Parameterizing arguments

New in version 0.10.8: JupySQL uses Jinja templates for enabling parametrization of arguments. Arguments are parametrized with {{variable}}.

Parametrization via {{variable}}

JupySQL supports variable expansion of arguments in the form of {{variable}}. This allows the user to specify arguments with placeholders that can be replaced by variables dynamically.

The benefits of using parametrized arguments is that they can be reused for different purposes.

Let’s load some data and connect to the in-memory DuckDB instance:

%load_ext sql
%sql duckdb://
%config SqlMagic.displaylimit = 3

Connecting to 'duckdb://'

filename = "penguins.csv"

from pathlib import Path
from urllib.request import urlretrieve

if not Path("penguins.csv").is_file():
    urlretrieve(
        "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv",
        filename,
    )

Now let’s create a snippet from the data by declaring a table variable and use it in the --save argument.

Create a snippet

table = "penguins_data"

%%sql --save {{table}}
SELECT *
FROM penguins.csv

Running query in 'duckdb://'

species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
Adelie	Torgersen	39.1	18.7	181	3750	MALE
Adelie	Torgersen	39.5	17.4	186	3800	FEMALE
Adelie	Torgersen	40.3	18.0	195	3250	FEMALE

Truncated to displaylimit of 3.

snippet = %sqlcmd snippets {{table}}
print(snippet)

SELECT *
FROM penguins.csv

Plot a histogram

Now, let’s declare a variable column and plot a histogram on the data.

column = "body_mass_g"

%sqlplot boxplot --table {{table}} --column {{column}}

<Axes: title={'center': "'body_mass_g' from 'penguins_data'"}, ylabel='body_mass_g'>

Profile and Explore

We can use the filename variable to profile and explore the data as well:

%sqlcmd profile --table {{filename}}

	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
count	344	344	342	342	342	342	333
unique	3	3	164	80	55	94	2
top	Adelie	Biscoe	nan	nan	nan	nan	MALE
freq	152	168	nan	nan	nan	nan	168
mean	nan	nan	43.9219	17.1512	200.9152	4201.7544	nan
std	nan	nan	5.4516	1.9719	14.0411	800.7812	nan
min	nan	nan	32.1	13.1	172	2700	nan
25%	nan	nan	39.2000	15.6000	190.0000	3550.0000	nan
50%	nan	nan	44.4000	17.3000	197.0000	4050.0000	nan
75%	nan	nan	48.5000	18.7000	213.0000	4750.0000	nan
max	nan	nan	59.6	21.5	231	6300	nan

%sqlcmd explore --table {{filename}}

Run some tests

%sqlcmd test --table {{table}} --column {{column}} --greater 3500

True