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        "author": "Jonathan Harrington",
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            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.7",
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        "description": "============================\nMFrame - A minimal DataFrame\n============================\n\n\nA lightweight single file DataFrame implementation that works on older Python distrubtions such as Jython.\n\nI use it with Java data tools such as `Streamsets <https://streamsets.com/>`_.\n\nFeel free to fork, add tests and features and make a pull request.\n\nInstall\n=======\n\n\n $ pip install mframe\n\n\nor copy mframe.py to your project folder.\n\nUsage\n=====\n\nIt's goal is to be familar to pandas users without promising 100% compatability. My workflow usually involves writing the code in a Jupyter notebook using Python 3 and then testing it with Jython before deploying it to Streamsets.\n\n\n    >>> from mframe import DataFrame\n    >>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}\n    >>> df = DataFrame(data)\n    >>> df\n    {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}\n\n    >>> df.pd #  the pd alias returns a pandas dataframe, useful for printing in Jupyter when developing\n         col_1 col_2\n    0      3     a\n    1      2     b\n    2      1     c\n    3      0     d\n\n    >>> df['col_1'] # Subscript access\n    [3, 2, 1, 0]\n\n    >>> df.col_1 # Attribute access\n    [3, 2, 1, 0]\n\n    >>> df[df.col_1 > 1] # filtering works\n         col_1 col_2\n    0      3     a\n    1      2     b\n    >>> df[(df.col_1 > 1) & (df.col_2 == 'a')]\n         col_1 col_2\n    0      3     a\n\n    >>> df['col_1'] = df.col_1.apply(str) # Apply is available\n    >>> df.col_1\n    ['3', '2', '1', '0']\n\n    >>> list(df.iterrows()) # returns a generator of dictionaries\n    [{'col_1': '3', 'col_2': 'a'}, {'col_1': '2', 'col_2': 'b'}, {'col_1': '1', 'col_2': 'c'}, {'col_1': '0', 'col_2': 'd'}]\n\nTested on\n=========\n\n- Python 3.7\n- Jython 2.7\n\n",
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