Join us and get access to thousands of tutorials and a community of expert Pythonistas.

Unlock This Lesson

This lesson is for members only. Join us and get access to thousands of tutorials and a community of expert Pythonistas.

Unlock This Lesson

Hint: You can adjust the default video playback speed in your account settings.
Hint: You can set your subtitle preferences in your account settings.
Sorry! Looks like there’s an issue with video playback 🙁 This might be due to a temporary outage or because of a configuration issue with your browser. Please refer to our video player troubleshooting guide for assistance.

Reading and Writing Files With pandas (Summary)

Darren Jones
Reading and Writing Files With pandas Darren Jones 01:16
Recommended Tutorial Course Slides (.pdf) Sample Code (.zip) Ask a Question

You now know how to save the data and labels from pandas DataFrame objects to different kinds of files. You also know how to load your data from files and create DataFrame objects.

You’ve used the pandas read_csv() and .to_csv() methods to read and write CSV files. You also used similar methods to read and write Excel, JSON, HTML, SQL, and pickle files. These functions are very convenient and widely used. They allow you to save or load your data in a single function or method call.

You’ve also learned how to save time, memory, and disk space when working with large data files:

  • Compress or decompress files
  • Choose the rows and columns you want to load
  • Use less precise data types
  • Split data into chunks and process them one by one
Download

Sample Code (.zip)

 33.8 KB
Download

Course Slides (.pdf)

 11.6 MB

00:00 Summary. Well done! You’ve made it to the end of the course. In it, you’ve learned how to save the data and label from pandas DataFrame objects to different file types.

00:12 You’ve also learned how to load data from those files and create DataFrame objects. You’ve used the pandas read_csv() and .to_csv() methods to read and write CSV files, and you’ve used similar methods to read and write Excel, JSON, HTML, SQL, and pickle files.

00:32 These functions are convenient and widely used. They allow you to save or load your data in a single function or method call.

00:40 You’ve also learned how to save time, memory, and disk space when working with large data files by using compression and decompression, choosing the rows and columns you want to load, using less precise data types, and splitting the data into chunks.

00:56 Using the wide range of techniques that you’ve learned in this course, you can be confident that you can use files in conjunction with pandas to use a wide range of data sources and store your data in an efficient manner.

01:09 We hope you found this course useful, and we’ll see you again soon at realpython.com.

Steven Wolk on Aug. 19, 2021

I learned about Pandas and pickle files. You can really get into a pickle if you are not careful!

Become a Member to join the conversation.