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In this example, we define a SQL query – feel free to swap out the example query for your own. When you call the `.execute()` method on the cursor, the query runs against your Databricks SQL warehouse. The cursor then fetches the results, converting them into a pandas DataFrame using `.fetch_pandas_all()`. This DataFrame (`df`) is now ready for you to use with pandas! The beauty of lv mobile this approach lies in its simplicity. You write the SQL query, execute it, and instantly get a DataFrame to manipulate, analyze, and visualize. This means you can use all the familiar pandas functions – filtering, grouping, joining, and more – directly on the data pulled from Databricks SQL. It is like having a direct line of communication between your data warehouse and your data analysis tools.
