{
    "info": {
        "author": "G\u00f6khan Gerdan",
        "author_email": "gokhang1327@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# Aprioripy: Apriori algorithm.\n<img src=\"4742c1dc-c672-4194-96c3-54ee5b88a840.jpeg\">\n\n## Apriori algorithm usage:\n\n```python\nfrom apriopy import Apriopy\n\n\ntest_table = pd.DataFrame({\n    \"items\": [\"1, 3, 4\", \"2, 3, 5\", \"1, 2, 3, 5\", \"2, 5\"]\n})\n\nprint(\"Test table\")\nprint(test_table)\n\nap = Aprioripy(table=test_table)\n\nprint(\"\\nFrequency table:\")\nprint(ap.frequency_table)\n\nap.apriori(min_support=0.5)\n\nfor i in ap.association_tables.keys():\n    print(\"\\nAssociation table \" + i)\n    print(ap.association_tables[i])\n\ntest_table = pd.DataFrame(\n    [\n        {\"1\": 1, \"2\": 0, \"3\": 1, \"4\": 1, \"5\": 0},\n        {\"1\": 0, \"2\": 1, \"3\": 1, \"4\": 0, \"5\": 1},\n        {\"1\": 1, \"2\": 1, \"3\": 1, \"4\": 0, \"5\": 1},\n        {\"1\": 0, \"2\": 1, \"3\": 0, \"4\": 0, \"5\": 1}\n    ]\n)\n\nprint(\"\\nTest table:\")\nprint(test_table)\n\nap = Aprioripy(table=test_table, convert=False)\n\nprint(\"\\nFrequency table:\")\nprint(ap.frequency_table)\n\nap.apriori(min_support=0.5)\n\nfor i in ap.association_tables.keys():\n    print(\"\\nAssociation table \" + i)\n    print(ap.association_tables[i])\n```\n## Output:\n\n```\nTest table\n        items\n0     1, 3, 4\n1     2, 3, 5\n2  1, 2, 3, 5\n3        2, 5\n\nFrequency table:\n  item  frequency\n0    1       0.50\n1    2       0.75\n2    3       0.75\n3    4       0.25\n4    5       0.75\n\nAssociation table L1\n  item  frequency\n0    1       0.50\n1    2       0.75\n2    3       0.75\n4    5       0.75\n\nAssociation table L2\n  itemset  frequency\n1  (1, 3)       0.50\n4  (2, 3)       0.50\n6  (2, 5)       0.75\n8  (3, 5)       0.50\n\nAssociation table L3\n     itemset  frequency\n7  (2, 3, 5)        0.5\n\nTest table:\n   1  2  3  4  5\n0  1  0  1  1  0\n1  0  1  1  0  1\n2  1  1  1  0  1\n3  0  1  0  0  1\n\nFrequency table:\n  item  frequency\n0    1       0.50\n1    2       0.75\n2    3       0.75\n3    4       0.25\n4    5       0.75\n\nAssociation table L1\n  item  frequency\n0    1       0.50\n1    2       0.75\n2    3       0.75\n4    5       0.75\n\nAssociation table L2\n  itemset  frequency\n1  (1, 3)       0.50\n4  (2, 3)       0.50\n6  (2, 5)       0.75\n8  (3, 5)       0.50\n\nAssociation table L3\n     itemset  frequency\n7  (2, 3, 5)        0.5\n```\n\n\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/gokhangerdan/Aprioripy",
        "keywords": "",
        "license": "",
        "maintainer": "",
        "maintainer_email": "",
        "name": "aprioripy",
        "package_url": "https://pypi.org/project/aprioripy/",
        "platform": "",
        "project_url": "https://pypi.org/project/aprioripy/",
        "project_urls": {
            "Homepage": "https://github.com/gokhangerdan/Aprioripy"
        },
        "release_url": "https://pypi.org/project/aprioripy/0.0.2/",
        "requires_dist": null,
        "requires_python": ">=3.6",
        "summary": "Apriori algorithm.",
        "version": "0.0.2"
    },
    "last_serial": 5824161,
    "releases": {
        "0.0.2": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "d0de76adb280764366c4ff6518ac2807",
                    "sha256": "2d6078103dbb404e258513dcf5a5784671d23c6a5dec4a128f472f46840cf68e"
                },
                "downloads": -1,
                "filename": "aprioripy-0.0.2-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "d0de76adb280764366c4ff6518ac2807",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": ">=3.6",
                "size": 3650,
                "upload_time": "2019-09-13T06:50:25",
                "url": "https://files.pythonhosted.org/packages/cb/d9/7ff71b33b94262f19d6b43321b9135c33b3ff2aa77ebf958d24f28c064a9/aprioripy-0.0.2-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "50f6c689b1baebc6ec18d7549f14113b",
                    "sha256": "f14bcc0e9ef1d31b81cd55062057789754833d36f887ef9bf2151791a503980c"
                },
                "downloads": -1,
                "filename": "aprioripy-0.0.2.tar.gz",
                "has_sig": false,
                "md5_digest": "50f6c689b1baebc6ec18d7549f14113b",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 2561,
                "upload_time": "2019-09-13T06:50:27",
                "url": "https://files.pythonhosted.org/packages/d5/53/a0e9c036278a972e139e685a643a7f0bf6e97001c3519dbfa008c9191664/aprioripy-0.0.2.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "d0de76adb280764366c4ff6518ac2807",
                "sha256": "2d6078103dbb404e258513dcf5a5784671d23c6a5dec4a128f472f46840cf68e"
            },
            "downloads": -1,
            "filename": "aprioripy-0.0.2-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "d0de76adb280764366c4ff6518ac2807",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6",
            "size": 3650,
            "upload_time": "2019-09-13T06:50:25",
            "url": "https://files.pythonhosted.org/packages/cb/d9/7ff71b33b94262f19d6b43321b9135c33b3ff2aa77ebf958d24f28c064a9/aprioripy-0.0.2-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "50f6c689b1baebc6ec18d7549f14113b",
                "sha256": "f14bcc0e9ef1d31b81cd55062057789754833d36f887ef9bf2151791a503980c"
            },
            "downloads": -1,
            "filename": "aprioripy-0.0.2.tar.gz",
            "has_sig": false,
            "md5_digest": "50f6c689b1baebc6ec18d7549f14113b",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 2561,
            "upload_time": "2019-09-13T06:50:27",
            "url": "https://files.pythonhosted.org/packages/d5/53/a0e9c036278a972e139e685a643a7f0bf6e97001c3519dbfa008c9191664/aprioripy-0.0.2.tar.gz"
        }
    ]
}