Transfermarkt Datasets
C+ 76 completed
Data Tool
infrastructure / python · small
113
Files
5,611
LOC
1
Frameworks
7
Languages
Pipeline State
completedRun ID
#297505Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
70.24Framework unique
—Isolation
—Last stage change
2026-05-10 03:34:36Deduplication group #62121
Member of a group with 1 similar repo(s) — this repo is canonical view group →
Top concepts (9)
testingpresentationbusiness_logicdata_accessinfrastructureDatabaseFile ManagementTestingConfiguration
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🧪 Code Distillation
Browse all specs →Sample distilled functions (click for full spec)
submit_batch_job_and_waitSubmits a computational job to AWS Batch, requiring a job name, queue name, job definition, command list, allocated vCPUs, and memory, and optionally accepts a timeout in hours. It first retrieves the latest revision of the specified job definition, then submits the job using the provided parameters
seasons_listGenerates a list of integers representing years based on an input string detailing one or more seasons. It accepts a string that can represent a single year, a range of years separated by hyphens, or an invalid format. The function returns a list of integers containing the specified years, or raises
read_configReads project configuration data from a specified YAML file path, defaulting to "config.yml" if none is provided. It accepts one string argument representing the file location and returns the parsed configuration content as a Python dictionary. The function's notable side-effect is opening and readi
AI Prompt
Create a comprehensive Python data pipeline to extract, prepare, and publish Transfermarkt football datasets. I need the system to handle multiple data sources, structured into 10 distinct tables covering players, clubs, games, and transfers. The pipeline should be robust enough to be automatically updated and ideally use tools like dbt for transformation. Please structure the project to manage configuration via YAML and support data versioning.
python data-pipeline transfermarkt dbt sql yaml data-extraction sports-analytics
Generated by gemma4:latest
Catalog Information
This project extracts, prepares, publishes, and updates Transfermarkt datasets for use in various applications.
Description
Transfermarkt-datasets is a project that automates the process of extracting, preparing, publishing, and updating Transfermarkt datasets. It utilizes Python scripts to fetch data from Transfermarkt and store it in a structured format. The project leverages popular libraries such as pandas, plotly, and scipy for data manipulation and visualization. Streamlit is used to create an interactive interface for users to explore the datasets. This project aims to provide a convenient way to access and utilize Transfermarkt data.
الوصف
هذا المشروع يقوم بتحليل وتحويل وتنظيم بيانات ترنسفرماركت لاستخدامها في تطبيقات متعددة. يستخدم هذا المشروع سكريبتات بايثون لجلب البيانات من موقع ترنسفرماركت وتخزينها في صيغة منظمة. يعتمد المشروع على مكتبات شائعة مثل بانداس، بلاطلي، وسيباي، وستريمليت لتحليل البيانات والتصوير. هذا المشروع يسعى إلى توفير وسيلة سهلة للوصول إلى بيانات ترنسفرماركت.
Novelty
5/10Tags
data-extraction data-preparation data-publishing transfermarkt-data sports-data
Technologies
aws-sdk pandas plotly scipy streamlit
Claude Models
claude-opus-4.6 claude-sonnet-4.6
Quality Score
C+
75.8/100
Structure
79
Code Quality
100
Documentation
67
Testing
65
Practices
58
Security
65
Dependencies
90
Strengths
- CI/CD pipeline configured (github_actions)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Low average code complexity \u2014 well-structured code
- Containerized deployment (Docker)
- Properly licensed project
Security & Health
4.6h
Tech Debt (B)
Medium
DORA Rating
A
OWASP (100%)
Repobility · code-quality intelligence · https://repobility.com
PASS
Quality Gate
A
Risk (2)
CC0-1.0
License
0.9%
Duplication
Languages
Frameworks
pytest
Symbols
variable93
method40
function27
class20
constant12
property8
Concepts (9)
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| Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/ | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | presentation | Detected presentation layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | business_logic | Detected business_logic layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | data_access | Detected data_access layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | infrastructure | Detected infrastructure layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 26 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | File Management | Detected from 15 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 8 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Configuration | Detected from 4 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Faiohttp3.13.5 · 354 gadgets · risk 8061.4Nrequests2.33.1 · 0 gadgets · risk 3687.0Nasyncio4.0.0 · 0 gadgets · risk 0.0Nboto31.42.88 · 0 gadgets · risk 0.0Ninflection0.5.1 · 0 gadgets · risk 0.0Fnumpy2.4.4 · 6,596 gadgets · risk 0.0Fpandas3.0.2 · 6,381 gadgets · risk 0.0Nplotly6.7.0 · 0 gadgets · risk 0.0