Metadata-Version: 2.1
Name: BaseballFaker
Version: 0.0.3
Summary: A small example package
Author-email: Josh Kolodny <kolo.josh@gmail.com>
Project-URL: Homepage, https://github.com/JKolodny/BaseballFaker
Project-URL: Bug Tracker, https://github.com/JKolodny/BaseballFaker/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: faker
Requires-Dist: numpy
Requires-Dist: requests

# ![image](https://github.com/JKolodny/BaseballFaker/assets/24982246/a964ab4e-bf43-41b7-9c41-0b8a5e2379e9)

![image](https://github.com/JKolodny/BaseballFaker/assets/24982246/b6762139-61eb-44d4-a7c3-2762ed5c9659)

## Motivation

* Lately, when trying to work on sports analytics projects, I realize that a big
bottleneck (at least for me) is getting my hands on data. In effect, this makes it so that when I want to start a project, I try to re-learn a little about web scraping for a day or two, and then quit soon after that. 

* The value I'm trying to create here is the ability to generate simple, probably not great, but reasonable fake data for use in projects before committing to developing web scraping pipelines.

## Usage 
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1i2MNvq7QqfESSEbhTfe1WYn05_WtMeK-?usp=sharing)

```
from baseballfaker.baseball import batting

batting(CAREER_LENGTH=10, 
        skill_level=.9)
```

* Example Output:
    * see __example.ipynb__:
      * ![image](https://github.com/JKolodny/SportFaker/assets/24982246/6578c484-5247-41e9-9d7f-9cd22233bb80)

## License

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)





