README.md for Sports Archive Project

Project Overview

The Sports Archive Project is designed for efficient management and querying of a large sports game dataset. It allows users to perform various operations such as listing all games, counting games per sport, and retrieving specific representative data.

Key Components

Main Script (archive.py):
- Manages command-line arguments to perform operations like listing games, counting games per sport, and displaying representative data.
- Integrates DataArchiveService for data operations and timing_decorator for performance tracking.
Data Archive Service (controls.py):
- Central service for data handling.
- Reads and processes data from a Parquet file.
- Offers functionalities like game listing, game count by sport, and representative data retrieval.
Performance Decorator (performance_decorator.py):
- A decorator for measuring and reporting function execution times.
Unit Tests (tests directory):
- Contains tests for DataArchiveService.
- Ensures the accuracy and reliability of service functionalities.

Installation

Before running the project, install the necessary dependencies:

pip install -r requirements.txt

How to Run

Place the Parquet data file in the project directory. Run archive.py with the following arguments for different functionalities:

Counting Games by Sport:
```
python archive.py -count [SPORT]
```
Replace [SPORT] with the desired sport name to count the number of games for that sport.
Displaying Representative Data for a Sport:
```
python archive.py -repr [SPORT] [FRAME COUNT] [FIXTURES COUNT]
```
Replace [SPORT], [FRAME COUNT], and [FIXTURES COUNT] with the desired sport name, frame count, and fixtures count, respectively. This command shows representative data for the specified sport.
Testing date processing:
```
python archive.py -test
```

Example Commands:

To count games for Tennis:
```
python archive.py -count tennis
```
To display representative data for Tennis:
```
python archive.py -repr tennis 100 200
```
Testing date processing:
```
python archive.py -test
```

Development Environment

Python 3.x
Pandas library
PyArrow for Parquet file handling

Testing

Execute the unit tests in the tests directory to check the functionalities. Passing all tests ensures system reliability.

Future Enhancements

Expand the range of sports data.
Add advanced data analysis features.
Optimize for larger datasets.

Contributions

We welcome contributions via the standard fork-pull request workflow.

This README offers a basic guide to the Sports Archive Project. For comprehensive documentation, consult the inline comments and docstrings in the codebase.

haveacar/dataarchive