Deepnote provides an easy way to explore the current variables present in a notebook. After executing a cell that defines a variable, the variable will appear in the right-hand sidebar with info about its type and contents.
For all variable types, you can click on the variable to show the variable's contents from within the notebook itself:
When displaying any Pandas DataFrame, Deepnote provides interactive controls that allow you explore your data without having to write any additional code:
- Add and combine filters and row sorting to examine subsets of your data
- Visualize ratios, distributions, and data types for each column (depending on the size of your DataFrame)
- Paginate through your DataFrame and download it as a CSV file by using the controls at the bottom
- Create a no-code chart for the DataFrame by clicking the
visualizebutton in the right-hand corner