Jupyter

pyprql contains pyprql.magic, a thin wrapper of JupySQL’s SQL IPython magics. This allows us to run PRQL interactively on Jupyter/IPython.

Check out <https://pyprql.readthedocs.io/> for more context.

Installation

1. pip install pyprql

Usage

When installing pyprql, the duckdb-engine package is also installed with it, so we can start using PRQL immediately to query CSV and Parquet files.

For example, running the example from the JupySQL documentation on IPython:

1. In [1]: %load_ext pyprql.magic

3. In [2]: !curl -sL https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv -o penguins.csv

5. In [3]: %prql duckdb://

7. In [4]: %prql from `penguins.csv` | take 3
8. Out[4]:
9. species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g     sex
10. 0  Adelie  Torgersen            39.1           18.7                181         3750    MALE
11. 1  Adelie  Torgersen            39.5           17.4                186         3800  FEMALE
12. 2  Adelie  Torgersen            40.3           18.0                195         3250  FEMALE

14. In [5]: %%prql
15. ...: from `penguins.csv`
16. ...: filter bill_length_mm > 40
17. ...: take 3
18. ...:
19. ...:
20. Out[5]:
21. species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g     sex
22. 0  Adelie  Torgersen            40.3           18.0                195         3250  FEMALE
23. 1  Adelie  Torgersen            42.0           20.2                190         4250    None
24. 2  Adelie  Torgersen            41.1           17.6                182         3200  FEMALE