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engineering-nba-data

Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.

适用职业数据分析师
License 许可
MIT
最近更新
2026-06-24
Stars
★ 66
Forks
24

01 前言 Front Matter

name
engineering-nba-data
description
Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.

02 资源文件 301

03 SKILL.md 正文

Goal: Extract and process NBA statistical data efficiently using the nba_api library for data analysis, reporting, and application development.

IMPORTANT: The nba_api library accesses stats.nba.com endpoints. All data requests return structured datasets that can be output as JSON, dictionaries, or pandas DataFrames.

Workflow

Phase 1: Setup and Installation

  • Install nba_api: pip install nba_api if not yet installed
  • Import required modules based on task:
    • from nba_api.stats.endpoints import [endpoint_name] for stats.nba.com data
    • from nba_api.stats.static import players, teams for static lookups
    • from nba_api.stats.library.parameters import [parameter_classes] for valid parameter values

Phase 2: Data Retrieval

For Player/Team Lookups (No API Calls):

  • Use players.find_players_by_full_name('player_name') for player searches
  • Use teams.find_teams_by_full_name('team_name') for team searches
  • Both return dictionaries with id, full_name, and other metadata
  • No HTTP requests are sent; data is embedded in the package

For Stats Endpoints (API Calls):

  • Identify the correct endpoint from table of contents
  • Initialize endpoint with required parameters: endpoint_class(param1=value1, param2=value2)
  • Access datasets using dot notation: response_object.dataset_name
  • Retrieve data in desired format:
    • .get_json() for JSON string
    • .get_dict() for dictionary
    • .get_data_frame() for pandas DataFrame

Custom Request Configuration:

  • Set custom headers: endpoint_class(player_id=123, headers=custom_headers)
  • Set proxy: endpoint_class(player_id=123, proxy='127.0.0.1:80')
  • Set timeout: endpoint_class(player_id=123, timeout=100) (in seconds)

Phase 3: Data Processing

  • Extract specific datasets from endpoint responses
  • Transform data using pandas for aggregations, filtering, joins
  • Normalize nested data structures as needed
  • Handle multiple datasets returned by single endpoint

Phase 4: Output and Storage

  • Export to CSV: df.to_csv('output.csv', index=False)
  • Export to JSON: Use .get_json() or df.to_json()
  • Store in database using pandas .to_sql() method
  • Cache responses to minimize API calls

Rules

  • Required packages: nba_api must be installed before use
  • Static first: Always use static lookups (players/teams) for ID retrieval before making API calls
  • Parameter validation: Reference parameters.md for valid parameter values
  • Endpoint selection: Check table of contents to find the correct endpoint
  • Rate limiting: Be mindful of API rate limits; cache data when possible
  • Error handling: Wrap API calls in try-except blocks to handle network failures
  • Data formats: Know when to use JSON, dict, or DataFrame based on downstream requirements
  • Season format: Seasons use format YYYY-YY (e.g., 2019-20)
  • League IDs: NBA=00, ABA=01, WNBA=10, G-League=20

Acceptance Criteria

  • Data retrieved successfully from appropriate endpoint or static source
  • Correct parameters used based on documentation
  • Data formatted appropriately for intended use case
  • Error handling implemented for API failures
  • Code follows Python best practices
  • Results validated against expected structure
  • Documentation references included where relevant

Reference Documentation

Quick access to common resources:

Endpoint-specific documentation:

Refer to docs/nba_api/stats/endpoints/[endpoint_name].md for detailed parameter and dataset information for each endpoint.

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