# Save SQL query results in a pandas DataFrame
<a name="sagemaker-sql-extension-features-sql-execution-save-dataframe"></a>

You can store the results of your SQL query in a pandas DataFrame. The easiest way to output query results to a DataFrame is to use the [SQL editor features of the JupyterLab SQL extension](sagemaker-sql-extension-features-editor.md) query-result dropdown and choose the **Pandas dataframe** option.

Alternatively, you can add the parameter `--output '{"format": "DATAFRAME", "dataframe_name": "dataframe_name"}'` to your connection string.

For example, the following query extracts details of customers with the highest balance from the `Customer` table in Snowflake's `TPCH_SF1` database, using both pandas and SQL:
+ In this example, we extract all the data from the customer table and save then in a DataFrame named `all_customer_data`.

  ```
  %%sm_sql --output '{"format": "DATAFRAME", "dataframe_name": "all_customer_data"}' --metastore-id snowflake-connection-name --metastore-type GLUE_CONNECTION
  SELECT * FROM SNOWFLAKE_SAMPLE_DATA.TPCH_SF1.CUSTOMER
  ```

  ```
  Saved results to all_customer_data
  ```
+ Next, we extract the details of the highest account balance from the DataFrame.

  ```
  all_customer_data.loc[all_customer_data['C_ACCTBAL'].idxmax()].values
  ```

  ```
  array([61453, 'Customer#000061453', 'RxNgWcyl5RZD4qOYnyT3', 15,
  '25-819-925-1077', Decimal('9999.99'), 'BUILDING','es. carefully regular requests among the blithely pending requests boost slyly alo'],
  dtype=object)
  ```