{
    "info": {
        "author": "Michael Tartre",
        "author_email": "michael@enkratic.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Software Development",
            "Topic :: Software Development :: Code Generators",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: User Interfaces",
            "Topic :: System :: Shells",
            "Topic :: Utilities"
        ],
        "description": "<img src=\"https://raw.githubusercontent.com/enkratic/sqwrl/master/docs/sqwrl.png\" width=\"200\" height=\"200\">\n\n# sqwrl\n### Sqlachemy Query WRapper Library\n\n### Quickstart\n\n```python\nimport pandas as pd\nfrom sqwrl import DB\ndb = DB('sqlite:///:memory:')\ndf = pd.DataFrame({\"x\": [1,2,3,4,5], \"y\": list(\"abcdf\"), \"z\": [1.0, 1.5, 1.5, 1.2, 1.3]}).set_index(\"y\")\nans_df = pd.read_csv(\"tests/anscombe.csv\")\ndb[\"anscombe\"] = ans_df\nans_tbl = db[\"anscombe\"]\nans_tbl\n```\n\nNow you can (mostly) use the sqwrl table object as if it were a pandas dataframe!\n\n```python\nans_tbl[ans_tbl[\"dataset\"].isin([\"I\", \"II\"])][[\"x\", \"y\"]]\n```\n\nUse the `.df` attribute on sqwrl table objects to read their output into pandas DataFrames for any unsupported features.\n\n```python\n>>> (ans_tbl.df == ans_df).all().all()\nTrue\n```\n\nSee [usage](https://nbviewer.jupyter.org/github/enkratic/sqwrl/blob/master/usage.ipynb) for more usage examples.\n\n---\n\n### Basic Features:\n - [x] Smart Pandas like wheres using getitem\n - [x] `.loc` operations\n - [x] Indexing (including reset_index, drops, and appending)\n - [x] Sorting\n - [x] Most arithmetic operations lazily pushed\n - [x] Metadata like length and dtypes\n\n### Advanced Features:\n - Groupby\n   - [x] Groupby group iteration\n   - [x] Groupby aggregation\n   - [ ] Groupby aggregation over multiple columns\n   - [ ] Groupby group sizes\n   - [ ] Groupby transform and apply\n - Joins\n   - [ ] Pandas-style joins (have the basic framework in place for this)\n - Mutation\n   - [ ] Adding, deleting, and editing columns\n   - [ ] Appending existing tables\n   - [ ] Deleting or editing based on conditions\n\n\n",
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        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "sqwrl",
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        "project_url": "https://pypi.org/project/sqwrl/",
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            "numpy",
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            "sympy",
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        "summary": "Sqlachemy Query WRapper Library - pandas-like SQL",
        "version": "0.0.1"
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