# 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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"description": "# snowpark-checkpoints-hypothesis\n\n---\n##### This package is on Public Preview.\n---\n\n**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.\n\n## Installation\n\nYou can install this package using either **pip** or **conda**:\n\n```shell\npip install snowpark-checkpoints-hypothesis\n--or--\nconda install snowpark-checkpoints-hypothesis\n```\n\n## Usage\n\nThe typical workflow for using the Hypothesis library to generate Snowpark dataframes is as follows:\n\n1. Create a standard Python test function with the different assertions or conditions your code should satisfy for all inputs.\n2. Add the Hypothesis `@given` decorator to your test function and pass the `dataframe_strategy` function as an argument.\n3. Run the test. When the test is executed, Hypothesis will automatically provide the generated inputs as arguments to the test.\n\n### Example 1: Generate Snowpark DataFrames from a JSON schema file\n\nYou 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:\n\n```python\nfrom hypothesis import given\nfrom snowflake.hypothesis_snowpark import dataframe_strategy\nfrom snowflake.snowpark import DataFrame, Session\n\n\n@given(\n df=dataframe_strategy(\n schema=\"path/to/schema.json\",\n session=Session.builder.getOrCreate(),\n size=10,\n )\n)\ndef test_my_function(df: DataFrame):\n # Test your function here\n ...\n```\n\n### Example 2: Generate Snowpark DataFrames from a Pandera DataFrameSchema object\n\nYou can also use the `dataframe_strategy` function to create Snowpark DataFrames from a Pandera DataFrameSchema object:\n\n```python\nimport pandera as pa\nfrom hypothesis import given\nfrom snowflake.hypothesis_snowpark import dataframe_strategy\nfrom snowflake.snowpark import DataFrame, Session\n\n@given(\n df=dataframe_strategy(\n schema=pa.DataFrameSchema(\n {\n \"A\": pa.Column(pa.Int, checks=pa.Check.in_range(0, 10)),\n \"B\": pa.Column(pa.Bool),\n }\n ),\n session=Session.builder.getOrCreate(),\n size=10,\n )\n)\ndef test_my_function(df: DataFrame):\n # Test your function here\n ...\n```\n\n## Development\n\n### Set up a development environment\n\nTo set up a development environment, follow the steps below:\n\n1. Create a virtual environment using **venv** or **conda**. Replace \\<env-name\\> with the name of your environment.\n\n Using **venv**:\n\n ```shell\n python3.11 -m venv <env-name>\n source <env-name>/bin/activate\n ```\n\n Using **conda**:\n\n ```shell\n conda create -n <env-name> python=3.11\n conda activate <env-name>\n ```\n\n2. Configure your IDE to use the previously created virtual environment:\n\n * [Configuring a Python interpreter in PyCharm](https://www.jetbrains.com/help/pycharm/configuring-python-interpreter.html)\n * [Configuring a Python interpreter in VS Code](https://code.visualstudio.com/docs/python/environments#_manually-specify-an-interpreter)\n\n3. Install the project dependencies:\n\n ```shell\n pip install hatch\n pip install -e .\n ```\n\n### Running Tests\n\nTo run tests, run the following command.\n\n```shell\nhatch run test:check\n```\n\n------\n",
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