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        "bugtrack_url": null,
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            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Software Development :: Libraries"
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        "description": "**Table**\n    Think of this as a list of namedtuples, except the elements of the namedtuples\n    can be edited.  The table is initialized with a list of column header names.\n    Appending to the table adds new rows.  Rows are created using a list (positional)\n    or dict (column names).  Unspecified column cells default to ``None``.\n    \n    Other list methods (``insert``, ``pop``, ``remove``, ``extend``, ``reverse``) work as expected.\n    ``sort`` allows a string key to indicate a sort-by-column.  Search methods\n    (``index``, ``count``, ``__contains__``) accept either a Row object or a list or dict.\n    \n    Additional methods useful for tables:\n    \n    * ``take(indexes_or_func)`` returns a new table based on passed in indexes\n      Alternatively, applies a function to every row in the table and builds\n      a new table out of rows that evaluate to True\n    * ``column(colname)`` returns an iterator over values in a column\n    \n**LookupTable**\n    A lookup function or column is added to a basic Table.  The lookup function is similar\n    to a hash function, taking a Row object and returning a value.  If a column is provided,\n    the value in that column is the *lookup* value.  A dict keeps track of these \"lookup\"\n    values, allowing fast lookups on all seach methods (``index``, ``count``, ``__contains__``).\n    These search methods accept either a Row object (which is run through the lookup function)\n    or the *lookup* value.  The *lookup* values do not need to be unique.\n\nExample\n-------\n\n::\n\n    from datetime import datetime, date, timedelta\n    from roundtable import Table\n    # Create empty table with column headers\n    tbl = Table(['Timestamp', 'Event', 'Root Cause', 'Due Date'])\n    # Add rows to the table\n    tbl.append((datetime(2013,1,2,12,30), 'Error code 129',\n                'Short on board', date(2013,1,8)))\n    tbl.append({'Event': 'Pairwise testing',\n                'Due Date': date(2013,1,7)}) # other columns default to None\n    # Build a sorted list of tasks due in the next week\n    task_list = tbl.take(lambda row: row['Due Date'] - date.today() < timedelta(days=7))\n    task_list.sort(col='Due Date', reverse=True)\n\n    \nTable access methods:\n\n============================== ===========\nCode                           Return Type\n============================== ===========\n``mytable[0]``                 Row object\n``mytable[-1]``                Row object\n``mytable[0:10:2]``            new Table object\n``mytable[0][0]``              value in cell\n``mytable[0][-1]``             value in cell\n``mytable[0, 0]``              value in cell\n``mytable[0]['a column']``     value in cell\n``mytable[0, 'a column']``     value in cell\n``mytable[0].Col2``            value in cell*\n``mytable.column(1)``          iterator over values in column\n``mytable.column('a column')`` iterator over values in column\n============================== ===========\n\n\\* As a special case, if a column name is a valid Python variable name and\n**starts with a capital letter** then an attribute will be added to the Row\nobject allowing access.  The requirement to start with a capital letter avoids\nconflicts with other attributes and functions of the Row object.\n\n\nTable objects can be transformed into other table-like objects if modules are available:\n\n- NumPy Array :      ``mytable.as_array()``\n- Pandas DataFrame:  ``mytable.as_dataframe()``\n\nNote\n----\n\nWorks for Python 2.6+, 3.1+\n\nNo dependencies except when using the ``as_array()`` or ``as_dataframe()`` methods.",
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