polars_pets_per_grade.py

import polars as pl
import opendp.prelude as dp
dp.enable_features("contrib")

# set up your analysis
context = dp.Context.compositor(
    data=pl.scan_csv("grade_pets.csv"),
    privacy_unit=dp.unit_of(contributions=1),
    privacy_loss=dp.loss_of(epsilon=1.),
    split_evenly_over=3,
    margins={("grade",): dp.Margin(
        public_info="keys", 
        max_partition_length=100)
    })

# define a query to count the number of pets per grade
query = context.query() \
    .group_by("grade") \
    .agg(pl.col("pets").fill_null(0).dp.sum(bounds=(0, 10)))

release = query.release().collect()