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Documentation for metadata.py

Module Overview

The metadata.py module provides the Metadata class, which contains a dictionary mapping various representations of Major League Baseball (MLB) team names to their standard abbreviations. This mapping is essential for normalizing team names during data processing, ensuring consistency across the application.

Key Component

  • Team Abbreviations Mapping: A comprehensive dictionary that maps full team names, nicknames, and abbreviations to a standard three-letter abbreviation.

Class and Attribute

Metadata

Provides mappings from team names to their standard abbreviations.

Attributes

  • team_abbreviations (Dict[str, str]): A class variable that is a dictionary mapping various forms of team names to their standard three-letter abbreviations.

Example Usage

Accessing Team Abbreviations

You can access the team abbreviations directly from the Metadata class without instantiating it, as team_abbreviations is a class variable.

from metadata import Metadata

# Get the abbreviation for 'los angeles angels'
team_abbr = Metadata.team_abbreviations['los angeles angels']
print(team_abbr)  # Output: 'ana'

# Normalize a team name
team_name = 'Yankees'
normalized_team = Metadata.team_abbreviations.get(team_name.lower(), 'unknown')
print(normalized_team)  # Output: 'nya'

Understanding the Mapping

The team_abbreviations dictionary includes:

  • Full Team Names: e.g., 'boston red sox', 'atlanta braves'
  • Nicknames: e.g., 'red sox', 'braves'
  • City Names: e.g., 'boston', 'atlanta'
  • Abbreviations: e.g., 'bos', 'atl'
  • Historical Team Names: e.g., 'montreal expos'
  • Alternative Names: e.g., 'cal' for 'anaheim angels'

This comprehensive mapping ensures that different representations of team names are correctly mapped to a standard abbreviation, which is crucial for data consistency.

Additional Notes

  • Case Sensitivity: The keys in team_abbreviations are in lowercase. When accessing the dictionary, ensure that the team name is converted to lowercase to match the keys.

    team_name = 'Yankees'
normalized_team = Metadata.team_abbreviations.get(team_name.lower(), 'unknown')

  • Extensibility: You can extend the team_abbreviations dictionary by adding new mappings if needed.

    Metadata.team_abbreviations['new team name'] = 'new_abbr'

  • Usage in Data Processing: This mapping is often used in conjunction with data parsing functions to normalize team names extracted from various data sources like text, JSON, or CSV files.

Conclusion

The metadata.py module provides a vital utility for normalizing team names within the application. By using the Metadata class and its team_abbreviations attribute, developers can ensure consistency in team name representation, which is essential for accurate data processing and analysis.

Note: Ensure that when you use the Metadata class, the team names you provide as keys are in lowercase to match the dictionary keys. This practice helps avoid key mismatches due to case sensitivity.