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:

Interactive DataFrame output

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 visualize button in the right-hand corner