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)Output:
# 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 index and 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 False.
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
Feel free to play with alternative methods to convert a list of lists to a CSV file in our interactive code shell. Simply click the “Run” button and find the generated CSV files in the “Files” tab.
Where to Go From Here?
Enough theory. Let’s get some practice!
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