[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ETQ3M1RAZAS9AGEA5JP3Q2":3,"skill-same:01M6ETQ3M1RAZAS9AGEA5JP3Q2":1275},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":52,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6ETQ3M1RAZAS9AGEA5JP3Q2","engineering-nba-data",null,"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\u002Fteam lookups.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Femz1998\u002Fengineering-nba-data\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"engineering nba data","engineering","data","Charts","with","transforms","statistics","endpoints","use","game","team","python","api","logs","stats","library","data engineering","tasks","2026-11-10 10:12:30",false,"---\nname: engineering-nba-data\ndescription: 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\u002Fteam lookups.\n---\n\n**Goal**: Extract and process NBA statistical data efficiently using the nba_api library for data analysis, reporting, and application development.\n\n**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.\n\n## Workflow\n\n### Phase 1: Setup and Installation\n\n- Install nba_api: `pip install nba_api` if not yet installed\n- Import required modules based on task:\n  - `from nba_api.stats.endpoints import [endpoint_name]` for stats.nba.com data\n  - `from nba_api.stats.static import players, teams` for static lookups\n  - `from nba_api.stats.library.parameters import [parameter_classes]` for valid parameter values\n\n### Phase 2: Data Retrieval\n\n**For Player\u002FTeam Lookups (No API Calls)**:\n\n- Use `players.find_players_by_full_name('player_name')` for player searches\n- Use `teams.find_teams_by_full_name('team_name')` for team searches\n- Both return dictionaries with `id`, `full_name`, and other metadata\n- No HTTP requests are sent; data is embedded in the package\n\n**For Stats Endpoints (API Calls)**:\n\n- Identify the correct endpoint from [table of contents](docs\u002Ftable_of_contents.md)\n- Initialize endpoint with required parameters: `endpoint_class(param1=value1, param2=value2)`\n- Access datasets using dot notation: `response_object.dataset_name`\n- Retrieve data in desired format:\n  - `.get_json()` for JSON string\n  - `.get_dict()` for dictionary\n  - `.get_data_frame()` for pandas DataFrame\n\n**Custom Request Configuration**:\n\n- Set custom headers: `endpoint_class(player_id=123, headers=custom_headers)`\n- Set proxy: `endpoint_class(player_id=123, proxy='127.0.0.1:80')`\n- Set timeout: `endpoint_class(player_id=123, timeout=100)` (in seconds)\n\n### Phase 3: Data Processing\n\n- Extract specific datasets from endpoint responses\n- Transform data using pandas for aggregations, filtering, joins\n- Normalize nested data structures as needed\n- Handle multiple datasets returned by single endpoint\n\n### Phase 4: Output and Storage\n\n- Export to CSV: `df.to_csv('output.csv', index=False)`\n- Export to JSON: Use `.get_json()` or `df.to_json()`\n- Store in database using pandas `.to_sql()` method\n- Cache responses to minimize API calls\n\n## Rules\n\n- **Required packages**: `nba_api` must be installed before use\n- **Static first**: Always use static lookups (players\u002Fteams) for ID retrieval before making API calls\n- **Parameter validation**: Reference [parameters.md](docs\u002Fnba_api\u002Fstats\u002Flibrary\u002Fparameters.md) for valid parameter values\n- **Endpoint selection**: Check [table of contents](docs\u002Ftable_of_contents.md) to find the correct endpoint\n- **Rate limiting**: Be mindful of API rate limits; cache data when possible\n- **Error handling**: Wrap API calls in try-except blocks to handle network failures\n- **Data formats**: Know when to use JSON, dict, or DataFrame based on downstream requirements\n- **Season format**: Seasons use format `YYYY-YY` (e.g., `2019-20`)\n- **League IDs**: NBA=`00`, ABA=`01`, WNBA=`10`, G-League=`20`\n\n## Acceptance Criteria\n\n- Data retrieved successfully from appropriate endpoint or static source\n- Correct parameters used based on documentation\n- Data formatted appropriately for intended use case\n- Error handling implemented for API failures\n- Code follows Python best practices\n- Results validated against expected structure\n- Documentation references included where relevant\n\n## Reference Documentation\n\n**Quick access to common resources**:\n\n- [Table of Contents](docs\u002Ftable_of_contents.md) - Full documentation index\n- [Examples](docs\u002Fnba_api\u002Fstats\u002Fexamples.md) - Usage examples for endpoints and static data\n- [Parameters](docs\u002Fnba_api\u002Fstats\u002Flibrary\u002Fparameters.md) - Valid parameter values and patterns\n- [Endpoints Data Structure](docs\u002Fnba_api\u002Fstats\u002Fendpoints_data_structure.md) - Response format and methods\n- [Players](docs\u002Fnba_api\u002Fstats\u002Fstatic\u002Fplayers.md) - Static player lookup functions\n- [Teams](docs\u002Fnba_api\u002Fstats\u002Fstatic\u002Fteams.md) - Static team lookup functions\n- [HTTP Library](docs\u002Fnba_api\u002Flibrary\u002Fhttp.md) - HTTP request details\n\n**Endpoint-specific documentation**:\n\nRefer to `docs\u002Fnba_api\u002Fstats\u002Fendpoints\u002F[endpoint_name].md` for detailed parameter and dataset information for each endpoint.\n","**Goal**: Extract and process NBA statistical data efficiently using the nba_api library for data analysis, reporting, and application development.\n\n**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.\n\n## Workflow\n\n### Phase 1: Setup and Installation\n\n- Install nba_api: `pip install nba_api` if not yet installed\n- Import required modules based on task:\n  - `from nba_api.stats.endpoints import [endpoint_name]` for stats.nba.com data\n  - `from nba_api.stats.static import players, teams` for static lookups\n  - `from nba_api.stats.library.parameters import [parameter_classes]` for valid parameter values\n\n### Phase 2: Data Retrieval\n\n**For Player\u002FTeam Lookups (No API Calls)**:\n\n- Use `players.find_players_by_full_name('player_name')` for player searches\n- Use `teams.find_teams_by_full_name('team_name')` for team searches\n- Both return dictionaries with `id`, `full_name`, and other metadata\n- No HTTP requests are sent; data is embedded in the package\n\n**For Stats Endpoints (API Calls)**:\n\n- Identify the correct endpoint from [table of contents](docs\u002Ftable_of_contents.md)\n- Initialize endpoint with required parameters: `endpoint_class(param1=value1, param2=value2)`\n- Access datasets using dot notation: `response_object.dataset_name`\n- Retrieve data in desired format:\n  - `.get_json()` for JSON string\n  - `.get_dict()` for dictionary\n  - `.get_data_frame()` for pandas DataFrame\n\n**Custom Request Configuration**:\n\n- Set custom headers: `endpoint_class(player_id=123, headers=custom_headers)`\n- Set proxy: `endpoint_class(player_id=123, proxy='127.0.0.1:80')`\n- Set timeout: `endpoint_class(player_id=123, timeout=100)` (in seconds)\n\n### Phase 3: Data Processing\n\n- Extract specific datasets from endpoint responses\n- Transform data using pandas for aggregations, filtering, joins\n- Normalize nested data structures as needed\n- Handle multiple datasets returned by single endpoint\n\n### Phase 4: Output and Storage\n\n- Export to CSV: `df.to_csv('output.csv', index=False)`\n- Export to JSON: Use `.get_json()` or `df.to_json()`\n- Store in database using pandas `.to_sql()` method\n- Cache responses to minimize API calls\n\n## Rules\n\n- **Required packages**: `nba_api` must be installed before use\n- **Static first**: Always use static lookups (players\u002Fteams) for ID retrieval before making API calls\n- **Parameter validation**: Reference [parameters.md](docs\u002Fnba_api\u002Fstats\u002Flibrary\u002Fparameters.md) for valid parameter values\n- **Endpoint selection**: Check [table of contents](docs\u002Ftable_of_contents.md) to find the correct endpoint\n- **Rate limiting**: Be mindful of API rate limits; cache data when possible\n- **Error handling**: Wrap API calls in try-except blocks to handle network failures\n- **Data formats**: Know when to use JSON, dict, or DataFrame based on downstream requirements\n- **Season format**: Seasons use format `YYYY-YY` (e.g., `2019-20`)\n- **League IDs**: NBA=`00`, ABA=`01`, WNBA=`10`, G-League=`20`\n\n## Acceptance Criteria\n\n- Data 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