{"id":24529969,"url":"https://github.com/jvelezmagic/cookiecutter-conda-data-science","last_synced_at":"2025-10-08T09:40:22.064Z","repository":{"id":44439214,"uuid":"350575167","full_name":"jvelezmagic/cookiecutter-conda-data-science","owner":"jvelezmagic","description":null,"archived":false,"fork":false,"pushed_at":"2021-07-23T03:24:05.000Z","size":75,"stargazers_count":50,"open_issues_count":1,"forks_count":37,"subscribers_count":3,"default_branch":"main","last_synced_at":"2023-03-26T02:21:18.701Z","etag":null,"topics":["conda","cookiecutter","cookiecutter-python3","cookiecutter-template","data-science","mamba","python","python3","template-project"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jvelezmagic.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-03-23T04:08:22.000Z","updated_at":"2023-03-24T23:22:27.000Z","dependencies_parsed_at":"2022-08-30T01:10:27.882Z","dependency_job_id":null,"html_url":"https://github.com/jvelezmagic/cookiecutter-conda-data-science","commit_stats":null,"previous_names":[],"tags_count":null,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvelezmagic%2Fcookiecutter-conda-data-science","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvelezmagic%2Fcookiecutter-conda-data-science/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvelezmagic%2Fcookiecutter-conda-data-science/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jvelezmagic%2Fcookiecutter-conda-data-science/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jvelezmagic","download_url":"https://codeload.github.com/jvelezmagic/cookiecutter-conda-data-science/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":235082228,"owners_count":18932920,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["conda","cookiecutter","cookiecutter-python3","cookiecutter-template","data-science","mamba","python","python3","template-project"],"created_at":"2025-01-22T07:53:15.968Z","updated_at":"2025-10-08T09:40:17.043Z","avatar_url":"https://github.com/jvelezmagic.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Cookiecutter Conda Data Science\n\n_A logical, reasonably standardized, but flexible project structure for doing and sharing data science work._\n\n## Requirements\n\n- [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html)\n- [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html): This can be installed with pip by or conda depending on how you manage your Python packages:\n\n``` bash\npip install cookiecutter\n```\n\nor\n\n``` bash\nconda install -c conda-forge cookiecutter\n```\n\n## Create a new project\n\nIn a folder where you want your project generated:\n\n```bash\ncookiecutter https://github.com/jvelezmagic/cookiecutter-conda-data-science\n```\n\n## Resulting directory structure\n\n    ├── LICENSE\n    ├── tasks.py           \u003c- Invoke with commands like `notebook`.\n    ├── README.md          \u003c- The top-level README for developers using this project.\n    ├── install.md         \u003c- Detailed instructions to set up this project.\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\n    │   ├── interim        \u003c- Intermediate data that has been transformed.\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │\n    ├── models             \u003c- Trained and serialized models, model predictions, or model summaries.\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\n    │\n    ├── references         \u003c- Data dictionaries, manuals, and all other explanatory materials.\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures         \u003c- Generated graphics and figures to be used in reporting.\n    │\n    ├── environment.yml    \u003c- The requirements file for reproducing the analysis environment.\n    │\n    ├── .here              \u003c- File that will stop the search if none of the other criteria\n    │                         apply when searching head of project.\n    │\n    ├── setup.py           \u003c- Makes project pip installable (pip install -e .)\n    │                         so {{ cookiecutter.project_module_name }} can be imported.\n    │\n    └── {{ cookiecutter.project_module_name }}               \u003c- Source code for use in this project.\n        ├── __init__.py    \u003c- Makes {{ cookiecutter.project_module_name }} a Python module.\n        │\n        ├── data           \u003c- Scripts to download or generate data.\n        │   └── make_dataset.py\n        │\n        ├── features       \u003c- Scripts to turn raw data into features for modeling.\n        │   └── build_features.py\n        │\n        ├── models         \u003c- Scripts to train models and then use trained models to make\n        │   │                 predictions.\n        │   ├── predict_model.py\n        │   └── train_model.py\n        │\n        ├── utils          \u003c- Scripts to help with common tasks.\n            └── paths.py   \u003c- Helper functions to relative file referencing across project.\n        │\n        └── visualization  \u003c- Scripts to create exploratory and results oriented visualizations.\n            └── visualize.py\n\n## Contributing guide\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.\n\n## Credits\n\nThis project is heavily influenced by [drivendata's Cookiecutter Data Science](https://github.com/drivendata/cookiecutter-data-science), [andfanilo's Cookiecutter for Kaggle Conda projects](https://github.com/andfanilo/cookiecutter-kaggle), and julia's package [DrWatson](https://juliadynamics.github.io/DrWatson.jl/dev/).\n\nOther links that helped shape this cookiecutter :\n\n- [Write less terrible code with Jupyter Notebook](https://blog.godatadriven.com/write-less-terrible-notebook-code)\n- [Cookiecutter DataScience Opinions](http://drivendata.github.io/cookiecutter-data-science/#opinions)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvelezmagic%2Fcookiecutter-conda-data-science","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjvelezmagic%2Fcookiecutter-conda-data-science","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjvelezmagic%2Fcookiecutter-conda-data-science/lists"}