Using the IPython and Jupyter Magic

Work with pandas and PRQL in an IPython terminal or Jupyter notebook.


This is a thin wrapper around ploomber/jupysql magic. Full documentation of the supported features is available at their repository. Here, we document the most salient features or those where we differ.



If you have already installed pyprql into your environment, then you should be could to go! We bundle in IPython and pandas, though you’ll need to install Jupyter separately. If you haven’t installed pyprql, that’s as simple as:

pip install pyprql

Set Up

Open up either an IPython terminal or Jupyter notebook. First, we need to load the extension and connect to a database.

In [1]: %load_ext pyprql.magic

Connecting a database

We have two options for connecting a database

  1. Create an in-memory DB. This is the easiest way to get started.

    In [2]: %prql duckdb:///:memory:

    However, in-memory databases start off empty! So, we need to add some data. We have a two options:

    • We can easily add a pandas dataframe to the DuckDB database like so:

      In [3]: %prql --persist df

      where df is a pandas dataframe. This adds a table named df to the in-memory DuckDB instance.

    • Or download a CSV and query it directly, with DuckDB:


      …and then from `products.csv` will work.

  2. Connect to an existing database

    When connecting to a database, pass the connection string as an argument to the line magic %prql. The connection string needs to be in SQLAlchemy format, so any connection supported by SQLAlchemy is supported by the magic. Additional connection parameters can be passed as a dictionary using the --connection_arguments flag to the the %prql line magic. We ship with the necessary extensions to use DuckDB as the backend, and here connect to an in-memory database.


Now, let’s do a query! By default, PrqlMagic always returns the results as dataframe, and always prints the results. The results of the previous query are accessible in the _ variable.

These examples are based on the from `products.csv` example above.

In [4]: %%prql
   ...: from p = `products.csv`
   ...: filter supplierID == 1

Returning data to local variable _
   productID    productName  supplierID  categoryID      quantityPerUnit  unitPrice  unitsInStock  unitsOnOrder  reorderLevel  discontinued
0          1           Chai           1           1   10 boxes x 20 bags       18.0            39             0            10             0
1          2          Chang           1           1   24 - 12 oz bottles       19.0            17            40            25             0
2          3  Aniseed Syrup           1           2  12 - 550 ml bottles       10.0            13            70            25             0
In [5]: %%prql
   ...: from p = `products.csv`
   ...: group categoryID (
   ...:   aggregate {average unitPrice}
   ...: )

Returning data to local variable _
   categoryID  avg("unitPrice")
0           1         37.979167
1           2         23.062500
2           7         32.370000
3           6         54.006667
4           8         20.682500
5           4         28.730000
6           3         25.160000
7           5         20.250000

We can capture the results into a different variable like so:

In [6]: %%prql results <<
   ...: from p = `products.csv`
   ...: aggregate {min unitsInStock, max unitsInStock}

Returning data to local variable results
   min("unitsInStock")  max("unitsInStock")
0                    0                  125

Now, the output of the query is saved to results.

We can also use a line magic to capture the results like this:

In [7]: results = %prql from p = `products.csv` | aggregate {min unitsInStock, max unitsInStock}


We strive to provide sane defaults; however, should you need to change settings, a list of settings is available using the %config line magic.

In [8]: %config PrqlMagic
PrqlMagic(SqlMagic) options
    Set autocommit mode
    Current: True
    Automatically limit the size of the returned result sets
    Current: 0
    Return Pandas DataFrames instead of regular result sets
    Current: True
    Return Polars DataFrames instead of regular result sets
    Current: False
    Display results
    Current: True
    Return data into local variables from column names
    Current: False
    Show connection string after execute
    Current: False
    Automatically limit the number of rows displayed (full result set is still
    Current: None
    Only print the compiled SQL
    Current: False
    Path to DSN file. When the first argument is of the form [section], a
    sqlalchemy connection string is formed from the matching section in the DSN
    Current: 'odbc.ini'<Bool>
    Print number of rows affected by DML
    Current: False
    Polars DataFrame constructor keyword arguments(e.g. infer_schema_length,
    nan_to_null, schema_overrides, etc)
    Current: {}
    Don't display the full traceback on SQL Programming Error
    Current: True<Unicode>
    Set the table printing style to any of prettytable's defined styles
    Current: 'DEFAULT'<Unicode>
    Compile target of prql-compiler
    Current: 'sql.any'

If you want to change any of these, you can do that with the %config line magic as well.

In [9]: %config PrqlMagic.autoview = False