Manager,119000 

You can customize the CSV writer in its constructor (e.g., by modifying the delimiter from a comma ',' to a whitespace ' ' character). Have a look at the specification to learn about advanced modifications.

Method 3: Pure Python Without External Dependencies

If you don’t want to import any library and still convert a list of dicts into a CSV file, you can use standard Python implementation as well: it’s not complicated and very efficient.

This method is best if you won’t or cannot use external dependencies.

• Open the file f in writing mode using the standard open() function.
• Write the first dictionary’s keys in the file using the one-liner expression f.write(','.join(salary[0].keys())).
• Iterate over the list of dicts and write the values in the CSV using the expression f.write(','.join(str(x) for x in row.values())).

Here’s the concrete code example:

salary = [{'Name':'Alice', 'Job':'Data Scientist', 'Salary':122000}, {'Name':'Bob', 'Job':'Engineer', 'Salary':77000}, {'Name':'Carl', 'Job':'Manager', 'Salary':119000}] # Method 3

with open('my_file.csv','w') as f: f.write(','.join(salary[0].keys())) f.write('\n') for row in salary: f.write(','.join(str(x) for x in row.values())) f.write('\n')

Output:

Name,Job,Salary
Alice,Data Scientist,122000
Bob,Engineer,77000
Carl,Manager,119000

In the code, you first open the file object f. Then you iterate over each row and each element in the row and write the element to the file—one by one. After each element, you place the comma to generate the CSV file format. After each row, you place the newline character '\n'.

Note: to get rid of the trailing comma, you can check if the element x is the last element in the row within the loop body and skip writing the comma if it is.

Finxter Recommended: Join the Finxter community and download your 8+ Python cheat sheets to refresh your code understanding.

Method 4: Converting to List of Lists First

A simple approach to convert a list of dicts to a CSV file is to first convert the list of dicts to a list of lists and then use the approaches discussed in the following article (code block given).

salary = [['Alice', 'Data Scientist', 122000], ['Bob', 'Engineer', 77000], ['Ann', 'Manager', 119000]] # Method 1

import csv

with open('file.csv', 'w', newline='') as f: writer = csv.writer(f) writer.writerows(salary) # Method 2

import pandas as pd

df = pd.DataFrame(salary)

df.to_csv('file2.csv', index=False, header=False) # Method 3

a = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] import numpy as np

a = np.array(a)

np.savetxt('file3.csv', a, delimiter=',') # Method 4

with open('file4.csv','w') as f: for row in salary: for x in row: f.write(str(x) + ',') f.write('\n')

Related Tutorial: How to Convert a List to a CSV File in Python [5 Ways]

Where to Go From Here?

Enough theory. Let’s get some practice!

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To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?

You build high-value coding skills by working on practical coding projects!

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If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.

If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.

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Step 1: Install csv-ical Module with PIP

Run the following command in your command line or PowerShell (Windows) or shell or terminal (macOS, Linux, Ubuntu) to install the csv-ical library:

pip install csv-ical

In some instances, you need to modify this command a bit to make it work. If you need more assistance installing the library, check out my detailed guide.

Full Guide: How to install a library/module in Python?

Step 2: Prepare files

Create a new Python code file with the extension .py or a Jupyter Notebook with the file extension .ipynb. This creates a Python script or Jupyter Notebook that can run the code in Step 3 to conver the .ics.

Now, put the .ics file to be converted in the same folder as the newly-created Python script.

Use Jupyter Notebook to create a new .ipynb file

Step 3: Convert

This step consists of running the code doing these three things:

• Create and initialize a Convert object
• Read the .ics file
• Create the CSV object and save it at the specified location

Here’s the full code:

from csv_ical import Convert # Create and initialize a Convert object

convert = Convert()

convert.CSV_FILE_LOCATION = 'my_file.csv'

convert.SAVE_LOCATION = 'my_file.ics' # Read the .ics file

convert.read_ical(convert.SAVE_LOCATION) # Create the CSV object and save it at the specified location

convert.make_csv()

convert.save_csv(convert.CSV_FILE_LOCATION)

Thanks for going through the whole tutorial! <3