{"id":25409781,"url":"https://github.com/makepath/medaprep","last_synced_at":"2025-06-29T21:41:05.300Z","repository":{"id":56704745,"uuid":"511901364","full_name":"makepath/medaprep","owner":"makepath","description":"medaprep is a data preparation and feature engineering toolkit for geospatial applications.","archived":false,"fork":false,"pushed_at":"2022-08-24T23:26:42.000Z","size":805,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-14T11:25:38.134Z","etag":null,"topics":["data","data-science","datacleaning","eda","exploratory-data-analysis","xarray"],"latest_commit_sha":null,"homepage":"https://medaprep.readthedocs.io/en/latest/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/makepath.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-07-08T13:15:54.000Z","updated_at":"2023-01-25T16:30:49.000Z","dependencies_parsed_at":"2022-08-15T23:40:28.724Z","dependency_job_id":null,"html_url":"https://github.com/makepath/medaprep","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/makepath/medaprep","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/makepath%2Fmedaprep","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/makepath%2Fmedaprep/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/makepath%2Fmedaprep/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/makepath%2Fmedaprep/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/makepath","download_url":"https://codeload.github.com/makepath/medaprep/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/makepath%2Fmedaprep/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":261744853,"owners_count":23203285,"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":["data","data-science","datacleaning","eda","exploratory-data-analysis","xarray"],"created_at":"2025-02-16T09:28:11.615Z","updated_at":"2025-06-29T21:41:05.277Z","avatar_url":"https://github.com/makepath.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"medaprep\n#########\n\n|Documentation Status|\n\nmedaprep is used to prepare ``xarray`` Datasets for downstream tasks.\n\nUsage\n#####\n\nmedaprep.skim.features\n-----------------------\n\n.. code-block:: python\n\n    \u003e\u003e\u003e import numpy as np\n    \u003e\u003e\u003e import pandas as pd\n    \u003e\u003e\u003e import xarray as xr\n    \u003e\u003e\u003e from medaprep import skim\n    \u003e\u003e\u003e temp = 15 + 8 * np.random.randn(2, 2, 3)\n    \u003e\u003e\u003e precip = 10 * np.random.rand(2, 2, 3)\n    \u003e\u003e\u003e lon = [[-99.83, -99.32], [-99.79, -99.23]]\n    \u003e\u003e\u003e lat = [[42.25, 42.21], [42.63, 42.59]]\n    \u003e\u003e\u003e ds = xr.Dataset(\n      {\n          \"temperature\": ([\"x\", \"y\", \"time\"], temp),\n          \"precipitation\": ([\"x\", \"y\", \"time\"], precip),\n          },\n      coords={\n          \"lon\": ([\"x\", \"y\"], lon),\n          \"lat\": ([\"x\", \"y\"], lat),\n          \"time\": pd.date_range(\"2014-09-06\", periods=3),\n          \"reference_time\": pd.Timestamp(\"2014-09-05\"),\n          },\n                     )\n     \u003e\u003e\u003e df = skim.features(ds)\n     \u003e\u003e\u003e df\n         variables       data_types  NaNs    mean    std     maximums    minimums\n     0   temperature     float64     False   14.3177 9.08339 30.3361     -7.76803\n     1   precipitation   float64     False   4.62568 3.03081 9.89768     0.147005\n\nFor more details see `Documentation`_ and `Example Notebooks`_.\n\nInstallation\n############\n\nUsing pip\n---------\n\n.. code-block:: bash\n\n   pip install medaprep\n\nUsing Conda\n-----------\n\n.. code-block:: bash\n\n   conda install -c conda-forge medaprep\n\n\nDeveloping\n##########\n\npre-commit setup\n----------------\n\nThis project uses `pre-commit`, `isort`, `black`, and `flake8` to help enforce best practices. These libraries are all included in `requirements-dev.txt` and can be installed with `pip` by running:\n\n.. code-block:: bash\n   \n   pip install -r requirements-dev.txt\n\nOnce pre-commit is installed, install the hooks specified by the config file into `.git`:\n\n.. code-block:: bash\n\n   pre-commit install\n\nYou can then test pre-commit by running:\n\n.. code-block:: bash\n\n   pre-commit\n\n.. |Documentation Status| image:: https://readthedocs.org/projects/medaprep/badge/?version=latest\n    :target: https://medaprep.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. _`Documentation`: https://medaprep.readthedocs.io/\n\n.. _`Example Notebooks`: https://medaprep.readthedocs.io/en/latest/examples.html\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmakepath%2Fmedaprep","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmakepath%2Fmedaprep","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmakepath%2Fmedaprep/lists"}