dataforgoodfr/13_reveler_inegalites_cinema
Feb 2025 – Jul 2025
6 Months active
Languages Used
Jupyter NotebookPythonBashCSVDockerfileINIMarkdownSQL
Technical Skills
API IntegrationData ScrapingJupyter NotebooksPythonTestingAPI Development
Fitzfoufou
PROFILE
Fitzfoufou
Over six months, contributed to the dataforgoodfr/13_reveler_inegalites_cinema repository by building and refining a robust backend for cinema data analysis. Developed scalable data models, API endpoints, and automated data pipelines using Python, FastAPI, and SQLAlchemy, with Docker and CI/CD for deployment reliability. Enhanced data quality through enrichment workflows, standardized seeding scripts, and rigorous testing, supporting analytics and machine learning features. Integrated external sources like Allocine and CNC, implemented utility modules for parsing and validation, and maintained comprehensive documentation. Addressed bugs and optimized database interactions, ensuring accurate, reproducible data ingestion and reporting for research on cinema inequality and metadata analysis.
Overall Statistics
Feature vs Bugs
65%Features
Repository Contributions
139Total
Bugs
39
Commits
139
Features
71
Lines of code
65,403
Activity Months6
Your Network
12 peopleShared Repositories
12
Audrey DuranMember
Florian_YunMember
hadrien PoinseauxMember
ndretexMember
Julien-CommesMember
LudivineLacourMember
Nicolas RevelMember
Paul GabrielMember
lina kaciMember
Work History
July 2025
1 Commits
Jul 1, 2025July 2025: Maintained dataforgoodfr/13_reveler_inegalites_cinema with a targeted bug fix to improve data quality in the film credits pipeline. The change ensures accurate role naming in film credits, supporting reliable analytics and downstream reporting.
1 Commits
Jul 1, 2025July 2025: Maintained dataforgoodfr/13_reveler_inegalites_cinema with a targeted bug fix to improve data quality in the film credits pipeline. The change ensures accurate role naming in film credits, supporting reliable analytics and downstream reporting.
July 2025
June 2025
38 Commits • 22 Features
Jun 1, 2025June 2025 delivered substantial improvements in data quality, metadata enrichment, and data pipeline reliability for the dataforgoodfr/13_reveler_inegalites_cinema project. The work focused on Allocine and CNC seed data, standardization efforts, and groundwork for ML features, driving downstream analytics and reporting efficiency.
June 2025
38 Commits • 22 Features
Jun 1, 2025June 2025 delivered substantial improvements in data quality, metadata enrichment, and data pipeline reliability for the dataforgoodfr/13_reveler_inegalites_cinema project. The work focused on Allocine and CNC seed data, standardization efforts, and groundwork for ML features, driving downstream analytics and reporting efficiency.
May 2025
27 Commits • 8 Features
May 1, 2025May 2025: Delivered core data and pipeline improvements for dataforgoodfr/13_reveler_inegalites_cinema, focusing on value delivery, data reliability, and deployment velocity. Key features include robust name utilities, date parsing, and an Allocine data import seed script; a refactor of film detail retrieval to optimize queries; and stabilization of CI/CD and production configurations. These changes reduce data pipeline fragility, speed up data ingestion and dashboard access, and enable safer, repeated releases.
27 Commits • 8 Features
May 1, 2025May 2025: Delivered core data and pipeline improvements for dataforgoodfr/13_reveler_inegalites_cinema, focusing on value delivery, data reliability, and deployment velocity. Key features include robust name utilities, date parsing, and an Allocine data import seed script; a refactor of film detail retrieval to optimize queries; and stabilization of CI/CD and production configurations. These changes reduce data pipeline fragility, speed up data ingestion and dashboard access, and enable safer, repeated releases.
May 2025
April 2025
36 Commits • 19 Features
Apr 1, 2025April 2025 performance summary for dataforgoodfr/13_reveler_inegalites_cinema focused on data quality, enrichment, API enhancements, and deployment readiness. Key data-model improvements corrected film relations and refined attributes, enabling accurate graph queries and more reliable analytics. A new repositories layer was introduced to standardize and robustly create data, reducing duplication and drift. CNC seed workflows were strengthened with file-path handling, duplication prevention, and Excel sanitization, plus the addition of a fresh CNC 2024 dataset to expand test data coverage. External data enrichment progressed with an Allocine scraping flow to obtain IDs, film details, and casting, complemented by new Allocine CSV data and role-level allocine_name fields. API and discovery features expanded with a film fiche route, enhanced film search (including directors), and duration exposure in film details, alongside query performance improvements (index on original_name) and metabase-friendly table prefixing. Finally, improved observability and deployment readiness were established via a dedicated get_film_details metrics service, trailer/poster metrics, sample/demo data for testing/ML, and Docker/CI updates (Dockerfile fix, Docker Compose volumes, dependency updates).
April 2025
36 Commits • 19 Features
Apr 1, 2025April 2025 performance summary for dataforgoodfr/13_reveler_inegalites_cinema focused on data quality, enrichment, API enhancements, and deployment readiness. Key data-model improvements corrected film relations and refined attributes, enabling accurate graph queries and more reliable analytics. A new repositories layer was introduced to standardize and robustly create data, reducing duplication and drift. CNC seed workflows were strengthened with file-path handling, duplication prevention, and Excel sanitization, plus the addition of a fresh CNC 2024 dataset to expand test data coverage. External data enrichment progressed with an Allocine scraping flow to obtain IDs, film details, and casting, complemented by new Allocine CSV data and role-level allocine_name fields. API and discovery features expanded with a film fiche route, enhanced film search (including directors), and duration exposure in film details, alongside query performance improvements (index on original_name) and metabase-friendly table prefixing. Finally, improved observability and deployment readiness were established via a dedicated get_film_details metrics service, trailer/poster metrics, sample/demo data for testing/ML, and Docker/CI updates (Dockerfile fix, Docker Compose volumes, dependency updates).
March 2025
35 Commits • 21 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_reveler_inegalites_cinema: Delivered a production-ready backend foundation, reproducible local testing, and scalable data model expansions. Key deliverables include a Dockerized testing environment, a FastAPI + Uvicorn web API core, ORM and migrations with SQLAlchemy/Psycopg/Alembic, and comprehensive documentation updates. Business value includes faster onboarding, reliable local testing, scalable data ingestion and migrations, and improved developer productivity.
35 Commits • 21 Features
Mar 1, 2025March 2025 monthly summary for dataforgoodfr/13_reveler_inegalites_cinema: Delivered a production-ready backend foundation, reproducible local testing, and scalable data model expansions. Key deliverables include a Dockerized testing environment, a FastAPI + Uvicorn web API core, ORM and migrations with SQLAlchemy/Psycopg/Alembic, and comprehensive documentation updates. Business value includes faster onboarding, reliable local testing, scalable data ingestion and migrations, and improved developer productivity.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Focused on expanding test coverage for the Bechdelai library by introducing Jupyter notebooks to validate scraping modules across multiple sources (IMSDB, IMDB, BechdelTest, Allocine, OpenSubtitles, TMDB, Wikipedia, Scenarioteque) within dataforgoodfr/13_reveler_inegalites_cinema. IMSDB integration is functional; other sources require configuration/API keys. No major bugs fixed this month; primarily establishing prerequisites and a testing workflow to enable faster validation and regression checks. Business impact: improves data quality assurance for multi-source scraping, enabling safer, faster data collection for research on cinema inequality. Technologies: Python, Jupyter notebooks, Git, data scraping, API key management, and notebook-based testing.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025: Focused on expanding test coverage for the Bechdelai library by introducing Jupyter notebooks to validate scraping modules across multiple sources (IMSDB, IMDB, BechdelTest, Allocine, OpenSubtitles, TMDB, Wikipedia, Scenarioteque) within dataforgoodfr/13_reveler_inegalites_cinema. IMSDB integration is functional; other sources require configuration/API keys. No major bugs fixed this month; primarily establishing prerequisites and a testing workflow to enable faster validation and regression checks. Business impact: improves data quality assurance for multi-source scraping, enabling safer, faster data collection for research on cinema inequality. Technologies: Python, Jupyter notebooks, Git, data scraping, API key management, and notebook-based testing.
Activity
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Quality Metrics
Correctness90.6%
Maintainability91.2%
Architecture88.4%
Performance85.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashCSSCSVDockerfileGitHTMLINIJupyter NotebookMarkdownNginx
Technical Skills
API DevelopmentAPI IntegrationAlembicAlembic MigrationsAsyncIOAsynchronous ProgrammingBackend DevelopmentBeautifulSoupBrowser AutomationCI/CDCSV HandlingCSV ProcessingCode MaintenanceCode OrganizationConfiguration
Repositories Contributed To
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