Welcome to Deepnote

In this quick example, we will walk you through the basic steps of data exploration in a notebook.

Start with a query

To get you started, we prepared a sample dataset in a demo Snowflake integration. It contains country data for life expectancy, population and GDP per capita over the years.

select * from demo.gapminder

Based on free material from gapminder.org, CC-BY license.

Seamlessly switch to Python

The results of your queries are stored as pandas DataFrames, which makes it easy to work with them in Python. For example, you can explore how life expectancy correlates with economic wealth.

df['life_expectancy'].corr(df['gdp_per_capita'])

Finish with beautiful charts

Charts that push your presentation to the next level are easy to make in Deepnote. Here's a deeper look into the relationship between life expectancy and economic wealth, with additional indicators for continents and population size.

Next steps

Learn more about how to use Deepnote in our documentation.

Get creative. Have a look at some sample projects, then create a new one. Let your imagination run wild 🧑‍🎨

Connect your own data. Visit the Integrations menu to connect your database or data warehouse. Or just drop a CSV into a notebook.

Work together. Invite your team members and share insights with each other.