snowpark-checkpoints-hypothesis 0.1.0rc2

Hypothesis extension for generating Snowpark DataFrames

snowpark-checkpoints-hypothesis

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NOTE

This package is on Private Preview.

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snowpark-checkpoints-hypothesis is a Hypothesis extension for generating Snowpark DataFrames. This project provides strategies to facilitate testing and data generation for Snowpark DataFrames using the Hypothesis library.

Installation

You can install this package using either pip or conda:

pip install snowpark-checkpoints-hypothesis
--or--
conda install snowpark-checkpoints-hypothesis

Usage

The typical workflow for using the Hypothesis library to generate Snowpark dataframes is as follows:

1. Create a standard Python test function with the different assertions or conditions your code should satisfy for all inputs.
2. Add the Hypothesis @given decorator to your test function and pass the dataframe_strategy function as an argument.
3. Run the test. When the test is executed, Hypothesis will automatically provide the generated inputs as arguments to the test.

Example 1: Generate Snowpark DataFrames from a JSON schema file

You can use the dataframe_strategy function to create Snowpark DataFrames from a JSON schema file generated by the collect_dataframe_checkpoint function of the snowpark-checkpoints-collectors package:

from hypothesis import given
from snowflake.hypothesis_snowpark import dataframe_strategy
from snowflake.snowpark import DataFrame, Session


@given(
    df=dataframe_strategy(
        schema="path/to/schema.json",
        session=Session.builder.getOrCreate(),
        size=10,
    )
)
def test_my_function(df: DataFrame):
    # Test your function here
    ...

Example 2: Generate Snowpark DataFrames from a Pandera DataFrameSchema object

You can also use the dataframe_strategy function to create Snowpark DataFrames from a Pandera DataFrameSchema object:

import pandera as pa
from hypothesis import given
from snowflake.hypothesis_snowpark import dataframe_strategy
from snowflake.snowpark import DataFrame, Session

@given(
    df=dataframe_strategy(
        schema=pa.DataFrameSchema(
            {
                "A": pa.Column(pa.Int, checks=pa.Check.in_range(0, 10)),
                "B": pa.Column(pa.Bool),
            }
        ),
        session=Session.builder.getOrCreate(),
        size=10,
    )
)
def test_my_function(df: DataFrame):
    # Test your function here
    ...

Development

Set up a development environment

To set up a development environment, follow the steps below:

1. Create a virtual environment using venv or conda. Replace <env-name> with the name of your environment.

   Using venv:

   python3.11 -m venv <env-name>
   source <env-name>/bin/activate
   

   Using conda:

   conda create -n <env-name> python=3.11
   conda activate <env-name>
   

2. Configure your IDE to use the previously created virtual environment:

   • Configuring a Python interpreter in PyCharm
   • Configuring a Python interpreter in VS Code

3. Install the project dependencies:

   pip install hatch
   pip install -e .
   

Running Tests

To run tests, run the following command.

hatch run test:check

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