usports-basketball 1.0.1

A Python package for USports basketball stats.

usports-basketball

usports-basketball is a Python package that fetches and analyzes current basketball stats from the U Sports website. With two simple functions—one for fetching team stats and the other for player stats—users can easily retrieve detailed data for either the men's or women's league. Each function returns a DataFrame with the relevant statistics statistics, allowing users to gather data for analysis and insights.

Installation

You can install the package via pip:

pip install usports-basketball

Dependencies

This package relies on the following dependencies:

• requests
• BeautifulSoup (bs4)
• pandas

Functions

usports_team_stats

This function fetches and processes team statistics data, including standings, win-loss totals, shooting percentages, and other relevant team metrics for the current U Sports basketball season.

Parameters

• arg (str): The league for which you want to retrieve team statistics. Valid values are 'men' and 'women'.

Returns

• DataFrame: A pandas DataFrame containing the team statistics data.

usports_player_stats

This function fetches and processes player statistics data, including total games played, points scored, shooting percentages, rebounds, assists, turnovers, steals, blocks, and other individual player metrics for the current U Sports basketball season.

Parameters

• arg (str): The league for which you want to retrieve player statistics. Valid values are 'men' and 'women'.

Returns

• DataFrame: A pandas DataFrame containing the player statistics data.

Examples

• Fetching current stats from the league

from usports_basketball import usports_team_stats, usports_player_stats

# Fetching and processing men's team statistics
men_team_stats_df = usports_team_stats('men')

# Fetching and processing men's player statistics
men_player_stats_df = usports_player_stats('men')

# Fetching and processing women's team statistics
women_team_stats_df = usports_team_stats('women')

# Fetching and processing women's player statistics
women_player_stats_df = usports_player_stats('women')

• Viewing Column Names

# Use these code snippets to see all column names in each DataFrame
# For the team statistics DataFrame
print("Column names in men's player statistics DataFrame:")
print(men_player_stats_df.columns.tolist())

# For the player statistics DataFrame
print("\nColumn names in men's player statistics DataFrame:")
print(men_player_stats_df.columns.tolist())

• Getting top 10 scorers in OUA Conference (Men's League)

# Initialize a DataFrame to store OUA teams
oua_teams_df = women_team_stats_df[women_team_stats_df['conference'].isin(['OUA East', 'OUA West', 'OUA Central'])][['team_name']].copy()
# Filter the DataFrame to include only players from the OUA conference
oua_players_df = men_player_stats_df[men_player_stats_df['school'].isin(oua_teams_df['team_name'])]

# Calculate points per game (PPG) for each player. Rounded to 2 decimal places
oua_players_df['points_per_game'] = round(oua_players_df['total_points'] / oua_players_df['games_played'], 2)

# Sort the DataFrame by points per game in descending order
oua_players_df = oua_players_df.sort_values(by='points_per_game', ascending=False)

# Retrieve the top 10 players leading in points per game
top_10_players = oua_players_df.head(10)

# Print the top 10 players
print(top_10_players[['lastname_initials', 'first_name', 'school', 'points_per_game']].to_string(index=False))

• Getting teams who have at least 5 games in a row (Women's League)

# Filter the DataFrame to include only teams with a streak of 5+ wins
teams_with_win_streak = women_team_stats_df[women_team_stats_df['streak'].str.startswith('W', na=False)].copy()

# Convert 'streak' column to numeric for comparison
teams_with_win_streak['streak'] = teams_with_win_streak['streak'].str.extract('(\d+)').astype(float)

# Filter teams with streak of 5+ wins
teams_with_5plus_wins = teams_with_win_streak[teams_with_win_streak['streak'] >= 5]

# Print teams with streak of 5+ wins
print("Teams with streak of 5+ wins:")
print(teams_with_5plus_wins[['team_name', 'games_played', 'conference', 'win_percentage']])

• Exporting DataFrame to different files

# Export the  to a CSV file using the `to_csv()` method
men_team_stats_df.to_csv('men_team_stats.csv', index=False)  # Export without row index

#Export to a HTML table representation using to_html method
men_team_stats_df.to_html('table.html')

#Export to a JSON file using the to_json() method
men_team_stats_df.to_json('men_team_stats.json', orient='records') # Export in 'records' format

#Export to a SQL database using the to_sql() method (requires SQLAlchemy)
from sqlalchemy import create_engine

engine = create_engine('sqlite:///usports_stats.db') #Export to SQLite database
women_team_stats_df.to_sql('team_stats', con=engine, index=False) # Export without row index
women_player_stats_df.to_sql('player_stats', con=engine, index=False) # Export without row index

Explore Pandas DataFrame documentation for instructions on manipulating data and performing data analysis.

Author

This package was developed by OJ Adeyemi.

Contributing

Contributions, bug reports, and feature requests are welcome! Please feel free to open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License. See the LICENSE file for details. for details.