basketball-reference-webscrapper 0.4.0

Python package for Basketball Reference that gathers data by scraping the website

WebScrapBasketballReference

WebScrapBasketballReference is a Python package designed to scrape NBA games data from the Basketball Reference website. By providing a URL and a season, the package returns all games' team stats for that season.

Features

• Scrapes NBA gamelogs, schedules, and player attributes.
• Validates user inputs to ensure data accuracy.
• Handles team-specific data filtering.
• Collects and processes data into a pandas DataFrame.

Installation

To install WebScrapBasketballReference, clone the repository and install the required dependencies:

git clone https://github.com/yourusername/WebScrapBasketballReference.git
cd WebScrapBasketballReference
pip install -r requirements.txt

Usage

Importing the Package

from basketball_reference_webscrapper.data_models.feature_model import FeatureIn
from basketball_reference_webscrapper.webscrap_basketball_reference import WebScrapBasketballReference

Creating a FeatureIn Object

feature_object = FeatureIn(
    url='https://www.basketball-reference.com',
    season=2023,
    data_type='gamelog',  # 'gamelog', 'schedule', or 'player_attributes'
    team='all'  # 'all' or a list of team abbreviations e.g., ['BOS', 'LAL']
)

Scraping Data

scraper = WebScrapBasketballReference(feature_object)
data = scraper.webscrappe_nba_games_data()
print(data)

Input Validation

The package performs several input validations:

• Data Type Validation: Ensures data_type is one of 'gamelog', 'schedule', or 'player_attributes'.
• Season Validation: Ensures season is an integer between 2000 and the current NBA season.
• Team List Validation: Ensures team is either 'all' or a list of valid NBA team abbreviations.

Configuration

The package uses a params.yaml file to store URL patterns and other configurations. Ensure this file is correctly set up in the basketball_reference_webscrapper directory.

Logging

Logging is configured to provide information about the scraping execution. Logs can be viewed to monitor the process and debug if necessary.

Example

from basketball_reference_webscrapper.data_models.feature_model import FeatureIn
from basketball_reference_webscrapper.webscrap_basketball_reference import WebScrapBasketballReference

# Define the feature object
feature_object = FeatureIn(
    url='https://www.basketball-reference.com',
    season=2023,
    data_type='gamelog',
    team='BOS'  # Example team abbreviation for Boston Celtics
)

# Create the scraper instance
scraper = WebScrapBasketballReference(feature_object)

# Scrape the data
data = scraper.webscrappe_nba_games_data()

# Display the data
print(data)

Contributing

Contributions are welcome! Please submit a pull request or create an issue to report bugs or request features.

License

Contact

For any questions or feedback, please contact [[e-mail]].