Metadata-Version: 1.1
Name: meza
Version: 0.28.0
Summary: A Python toolkit for processing tabular data
Home-page: https://github.com/reubano/meza
Author: Reuben Cummings
Author-email: reubano@gmail.com
License: MIT
Download-URL: https://github.com/reubano/meza/archive/v0.28.0.tar.gz
Description: meza: A Python toolkit for processing tabular data
        ======================================================
        
        |travis| |versions| |pypi|
        
        Index
        -----
        
        `Introduction`_ | `Requirements`_ | `Motivation`_ | `Usage`_ | `Interoperability`_ |
        `Installation`_ | `Project Structure`_ | `Design Principles`_ | `Readers`_ |
        `Scripts`_ | `Contributing`_ | `Credits`_ | `License`_
        
        Introduction
        ------------
        
        **meza** is a Python `library`_ for reading and processing tabular data.
        It has a functional programming style API, excels at reading, large files,
        and can process 10+ file types.
        
        With meza, you can
        
        - Read csv/xls/xlsx/mdb/dbf files, and more!
        - Type cast records (date, float, text...)
        - Process Uñicôdë text
        - Lazily stream files by default
        - and much more...
        
        Requirements
        ------------
        
        meza has been tested and is known to work on Python 2.7, 3.4, and 3.5;
        PyPy 4.0; and PyPy3 2.4
        
        Optional Dependencies
        ^^^^^^^^^^^^^^^^^^^^^
        
        ===============================  ==============  ==============================  =======================
        Function                         Dependency      Installation                    File type / extension
        ===============================  ==============  ==============================  =======================
        ``meza.io.read_mdb``             `mdbtools`_     ``sudo port install mdbtools``   Microsoft Access / mdb
        ``meza.io.read_html``            `lxml`_ [#]_    ``pip install lxml``             HTML / html
        ``meza.convert.records2array``   `NumPy`_ [#]_   ``pip install numpy``            n/a
        ``meza.convert.records2df``      `pandas`_       ``pip install pandas``           n/a
        ===============================  ==============  ==============================  =======================
        
        Notes
        ^^^^^
        
        .. [#] If ``lxml`` isn't present, ``read_html`` will default to the builtin Python html reader
        
        .. [#] ``records2array`` can be used without ``numpy`` by passing ``native=True`` in the function call. This will convert ``records`` into a list of native ``array.array`` objects.
        
        Motivation
        ----------
        
        pandas is great, but installing it isn't exactly a `walk in the park`_. It also
        doesn't play nice with `PyPy`_. `csvkit`_ is an equally useful project, but it
        doesn't expose the same API when used as `a library`_ as it does via the command
        line. I designed **meza** to provide much of same functionality as
        pandas and csvkit, while using functional programming methods.
        
        A simple data processing example is shown below:
        
        First create a simple csv file (in bash)
        
        .. code-block:: bash
        
            echo 'col1,col2,col3\nhello,5/4/82,1\none,1/1/15,2\nhappy,7/1/92,3\n' > data.csv
        
        Now we can read the file, manipulate the data a bit, and write the manipulated
        data back to a new file.
        
        .. code-block:: python
        
            from meza import io, process as pr, convert as cv
            from io import open
        
            # Load the csv file
            records = io.read_csv('data.csv')
        
            # `records` are iterators over the rows
            row = next(records)
            row
            >>> {'col1': 'hello', 'col2': '5/4/82', 'col3': '1'}
        
            # Let's replace the first row so as not to loose any data
            records = pr.prepend(records, row)
        
            # Guess column types. Note: `detect_types` returns a new `records`
            # generator since it consumes rows during type detection
            records, result = pr.detect_types(records)
            {t['id']: t['type'] for t in result['types']}
            >>> {'col1': 'text', 'col2': 'date', 'col3': 'int'}
        
            # Now type cast the records. Note: most `meza.process` functions return
            # generators, so lets wrap the result in a list to view the data
            casted = list(pr.type_cast(records, result['types']))
            casted[0]
            >>> {'col1': 'hello', 'col2': datetime.date(1982, 5, 4), 'col3': 1}
        
            # Cut out the first column of data and merge the rows to get the max value
            # of the remaining columns. Note: since `merge` (by definition) will always
            # contain just one row, it is returned as is (not wrapped in a generator)
            cut_recs = pr.cut(casted, ['col1'], exclude=True)
            merged = pr.merge(cut_recs, pred=bool, op=max)
            merged
            >>> {'col2': datetime.date(2015, 1, 1), 'col3': 3}
        
            # Now write merged data back to a new csv file.
            io.write('out.csv', cv.records2csv(merged))
        
            # View the result
            with open('out.csv', 'utf-8') as f:
                f.read()
            >>> 'col2,col3\n2015-01-01,3\n'
        
        Usage
        -----
        
        meza is intended to be used directly as a Python library.
        
        Usage Index
        ^^^^^^^^^^^
        
        - `Reading data`_
        - `Processing data`_
        
          + `Numerical analysis (à la pandas)`_
          + `Text processing (à la csvkit)`_
          + `Geo processing (à la mapbox)`_
        
        - `Writing data`_
        - `Cookbook`_
        
        Reading data
        ^^^^^^^^^^^^
        
        meza can read both filepaths and file-like objects. Additionally, all readers
        return equivalent `records` iterators, i.e., a generator of dictionaries with
        keys corresponding to the column names.
        
        .. code-block:: python
        
            from io import open, StringIO
            from meza import io
        
            """Read a filepath"""
            records = io.read_json('path/to/file.json')
        
            """Read a file like object and de-duplicate the header"""
            f = StringIO('col,col\nhello,world\n')
            records = io.read_csv(f, dedupe=True)
        
            """View the first row"""
            next(records)
            >>> {'col': 'hello', 'col_2': 'world'}
        
            """Read the 1st sheet of an xls file object opened in text mode."""
            # Also, santize the header names by converting them to lowercase and
            # replacing whitespace and invalid characters with `_`.
            with open('path/to/file.xls', 'utf-8') as f:
                for row in io.read_xls(f, sanitize=True):
                    # do something with the `row`
                    pass
        
            """Read the 2nd sheet of an xlsx file object opened in binary mode"""
            # Note: sheets are zero indexed
            with open('path/to/file.xlsx') as f:
                records = io.read_xls(f, encoding='utf-8', sheet=1)
                first_row = next(records)
                # do something with the `first_row`
        
            """Read any recognized file"""
            records = io.read('path/to/file.geojson')
            f.seek(0)
            records = io.read(f, ext='csv', dedupe=True)
        
        Please see `Readers`_ for a complete list of available readers and recognized
        file types.
        
        Processing data
        ^^^^^^^^^^^^^^^
        
        Numerical analysis (à la pandas) [#]_
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In the following example, ``pandas`` equivalent methods are preceded by ``-->``,
        and command output is preceded by ``>>>``.
        
        .. code-block:: python
        
            import itertools as it
            import random
        
            from io import StringIO
            from meza import io, process as pr, convert as cv, stats
        
            # Create some data in the same structure as what the various `read...`
            # functions output
            header = ['A', 'B', 'C', 'D']
            data = [(random.random() for _ in range(4)) for x in range(7)]
            df = [dict(zip(header, d)) for d in data]
            df[0]
            >>> {'A': 0.53908..., 'B': 0.28919..., 'C': 0.03003..., 'D': 0.65363...}
        
            """Sort records by the value of column `B` --> df.sort_values(by='B')"""
            next(pr.sort(df, 'B'))
            >>> {'A': 0.53520..., 'B': 0.06763..., 'C': 0.02351..., 'D': 0.80529...}
        
            """Select column `A` --> df['A']"""
            next(pr.cut(df, ['A']))
            >>> {'A': 0.53908170489952006}
        
            """Select the first the rows of data --> df[0:3]"""
            len(list(it.islice(df, 3)))
            >>> 3
        
            """Select all data whose value for column `A` is less than 0.5
            --> df[df.A < 0.5]
            """
            next(pr.tfilter(df, 'A', lambda x: x < 0.5))
            >>> {'A': 0.21000..., 'B': 0.25727..., 'C': 0.39719..., 'D': 0.64157...}
        
            # Note: since `aggregate` and `merge` (by definition) return just one row,
            # they return them as is (not wrapped in a generator).
            """Calculate the mean of column `A` across all data --> df.mean()['A']"""
            pr.aggregate(df, 'A', stats.mean)['A']
            >>> 0.5410437473067938
        
            """Calculate the sum of each column across all data --> df.sum()"""
            pr.merge(df, pred=bool, op=sum)
            >>> {'A': 3.78730..., 'C': 2.82875..., 'B': 3.14195..., 'D': 5.26330...}
        
        Text processing (à la csvkit) [#]_
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In the following example, ``csvkit`` equivalent commands are preceded by ``-->``,
        and command output is preceded by ``>>>``.
        
        First create a few simple csv files (in bash)
        
        .. code-block:: bash
        
            echo 'col_1,col_2,col_3\n1,dill,male\n2,bob,male\n3,jane,female' > file1.csv
            echo 'col_1,col_2,col_3\n4,tom,male\n5,dick,male\n6,jill,female' > file2.csv
        
        Now we can read the files, manipulate the data, convert the manipulated data to
        json, and write the json back to a new file. Also, note that since all readers
        return equivalent `records` iterators, you can use them interchangeably (in
        place of ``read_csv``) to open any supported file. E.g., ``read_xls``,
        ``read_sqlite``, etc.
        
        .. code-block:: python
        
            import itertools as it
        
            from meza import io, process as pr, convert as cv
        
            """Combine the files into one iterator
            --> csvstack file1.csv file2.csv
            """
            records = io.join('file1.csv', 'file2.csv')
            next(records)
            >>> {'col_1': '1', 'col_2': 'dill', 'col_3': 'male'}
            next(it.islice(records, 4, None))
            >>> {'col_1': '6', 'col_2': 'jill', 'col_3': 'female'}
        
            # Now let's create a persistant records list
            records = list(io.read_csv('file1.csv'))
        
            """Sort records by the value of column `col_2`
            --> csvsort -c col_2 file1.csv
            """
            next(pr.sort(records, 'col_2'))
            >>> {'col_1': '2', 'col_2': 'bob', 'col_3': 'male'
        
            """Select column `col_2` --> csvcut -c col_2 file1.csv"""
            next(pr.cut(records, ['col_2']))
            >>> {'col_2': 'dill'}
        
            """Select all data whose value for column `col_2` contains `jan`
            --> csvgrep -c col_2 -m jan file1.csv
            """
            next(pr.grep(records, [{'pattern': 'jan'}], ['col_2']))
            >>> {'col_1': '3', 'col_2': 'jane', 'col_3': 'female'}
        
            """Convert a csv file to json --> csvjson -i 4 file1.csv"""
            io.write('file.json', cv.records2json(records))
        
            # View the result
            with open('file.json', 'utf-8') as f:
                f.read()
            >>> '[{"col_1": "1", "col_2": "dill", "col_3": "male"}, {"col_1": "2",
            ... "col_2": "bob", "col_3": "male"}, {"col_1": "3", "col_2": "jane",
            ... "col_3": "female"}]'
        
        Geo processing (à la mapbox) [#]_
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        In the following example, ``mapbox`` equivalent commands are preceded by ``-->``,
        and command output is preceded by ``>>>``.
        
        First create a geojson file (in bash)
        
        .. code-block:: bash
        
            echo '{"type": "FeatureCollection","features": [' > file.geojson
            echo '{"type": "Feature", "id": 11, "geometry": {"type": "Point", "coordinates": [10, 20]}},' >> file.geojson
            echo '{"type": "Feature", "id": 12, "geometry": {"type": "Point", "coordinates": [5, 15]}}]}' >> file.geojson
        
        Now we can open the file, split the data by id, and finally convert the split data
        to a new geojson file-like object.
        
        .. code-block:: python
        
            from meza import io, process as pr, convert as cv
        
            # Load the geojson file and peek at the results
            records, peek = pr.peek(io.read_geojson('file.geojson'))
            peek[0]
            >>> {'lat': 20, 'type': 'Point', 'lon': 10, 'id': 11}
        
            """Split the records by feature ``id`` and select the first feature
            --> geojsplit -k id file.geojson
            """
            splits = pr.split(records, 'id')
            feature_records, name = next(splits)
            name
            >>> 11
        
            """Convert the feature records into a GeoJSON file-like object"""
            geojson = cv.records2geojson(feature_records)
            geojson.readline()
            >>> '{"type": "FeatureCollection", "bbox": [10, 20, 10, 20], "features": '
            ... '[{"type": "Feature", "id": 11, "geometry": {"type": "Point", '
            ... '"coordinates": [10, 20]}, "properties": {"id": 11}}], "crs": {"type": '
            ... '"name", "properties": {"name": "urn:ogc:def:crs:OGC:1.3:CRS84"}}}'
        
            # Note: you can also write back to a file as shown previously
            # io.write('file.geojson', geojson)
        
        Writing data
        ^^^^^^^^^^^^
        
        meza can persist ``records`` to disk via the following functions:
        
        - ``meza.convert.records2csv``
        - ``meza.convert.records2json``
        - ``meza.convert.records2geojson``
        
        Each function returns a file-like object that you can write to disk via
        ``meza.io.write('/path/to/file', result)``.
        
        .. code-block:: python
        
            from meza import io, convert as cv
            from io import StringIO, open
        
            # First let's create a simple tsv file like object
            f = StringIO('col1\tcol2\nhello\tworld\n')
            f.seek(0)
        
            # Next create a records list so we can reuse it
            records = list(io.read_tsv(f))
            records[0]
            >>> {'col1': 'hello', 'col2': 'world'}
        
            # Now we're ready to write the records data to file
        
            """Create a csv file like object"""
            cv.records2csv(records).readline()
            >>> 'col1,col2\n'
        
            """Create a json file like object"""
            cv.records2json(records).readline()
            >>> '[{"col1": "hello", "col2": "world"}]'
        
            """Write back csv to a filepath"""
            io.write('file.csv', cv.records2csv(records))
            with open('file.csv', 'utf-8') as f_in:
                f_in.read()
            >>> 'col1,col2\nhello,world\n'
        
            """Write back json to a filepath"""
            io.write('file.json', cv.records2json(records))
            with open('file.json', 'utf-8') as f_in:
                f_in.readline()
            >>> '[{"col1": "hello", "col2": "world"}]'
        
        Cookbook
        ^^^^^^^^
        
        Please see the `cookbook guide`_ for more examples.
        
        Notes
        ^^^^^
        
        .. [#] http://pandas.pydata.org/pandas-docs/stable/10min.html#min
        .. [#] https://csvkit.readthedocs.org/en/0.9.1/cli.html#processing
        .. [#] https://github.com/mapbox?utf8=%E2%9C%93&query=geojson
        
        Interoperability
        ----------------
        
        meza plays nicely with NumPy and friends out of the box
        
        setup
        ^^^^^
        
        .. code-block:: python
        
            from meza import process as pr
        
            # First create some records and types. Also, convert the records to a list
            # so we can reuse them.
            records = [{'a': 'one', 'b': 2}, {'a': 'five', 'b': 10, 'c': 20.1}]
            records, result = pr.detect_types(records)
            records, types = list(records), result['types']
            types
            >>> [
            ...     {'type': 'text', 'id': 'a'},
            ...     {'type': 'int', 'id': 'b'},
            ...     {'type': 'float', 'id': 'c'}]
        
        
        from records to pandas.DataFrame to records
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            import pandas as pd
            from meza import convert as cv
        
            """Convert the records to a DataFrame"""
            df = cv.records2df(records, types)
            df
            >>>         a   b   c
            ... 0   one   2   NaN
            ... 1  five  10  20.1
            # Alternatively, you can do `pd.DataFrame(records)`
        
            """Convert the DataFrame back to records"""
            next(cv.df2records(df))
            >>> {'a': 'one', 'b': 2, 'c': nan}
        
        from records to arrays to records
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code-block:: python
        
            import numpy as np
        
            from array import array
            from meza import convert as cv
        
            """Convert records to a structured array"""
            recarray = cv.records2array(records, types)
            recarray
            >>> rec.array([('one', 2, nan), ('five', 10, 20.100000381469727)],
            ...           dtype=[('a', 'O'), ('b', '<i4'), ('c', '<f4')])
            recarray.b
            >>> array([ 2, 10], dtype=int32)
        
            """Convert records to a native array"""
            narray = cv.records2array(records, types, native=True)
            narray
            >>> [[array('u', 'a'), array('u', 'b'), array('u', 'c')],
            ... [array('u', 'one'), array('u', 'five')],
            ... array('i', [2, 10]),
            ... array('f', [0.0, 20.100000381469727])]
        
            """Convert a 2-D NumPy array to a records generator"""
            data = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
            data
            >>> array([[1, 2, 3],
            ...        [4, 5, 6]], dtype=int32)
            next(cv.array2records(data))
            >>> {'column_1': 1, 'column_2': 2, 'column_3': 3}
        
            """Convert the structured array back to a records generator"""
            next(cv.array2records(recarray))
            >>> {'a': 'one', 'b': 2, 'c': nan}
        
            """Convert the native array back to records generator"""
            next(cv.array2records(narray, native=True))
            {'a': 'one', 'b': 2, 'c': 0.0}
        
        Installation
        ------------
        
        (You are using a `virtualenv`_, right?)
        
        At the command line, install meza using either ``pip`` (*recommended*)
        
        .. code-block:: bash
        
            pip install meza
        
        or ``easy_install``
        
        .. code-block:: bash
        
            easy_install meza
        
        Please see the `installation doc`_ for more details.
        
        Project Structure
        -----------------
        
        .. code-block:: bash
        
            ┌── AUTHORS.rst
            ├── CHANGES.rst
            ├── CONTRIBUTING.rst
            ├── INSTALLATION.rst
            ├── LICENSE
            ├── MANIFEST.in
            ├── Makefile
            ├── README.rst
            ├── TODO.rst
            ├── data
            │   ├── converted
            │   │   ├── dbf.csv
            │   │   ├── fixed.csv
            │   │   ├── geo.csv
            │   │   ├── geojson.csv
            │   │   ├── json.csv
            │   │   ├── json_multiline.csv
            │   │   └── sheet_2.csv
            │   └── test
            │       ├── fixed.txt
            │       ├── fixed_w_header.txt
            │       ├── iris.csv
            │       ├── irismeta.csv
            │       ├── latin1.csv
            │       ├── mac_newlines.csv
            │       ├── newline.json
            │       ├── no_header_row.csv
            │       ├── test.csv
            │       ├── test.dbf
            │       ├── test.geojson
            │       ├── test.html
            │       ├── test.json
            │       ├── test.mdb
            │       ├── test.sqlite
            │       ├── test.tsv
            │       ├── test.xls
            │       ├── test.xlsx
            │       ├── test.yml
            │       ├── utf16_big.csv
            │       ├── utf16_little.csv
            │       └── utf8.csv
            ├── dev-requirements.txt
            ├── examples.py
            ├── helpers
            │   ├── check-stage
            │   ├── clean
            │   ├── pippy
            │   ├── srcdist
            │   └── wheel
            ├── manage.py
            ├── py2-requirements.txt
            ├── requirements.txt
            ├── setup.cfg
            ├── setup.py
            ├── meza
            │   ├── __init__.py
            │   ├── convert.py
            │   ├── dbf.py
            │   ├── fntools.py
            │   ├── io.py
            │   ├── process.py
            │   ├── stats.py
            │   ├── typetools.py
            │   └── unicsv.py
            ├── tests
            │   ├── __init__.py
            │   ├── standard.rc
            │   ├── test_fntools.py
            │   ├── test_io.py
            │   └── test_process.py
            └── tox.ini
        
        Design Principles
        -----------------
        
        - prefer functions over objects
        - provide enough functionality out of the box to easily implement the most common data analysis use cases
        - make conversion between ``records``, ``arrays``, and ``DataFrames`` dead simple
        - whenever possible, lazily read objects and stream the result [#]_
        
        .. [#] Notable exceptions are ``meza.process.group``, ``meza.process.sort``, ``meza.io.read_dbf``, ``meza.io.read_yaml``, and ``meza.io.read_html``. These functions read the entire contents into memory up front.
        
        Readers
        -------
        
        meza' available readers are outlined below:
        
        +-----------------------+-------------------------+----------------+
        | File type             | Recognized extension(s) | Default reader |
        +=======================+=========================+================+
        | Comma separated file  | csv                     | read_csv       |
        +-----------------------+-------------------------+----------------+
        | dBASE/FoxBASE         | dbf                     | read_dbf       |
        +-----------------------+-------------------------+----------------+
        | Fixed width file      | fixed                   | read_fixed_fmt |
        +-----------------------+-------------------------+----------------+
        | GeoJSON               | geojson, geojson.json   | read_geojson   |
        +-----------------------+-------------------------+----------------+
        | HTML table            | html                    | read_html      |
        +-----------------------+-------------------------+----------------+
        | JSON                  | json                    | read_json      |
        +-----------------------+-------------------------+----------------+
        | Microsoft Access      | mdb                     | read_mdb       |
        +-----------------------+-------------------------+----------------+
        | SQLite                | sqlite                  | read_sqlite    |
        +-----------------------+-------------------------+----------------+
        | Tab separated file    | tsv                     | read_tsv       |
        +-----------------------+-------------------------+----------------+
        | Microsoft Excel       | xls, xlsx               | read_xls       |
        +-----------------------+-------------------------+----------------+
        | YAML                  | yml, yaml               | read_yaml      |
        +-----------------------+-------------------------+----------------+
        
        Alternatively, meza provides a universal reader which will select the
        appropriate reader based on the file extension as specified in the above
        table.
        
        .. code-block:: python
        
            from io import open
            from meza import io
        
            records1 = io.read('path/to/file.csv')
            records2 = io.read('path/to/file.xls')
        
            with open('path/to/file.json', encoding='utf-8') as f:
                records3 = io.read(f, ext='json')
        
        Args
        ^^^^
        
        Most readers take as their first argument, either a file path or file like object.
        The notable execption is ``read_mdb`` which only accepts a file path.
        File like objects should be opened using Python's stdlib ``io.open``. If the file
        is opened in binary mode ``io.open('/path/to/file')``, be sure to pass the proper
        encoding if it is anything other than ``utf-8``, e.g.,
        
        .. code-block:: python
        
            from io import open
            from meza import io
        
            with open('path/to/file.xlsx') as f:
                records = io.read_xls(f, encoding='latin-1')
        
        Kwargs
        ^^^^^^
        
        While each reader has kwargs specific to itself, the following table outlines
        the most common ones.
        
        ==========  ====  =======================================  =======  =====================================================================================================
        kwarg       type  description                              default  implementing readers
        ==========  ====  =======================================  =======  =====================================================================================================
        mode        str   File open mode                           rU       read_csv, read_fixed_fmt, read_geojson, read_html, read_json, read_tsv, read_xls, read_yaml
        encoding    str   File encoding                            utf-8    read_csv, read_dbf, read_fixed_fmt, read_geojson, read_html, read_json, read_tsv, read_xls, read_yaml
        has_header  bool  Data has a header row?                   True     read_csv, read_fixed_fmt, read_tsv, read_xls
        first_row   int   First row to read (zero indexed)         0        read_csv, read_fixed_fmt, read_tsv, read_xls
        first_col   int   First column to read (zero indexed)      0        read_csv, read_fixed_fmt, read_tsv, read_xls
        sanitize    bool  Underscorify and lowercase field names?  False    read_csv, read_dbf, read_fixed_fmt, read_html, read_mdb, read_tsv, read_xls
        dedupe      bool  Deduplicate field names?                 False    read_csv, read_fixed_fmt, read_html, read_mdb, read_tsv, read_xls
        sheet       int   Sheet to read (zero indexed)             0        read_xls
        table       int   Table to read (zero indexed)             0        read_dbf, read_html, read_mdb, read_sqlite
        ==========  ====  =======================================  =======  =====================================================================================================
        
        Scripts
        -------
        
        meza comes with a built in task manager ``manage.py``
        
        Setup
        ^^^^^
        
        .. code-block:: bash
        
            pip install -r dev-requirements.txt
        
        Examples
        ^^^^^^^^
        
        *Run python linter and nose tests*
        
        .. code-block:: bash
        
            manage lint
            manage test
        
        Contributing
        ------------
        
        Please mimic the coding style/conventions used in this repo.
        If you add new classes or functions, please add the appropriate doc blocks with
        examples. Also, make sure the python linter and nose tests pass.
        
        Please see the `contributing doc`_ for more details.
        
        Credits
        -------
        
        Shoutouts to `csvkit`_, `messytables`_, and `pandas`_ for heavily inspiring meza.
        
        License
        -------
        
        meza is distributed under the `MIT License`_.
        
        .. |travis| image:: https://img.shields.io/travis/reubano/meza/master.svg
            :target: https://travis-ci.org/reubano/meza
        
        .. |versions| image:: https://img.shields.io/pypi/pyversions/meza.svg
            :target: https://pypi.python.org/pypi/meza
        
        .. |pypi| image:: https://img.shields.io/pypi/v/meza.svg
            :target: https://pypi.python.org/pypi/meza
        
        .. _mdbtools: http://sourceforge.net/projects/mdbtools/
        .. _lxml: http://www.crummy.com/software/BeautifulSoup/bs4/doc/#installing-a-parser
        .. _library: #usage
        .. _NumPy: https://github.com/numpy/numpy
        .. _a library: https://csvkit.readthedocs.org/en/0.9.1/api/csvkit.py3.html
        .. _PyPy: https://github.com/pydata/pandas/issues/9532
        .. _walk in the park: http://pandas.pydata.org/pandas-docs/stable/install.html#installing-pandas-with-anaconda
        .. _csvkit: https://github.com/onyxfish/csvkit
        .. _messytables: https://github.com/okfn/messytables
        .. _pandas: https://github.com/pydata/pandas
        .. _MIT License: http://opensource.org/licenses/MIT
        .. _virtualenv: http://www.virtualenv.org/en/latest/index.html
        .. _contributing doc: https://github.com/reubano/meza/blob/master/CONTRIBUTING.rst
        .. _installation doc: https://github.com/reubano/meza/blob/master/INSTALLATION.rst
        .. _cookbook guide: https://github.com/reubano/meza/blob/master/COOKBOOK.rst
        
        
        =========
        Changelog
        =========
        
        Here you can find the recent changes to meza..
        
        .. changelog::
            :version: dev
            :released: Ongoing
        
            .. change::
                :tags:  docs
        
                Updated CHANGES.
        
        .. changelog::
            :version: 0.1.0
            :released: 2015-12-05
        
            .. change::
                :tags: docs
        
                First release on PyPi.
        
        .. todo:: vim: set filetype=rst:
        
Keywords: meza,A,Python,toolkit,for,processing,tabular,data
Platform: MacOS X
Platform: Windows
Platform: Linux
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: PyPy
Classifier: Environment :: Console
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
