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Develop your notebook

The Notebook view is where you query, transform, visualize, and explore your data.

Develop your analysis​

Once you've created your project, you're ready to start developing the Notebook view.

Start with SQL or Python​

Hex projects typically begin by adding a SQL cell or Python cell that pulls data from a warehouse connection, CSV file, or API. Add a single cell, or start with a template for a little extra structure.

Empty Hex project displaying templates and buttons to quickly ask Magic to generate an analysis, or add a SQL or Python cell

Choose your data source​

Browse available data connections in the Data browser sidebar. You can also upload files as a data source from the Files sidebar.

Data browser sidebar in a Hex project showing Demo Snowflake Data Connection and options to connect your own data warehouse

Bring data into your project​

To quickly bring data into your project from a data connection, click on the data connection from the Data sources sidebar, and click Query next to any table name. This will automatically create and run a SQL cell with a select * limited to 100 rows.

Data sources sidebar in a Hex project showing the schema browser for the Demo Data Connection and option to Query a table

You can also add a SQL cell from the Add cell bar at the bottom of your Notebook view, and write your own query from scratch. Click the Run button in the upper right of the cell (or cmd-return) to run the cell.

SQL cell querying distinct categories from the prod.order_details table from a Demo Data Connection

If you're querying an uploaded CSV file, you'll need to set your SQL cell's data source dropdown to "dataframes".

SQL cell querying a CSV file with data source dropdown open

Quickly visualize and transform your data​

SQL cells come with built-in display tables and charts, or you can add new cells to visualize and transform your data in multiple ways.

SQL cell on the 'Chart' tab showing a chart of items ordered over time

Reference your query result​

SQL cell outputs are stored as a dataframe, which is a standard table format with rows and columns. Once created, a dataframe becomes a data source in your project that you can query and reference in downstream cells.

Pivot cell referencing the dataframe output of a SQL cell

Python cell outputs are similarly stored as variables, including dataframes, which can be referenced in downstream cells.

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