baskref 1.0.0

baskRef is a tool to scrape basketball Data from the web.

BaskRef (Basketball Scraper)

BaskRef is a tool to scrape basketball Data from the web.

The goal of this project is to provide a data collection utility for NBA basketball data. The collection strategy is to scrape data from https://www.basketball-reference.com. The data can then be saved into a csv to be used by a different utility.

About the Package

What data are we collecting?

• games & game stats (in depth stats of the games)
• players game stats

All datasets are available to be collected:

• by day (all games in one day)
• by whole season (regular + playoffs)
• by playoffs

Future Collections (Not yet implemented)

• players meta data (Not Implemented)
• game logs (Not Implemented)

How to Install & Run the Package?

Install the project

pip install baskref

# optional set logging level. Default value is INFO
export LOG_LEVEL=DEBUG # INFO, DEBUG, ERROR

Scrape Game Data

Scrape all games for the 7th of January 2022.

baskref -t g -d 2022-01-07 -fp datasets
# python -c "from baskref import run_baskref; run_baskref()" -t g -d 2022-01-07 -fp datasets

Scrape all games for the 2006 NBA season (regular season + playoffs).

baskref -t gs -y 2006 -fp datasets
# python -c "from baskref import run_baskref; run_baskref()" -t gs -y 2006 -fp datasets

Scrape all games for the 2006 NBA playoffs.

baskref -t gp -y 2006 -fp datasets
# if you don't install the package
# python -c "from baskref import run_baskref; run_baskref()" -t gp -y 2006 -fp datasets

Scrape Game URLs only

# simply add "u" to any of the three scraping types:
# g -> gu, gs -> gsu, gp -> gpu
baskref -t gu -d 2022-01-07 -fp datasets

Scrape Player Stats Data

# simply add "pl" to any of the three scraping types:
# g -> gpl, gs -> gspl, gp -> gppl
baskref -t gpl -d 2022-01-07 -fp datasets

Scrape Using a Proxy

Use proxy for scraping.

baskref -t g -d 2022-01-07 -fp datasets -p http://someproxy.com

How to Use the Package?

Install requirements

pip install -r requirements.txt

Data Collection Utility

This refers to the scraping functionalities.

For any mode of collection first you need to import and initialize the below classes.

from baskref.data_collection import (
    BaskRefUrlScraper,
    BaskRefDataScraper,
)

url_scraper = BaskRefUrlScraper()
data_scraper = BaskRefDataScraper()

# optionally you can set a proxy
proxy_url_scraper = BaskRefUrlScraper("http://someproxy.com")
proxy_data_scraper = BaskRefDataScraper("http://someproxy.com")

The BaskRefDataScraper.get_games_data returns a list of dictionaries.

Collect games for a specific day

from datetime import date

game_urls = url_scraper.get_game_urls_day(date(2022,1,7))
game_data = data_scraper.get_games_data(game_urls)

Collect games for a specific season (regular + playoffs)

game_urls = url_scraper.get_game_urls_year(2006)
game_data = data_scraper.get_games_data(game_urls)

Collect games for a specific postseason

game_urls = url_scraper.get_game_urls_playoffs(2006)
game_data = data_scraper.get_games_data(game_urls)

Collect player stats for for a specific day

from datetime import date

game_urls = url_scraper.get_game_urls_day(date(2022,1,7))
pl_stats_data = data_scraper.get_player_stats_data(game_urls)

Data Saving Package

This refers to the saving of the data.

Save a list of dictionaries to a CSV file.

import os
from baskref.data_saving.file_saver import save_file_from_list

save_path = os.path.join('datasets', 'file_name.csv')
save_file_from_list(game_data, save_path)

How to Run Tests?

Run all tests with Pytest

pytest

Run all tests with coverage

coverage run --source=baskref -m pytest
coverage report --omit="*/test*" -m --skip-empty

Code Formating

The code base uses black for automatic formating. the configuration for black is stored in pyproject.toml file.

# run black over the entire code base
black .

Linting

The code base uses pylint and mypy for code linting.

Pylint

the configuration for pylint is stored in .pylintrc file.

# run pylint over the entire code base
pylint --recursive=y ./

MyPy

the configuration for mypy is stored in pyproject.toml file.

# run mypy over the entire code base
mypy baskref

Bonus

Prepare project for development

1. Create Virtual Environment

• You might want to use a virtual environment for executing the project.
• this is an optional step (if skipping go straight to step 2)

Create a new virtual environemnt

python -m venv venv  # The second parameter is a path to the virtual env.

Activate the new virtual environment

# Windows
.\venv\Scripts\activate

# Unix
source venv/bin/activate

Leaving the virtual environment

deactivate

2. Install all the dev requirements

pip install -r requirements_dev.txt

# uninstall all packages Windows
pip freeze > unins && pip uninstall -y -r unins && del unins

# uninstall all packages linux
pip freeze | xargs pip uninstall -y

3. Install the pre-commit hook

pre-commit install

Prepare a new Version

This section describes some of the steps when preparing a new baskref version.

• empty the dist folder

rm -rf dist/*

• adjust the pyproject.toml file

  • version
  • dependencies

• install project locally and test it

python -m build
pip install .

• install twine

pip install --upgrade twine

• publish project to test.pypi (optional)

twine upload --repository testpypi dist/*
# install from test.pypi
pip install --index-url https://test.pypi.org/simple/ baskref

• publish a new version

twine upload dist/*

Contributors

1. Dominik Zulovec Sajovic