snowpark-checkpoints-hypothesis 0.4.0
Hypothesis extension for generating Snowpark DataFrames
License: Apache License, Version 2.0
Requires Python: <3.12,>=3.9
Keywords: Snowflake, Snowpark, analytics, cloud, database, db

# snowpark-checkpoints-hypothesis

---
##### This package is on Public Preview.
---

**snowpark-checkpoints-hypothesis** is a [Hypothesis](https://hypothesis.readthedocs.io/en/latest/) 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**:

```shell
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](https://pypi.org/project/snowpark-checkpoints-collectors/) package:

```python
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:

```python
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**:

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

    Using **conda**:

    ```shell
    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](https://www.jetbrains.com/help/pycharm/configuring-python-interpreter.html)
    * [Configuring a Python interpreter in VS Code](https://code.visualstudio.com/docs/python/environments#_manually-specify-an-interpreter)

3. Install the project dependencies:

    ```shell
    pip install hatch
    pip install -e .
    ```

### Running Tests

To run tests, run the following command.

```shell
hatch run test:check
```

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