If you have ever found yourself rewriting the last line of a notebook cell repeatedly just to get an overview of your data, you’re not alone. In VS Code the default output for Pandas DataFrames is a static, truncated HTML table and it often fails to answer essential questions, such as:
- Do we have rogue blank values somewhere we did not expect?
- Do the columns we plan on using as keys really contain unique values?
- Are the data types what I expect them to be?
- How many times does a specific value show up in the results?
- What are the last 10 items in this 30k items list?
Check out how Data Wrangler integrates seamlessly with notebooks in VS Code to enable you to answer these questions quickly and easily, with just a few clicks.
Seamless integration with notebooks
The new experience seamlessly replaces the static HTML output for Pandas DataFrames, only where applicable, and without any additional actions. Just make sure the Data Wrangler extension is installed 😊
Column sorting and filtering
There is no need to write code for sorting and filtering. You can just click around the interactive UI as you explore the data.
Missing (blank) and distinct values are auto detected
You can instantly know if a column contains missing (blank) values or repeating values you did not expect just by glancing at the column header.
