Problem: How to convert a list of lists to a
Example: Given is a list of list—for example salary data of employees in a given company:
salary = [['Alice', 'Data Scientist', 122000], ['Bob', 'Engineer', 77000], ['Ann', 'Manager', 119000]]
Your goal is to write the content of the list of lists into a comma-separated-values (CSV) file format. Your outfile should look like this:
# file.csv Alice,Data Scientist,122000 Bob,Engineer,77000 Ann,Manager,119000
- CSV: Import the
csvmodule in Python, create a csv writer object, and write the list of lists to the file in using the
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
- NumPy: Import the NumPy library, create a NumPy array, and write the output to a CSV file using the
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).
Before we dive into these methods in 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.
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Method 1: Python’s CSV Module
You can convert a list of lists to a CSV file in Python easily—by using the csv library. This is the most customizable of all four methods.
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)
# file.csv Alice,Data Scientist,122000 Bob,Engineer,77000 Ann,Manager,119000
In the code, you first open the file using Python’s standard
open() command. Now, you can write content to the file object
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
You now pass a list of lists to the
writerows() method of the CSV writer that takes care of converting the list of lists to a CSV format.
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 2: 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 = [['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)
# 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
to_csv() method that allows you to write its contents into a CSV file.
You set the
header arguments 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
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 3: NumPy savetext()
NumPy is 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 lists to a CSV file by using NumPy’s
savetext() function and passing the NumPy array as an argument that arises from conversion of the list of lists.
This method is best if you have numerical data only—otherwise, it’ll lead to complicated data type conversions which are not recommended.
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=',')
# 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
fmt of the
savetxt() method (more here). However, I’d recommend you stick to method 2 (Pandas) to avoid unnecessary complexity in your code.
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Method 4: Pure Python Without External Dependencies
If you don’t want to import any library and still convert a list of lists 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.
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')
# file4.csv Alice,Data Scientist,122000, Bob,Engineer,77000, Ann,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
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.
Where to Go From Here?
Enough theory, let’s get some practice!
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