Export Data in Python with ONE LINE of Code!

Published in

Data And Beyond

3 min readDec 31, 2023

· Introduction
· The Dataset
· Importing the Pandas Library
· Importing the Kaggle Dataset
· Creating a New Dataframe
· Exporting the Data
· Conclusion

Introduction

If you want to permanently save some part of a dataset, then there’s a pretty easy way to do that in Python. This blog is going to show you how to export any data from your Python editor into your local computer.

So, let’s get into it!

The Dataset

For this blog, I’ll use a Kaggle dataset on Netflix movies and TV shows. You can click here to download the data and follow along!

Importing the Pandas Library

This will import the Pandas library, which will allow us to import the dataset from Kaggle into Python.

Importing the Kaggle Dataset

To load the Netflix dataset, I have to use a method from pandas called read_csv():

Here, I just passed in the name of the file into the read_csv() function and stored it into a variable called df.

I also said df.head() because I wanted to take a quick look at the dataset. The head() method basically returned the first 5 rows.

Creating a New Dataframe

Let’s say that I want to create a separate dataset on ONLY the Netflix movies and NOT the TV shows. Using the dataframe I just created before, I can create another dataframe from the existing one:

Here, I created a new dataframe called “df_movies” and say that it’s equal to “df”, as long as df[‘Type’] is equal to ‘Movie’, which is indicated by the conditional inside the square brackets.

So, this line of code created another dataframe that stored data on only the Netflix movies:

Exporting the Data

Now, I’m finally going to export this new dataframe, and it’ll only take one line of code:

In this cell, I used the to_csv() function from Pandas, which would automatically save the Pandas dataframe as a local csv file. And the name of the csv file would be called “netflix_movies.csv” because that’s what I passed inside the to_csv() argument.

So, after I ran that, I got that dataframe saved into my local folder, the same one as the one my Python program was located in.

Conclusion

Alright, that’s it for this blog! Hopefully you learned something new and if you learn better by watching a video, then check out this video that I made:

Thanks for reading, and happy New Year!

Originally published at https://dylans0ng.github.io on December 31, 2023.