[Tut] How to Convert Tuples to a CSV File in Python [4 Ways]

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[Tut] How to Convert Tuples to a CSV File in Python [4 Ways] - xSicKxBot - 05-02-2022

How to Convert Tuples to a CSV File in Python [4 Ways]

Problem: How to convert one or more tuples to a

Code:
csv

file?

Example: Given is a tuple or list of tuples—for example, salary data of employees in a given company:

Code:
salary = [('Alice', 'Data Scientist', 122000), ('Bob', 'Engineer', 77000), ('Ann', 'Manager', 119000)]

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

Code:
# file.csv
Alice,Data Scientist,122000
Bob,Engineer,77000
Ann,Manager,119000

Note that writing a single tuple to a CSV is a subproblem of writing multiple tuples to a CSV that can be easily solved by passing a list with a single tuple as an input to any function we’ll discuss in the article.

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

CSV: Import the
Code:
csv
module in Python, create a csv writer object, and write the list of tuples to the file in using the
Code:
writerows()
method on the writer object.
Pandas: Import the pandas library, create a Pandas DataFrame, and write the DataFrame to a file using the DataFrame method
Code:
DataFrame.to_csv('file.csv')
.
NumPy: Import the NumPy library, create a NumPy array, and write the output to a CSV file using the
Code:
numpy.savetxt('file.csv', array, delimiter=',')
method.
Python: Use a pure Python implementation that doesn’t require any library by using the Python file I/O functionality.

My preference is method 2 (Pandas) because it’s simplest to use and most robust for different input types (numerical or textual).

Try It Yourself: Before we dive into these methodsin more detail, feel free to play with them in our interactive code shell. Simply click the “Run” button and find the generated CSV files in the “Files” tab.

Try It Yourself: Click to run in interactive shell!

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Method 1: Python’s CSV Module

You can convert a list of tuples to a CSV file in Python easily—by using the

Code:
csv

library. This is the most customizable of all four methods.

Code:
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)

Output:

Code:
# file.csv
Alice,Data Scientist,122000
Bob,Engineer,77000
Ann,Manager,119000

In the code, you first open the file using Python’s standard

Code:
open()

command. Now, you can write content to the file object

Code:
f

.

Next, you pass this file object to the constructor of the CSV writer that implements some additional helper method—and effectively wraps the file object providing you with new CSV-specific functionality such as the

Code:
writerows()

method.

You now pass a list of tuples to the

Code:
writerows()

method of the CSV writer that takes care of converting the list of tuples to a CSV format.

You can customize the CSV writer in its constructor (e.g., by modifying the delimiter from a comma

Code:
','

to a whitespace

Code:
' '

character). Have a look at the specification to learn about advanced modifications.

Method 2: Pandas DataFrame to_csv()

[Image: list_of_tuples_to_csv-1024x576.jpg]

[Image: list_of_tuples_to_csv-1024x576.jpg]

You can convert a tuple or list of tuples to aPandas DataFrame that provides you with powerful capabilities such as the

Code:
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).

Code:
salary = [('Alice', 'Data Scientist', 122000), ('Bob', 'Engineer', 77000), ('Ann', 'Manager', 119000)] # Method 2
import pandas as pd
df = pd.DataFrame(salary)
df.to_csv('file2.csv', index=False, header=False)

Output:

Code:
# file2.csv
Alice,Data Scientist,122000
Bob,Engineer,77000
Ann,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

Code:
to_csv()

method that allows you to write its contents into a CSV file.

You set the

Code:
index

and

Code:
header

arguments of the

Code:
to_csv()

method to

Code:
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

Code:
False

.

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

Related article:Pandas Cheat Sheets to Pin to Your Wall

Method 3: NumPy savetext()

NumPyis at the core of Python’s data science and machine learning functionality. Even Pandas uses NumPy arrays to implement critical functionality.

You can convert a list of tuples to a CSV file by using NumPy’s

Code:
savetext()

function and passing the NumPy array as an argument that arises from the conversion of the list of tuples.

This method is best if you have numerical data only—otherwise, it’ll lead to complicated data type conversions which are not recommended.

Code:
a = [(1, 2, 3), (4, 5, 6), (7, 8, 9)] # Method 3
import numpy as np
a = np.array(a)
np.savetxt('file3.csv', a, delimiter=',')

Output:

Code:
# file3.csv
1.000000000000000000e+00,2.000000000000000000e+00,3.000000000000000000e+00
4.000000000000000000e+00,5.000000000000000000e+00,6.000000000000000000e+00
7.000000000000000000e+00,8.000000000000000000e+00,9.000000000000000000e+00

The output doesn’t look pretty: it stores the values as floats. But no worries, you can reformat the output using the format argument

Code:
fmt

of the

Code:
savetxt()

method (more here). However, I’d recommend you stick to method 2 (Pandas) to avoid unnecessary complexity in your code.

Method 4: Pure Python Without External Dependencies

If you don’t want to import any library and still convert a list of tuples into a CSV file, you can use standard Python implementation as well: it’s not complicated and efficient. However, if possible you should rely on libraries that do the job for you.

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

Code:
salary = [('Alice', 'Data Scientist', 122000), ('Bob', 'Engineer', 77000), ('Ann', 'Manager', 119000)] # Method 4
with open('file4.csv','w') as f: for row in salary: for x in row: f.write(str(x) + ',') f.write('\n')

Output:

Code:
# file4.csv
Alice,Data Scientist,122000,
Bob,Engineer,77000,
Ann,Manager,119000,

In the code, you first open the file object

Code:
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

Code:
'\n'

.

Note: to get rid of the trailing comma, you can check if the element

Code:
x

is the last element in the row within the loop body and skip writing the comma if it is.

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

Related Video

The following video shows how to convert a list of lists to a CSV in Python, converting a tuple or list of tuples will be similar:

Where to Go From Here?

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