{
    "info": {
        "author": "Karik Isichei",
        "author_email": "karik.isichei@digital.justice.gov.uk",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# data_linter\n\nA python package that validates datasets against a metadata schema which is defined [here](https://github.com/moj-analytical-services/data_linter/blob/master/data_linter/data/metadata_jsonschema.json).\n\nIt performs the following checks:\n- Are the columns of the correct data types (or can they be converted without error using `pd.Series.astype` in the case of untyped data formats like `csv`)\n- Column names:\n    - Are the columns named correctly?\n    - Are they in the same order specified in the meta data\n    - Are there any missing columns?\n- Where a regex `pattern` is provided in the metadata,  does the actual data always fit the `pattern`\n- Where an `enum` is provided in the metadata, does the actual data contain only values in the `enum`\n- Where `nullable` is set to false in the metadata, are there really no nulls in the data?\n\nThe package also provides functionality to `impose_metadata_types_on_pd_df`, which allows the user to safely convert a pandas dataframe to the datatypes specified in the metadata.  This is useful in the case you have an untyped data file such as a `csv` and want to ensure it is conformant with the metadata.\n\n## Usage\n\nFor detailed information about how to use the package, please see the [demo repo](https://github.com/moj-analytical-services/data_linter_demo).  This includes an interactive tutorial that you can run in your web browser.\n\nHere's a very basic example\n\n```\nimport pandas as pd\nimport json\n\nfrom data_linter.lint import Linter\n\ndef read_json_from_path(path):\n    with open(path) as f:\n        return_json = json.load(f)\n    return return_json\n\nmeta = read_json_from_path(\"tests/meta/test_meta_cols_valid.json\")\ndf = pd.read_parquet(\"tests/data/test_parquet_data_valid.parquet\")\nl = Linter(df, meta)\nl.check_all()\nl.markdown_report()\n```",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/moj-analytical-services/data_linter",
        "keywords": "",
        "license": "MIT",
        "maintainer": "Karik Isichei",
        "maintainer_email": "karik.isichei@digital.justice.gov.uk",
        "name": "data-linter",
        "package_url": "https://pypi.org/project/data-linter/",
        "platform": "",
        "project_url": "https://pypi.org/project/data-linter/",
        "project_urls": {
            "Homepage": "https://github.com/moj-analytical-services/data_linter",
            "Repository": "https://github.com/moj-analytical-services/data_linter"
        },
        "release_url": "https://pypi.org/project/data-linter/0.1.0/",
        "requires_dist": [
            "great-expectations (>=0.7.0,<0.8.0)",
            "pandas (>=0.25.0,<0.26.0)",
            "parameterized (>=0.7.0,<0.8.0)",
            "tabulate (>=0.8.0,<0.9.0)",
            "pyarrow (>=0.14.0,<0.15.0)",
            "jsonschema (>=3.0.0,<3.1.0)"
        ],
        "requires_python": ">=3.6,<4.0",
        "summary": "A python package that validates datasets against a metadata schema",
        "version": "0.1.0"
    },
    "last_serial": 5737371,
    "releases": {
        "0.1.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "1f29ac40c43eac4b8dd74ffaec860e65",
                    "sha256": "ea4920005c62f89a3daaa4c3357dfe64e0373ec321c8f14b39257fdf0a0d2139"
                },
                "downloads": -1,
                "filename": "data_linter-0.1.0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "1f29ac40c43eac4b8dd74ffaec860e65",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.6,<4.0",
                "size": 13136,
                "upload_time": "2019-08-27T15:04:01",
                "url": "https://files.pythonhosted.org/packages/49/a5/a0393051dfb52b00fd3df67717788ae0a4892a285270b4c56f39a986cbca/data_linter-0.1.0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "3c6f620617f15365d59c9a6a71eccac3",
                    "sha256": "9a4ea28e01c8459189051a778c9779fed85256f44c318f429f634a93d01110fb"
                },
                "downloads": -1,
                "filename": "data_linter-0.1.0.tar.gz",
                "has_sig": false,
                "md5_digest": "3c6f620617f15365d59c9a6a71eccac3",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6,<4.0",
                "size": 10650,
                "upload_time": "2019-08-27T15:04:04",
                "url": "https://files.pythonhosted.org/packages/c3/1e/5a0a2c964d2fa07b7a7d855c406eef9b36a03fefc85d01ff3b880b03a725/data_linter-0.1.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "1f29ac40c43eac4b8dd74ffaec860e65",
                "sha256": "ea4920005c62f89a3daaa4c3357dfe64e0373ec321c8f14b39257fdf0a0d2139"
            },
            "downloads": -1,
            "filename": "data_linter-0.1.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "1f29ac40c43eac4b8dd74ffaec860e65",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6,<4.0",
            "size": 13136,
            "upload_time": "2019-08-27T15:04:01",
            "url": "https://files.pythonhosted.org/packages/49/a5/a0393051dfb52b00fd3df67717788ae0a4892a285270b4c56f39a986cbca/data_linter-0.1.0-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "3c6f620617f15365d59c9a6a71eccac3",
                "sha256": "9a4ea28e01c8459189051a778c9779fed85256f44c318f429f634a93d01110fb"
            },
            "downloads": -1,
            "filename": "data_linter-0.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "3c6f620617f15365d59c9a6a71eccac3",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6,<4.0",
            "size": 10650,
            "upload_time": "2019-08-27T15:04:04",
            "url": "https://files.pythonhosted.org/packages/c3/1e/5a0a2c964d2fa07b7a7d855c406eef9b36a03fefc85d01ff3b880b03a725/data_linter-0.1.0.tar.gz"
        }
    ]
}