Metadata-Version: 2.1
Name: tafra
Version: 1.0.2
Summary: Tafra: innards of a dataframe
Home-page: https://github.com/petbox-dev/tafra
Author: David S. Fulford
Author-email: petbox.dev@gmail.com
License: UNKNOWN
Description: =============================
        Tafra: a minimalist dataframe
        =============================
        
        .. image:: https://img.shields.io/pypi/v/tafra.svg
            :target: https://pypi.org/project/tafra/
        
        .. image:: https://travis-ci.org/petbox-dev/tafra.svg?branch=master
            :target: https://travis-ci.org/petbox-dev/tafra
        
        .. image:: https://readthedocs.org/projects/tafra/badge/?version=latest
            :target: https://tafra.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
        .. image:: https://coveralls.io/repos/github/petbox-dev/tafra/badge.svg
            :target: https://coveralls.io/github/petbox-dev/tafra
            :alt: Coverage Status
        
        
        The ``tafra`` began life as a thought experiment: how could we reduce the idea
        of a da\ *tafra*\ me (as expressed in libraries like ``pandas`` or languages
        like R) to its useful essence, while carving away the cruft?
        The `original proof of concept <https://usethe.computer/posts/12-typing-groupby.html>`_
        stopped at "group by".
        
        .. `original proof of concept`_
        
        This library expands on the proof of concept to produce a practically
        useful ``tafra``, which we hope you may find to be a helpful lightweight
        substitute for certain uses of ``pandas``.
        
        A ``tafra`` is, more-or-less, a set of named *columns* or *dimensions*.
        Each of these is a typed ``numpy`` array of consistent length, representing
        the values for each column by *rows*.
        
        The library provides lightweight syntax for manipulating rows and columns,
        support for managing data types, iterators for rows and sub-frames,
        `pandas`-like "transform" support and conversion from `pandas` Dataframes,
        and SQL-style "group by" and join operations.
        
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Tafra                      | `Tafra <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra>`_                                                 |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Aggregations               | `Union <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.Union>`_,                                              |
        |                            | `GroupBy <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.GroupBy>`_,                                          |
        |                            | `Transform <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.Transform>`_,                                      |
        |                            | `IterateBy <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.IterateBy>`_,                                      |
        |                            | `InnerJoin <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.InnerJoin>`_,                                      |
        |                            | `LeftJoin <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.LeftJoin>`_,                                        |
        |                            | `CrossJoin <https://tafra.readthedocs.io/en/latest/api.html#tafra.groups.CrossJoin>`_                                       |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Aggregation Helpers        | `union <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.union>`__,                                         |
        |                            | `union_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.union_inplace>`_,                          |
        |                            | `group_by <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.group_by>`_,                                    |
        |                            | `transform <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.transform>`__,                                 |
        |                            | `iterate_by <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.iterate_by>`_,                                |
        |                            | `inner_join <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.inner_join>`_,                                |
        |                            | `left_join <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.left_join>`_,                                  |
        |                            | `cross_join <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.cross_join>`_                                 |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Constructors               | `as_tafra <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.as_tafra>`_,                                    |
        |                            | `from_dataframe <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.from_dataframe>`_,                        |
        |                            | `from_series <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.from_series>`_                               |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Destructors                | `to_records <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.to_records>`_,                                |
        |                            | `to_list <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.to_list>`_,                                      |
        |                            | `to_array <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.to_array>`_                                     |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Properties                 | `rows <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.rows>`_,                                            |
        |                            | `columns <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.columns>`_,                                      |
        |                            | `data <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.data>`_,                                            |
        |                            | `dtypes <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.dtypes>`_,                                        |
        |                            | `size <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.size>`_,                                            |
        |                            | `ndim <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.ndim>`_,                                            |
        |                            | `shape <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.shape>`_                                           |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Iter Methods               | `iterrows <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.iterrows>`_,                                    |
        |                            | `itertuples <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.itertuples>`_,                                |
        |                            | `itercols <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.itercols>`_                                     |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Dict-like Methods          | `keys <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.keys>`_,                                            |
        |                            | `values <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.values>`_,                                        |
        |                            | `items <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.items>`_,                                          |
        |                            | `get <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.get>`_,                                              |
        |                            | `update <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.update>`_,                                        |
        |                            | `update_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.update_inplace>`_,                        |
        |                            | `update_dtypes <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.update_dtypes>`_,                          |
        |                            | `update_dtypes_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.update_dtypes_inplace>`_           |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Other Helper Methods       | `rename <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.rename>`_,                                        |
        |                            | `rename_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.rename_inplace>`_,                        |
        |                            | `coalesce <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.coalesce>`_,                                    |
        |                            | `coalesce_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.coalesce_inplace>`_,                    |
        |                            | `_coalesce_dtypes <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra._coalesce_dtypes>`_,                    |
        |                            | `delete <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.delete>`_,                                        |
        |                            | `delete_inplace <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.delete_inplace>`_                         |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        | Printer Methods            | `pprint <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.pprint>`_,                                        |
        |                            | `pformat <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.pformat>`_,                                      |
        |                            | `to_html <https://tafra.readthedocs.io/en/latest/api.html#tafra.base.Tafra.to_html>`_                                       |
        +----------------------------+-----------------------------------------------------------------------------------------------------------------------------+
        
        Getting Started
        ===============
        
        Install the library with `pip <https://pip.pypa.io/en/stable/>`_:
        
        .. code-block:: shell
        
            pip install tafra
        
        
        A short example
        ---------------
        
        .. code-block:: python
        
            >>> from tafra import Tafra
        
            >>> t = Tafra({
            ...    'x': np.array([1, 2, 3, 4]),
            ...    'y': np.array(['one', 'two', 'one', 'two'], dtype='object'),
            ... })
        
            >>> t.pformat()
            Tafra(data = {
             'x': array([1, 2, 3, 4]),
             'y': array(['one', 'two', 'one', 'two'])},
            dtypes = {
             'x': 'int', 'y': 'object'},
            rows = 4)
        
            >>> print('List:', '\n', t.to_list())
            List:
             [array([1, 2, 3, 4]), array(['one', 'two', 'one', 'two'], dtype=object)]
        
            >>> print('Records:', '\n', tuple(t.to_records()))
            Record:
             ((1, 'one'), (2, 'two'), (3, 'one'), (4, 'two'))
        
            >>> gb = t.group_by(
            ...     ['y'], {'x': sum}
            ... )
        
            >>> print('Group By:', '\n', gb.pformat())
            Group By:
            Tafra(data = {
             'x': array([4, 6]), 'y': array(['one', 'two'])},
            dtypes = {
             'x': 'int', 'y': 'object'},
            rows = 2)
        
        
        Flexibility
        -----------
        
        Have some code that works with ``pandas``, or just a way of doing things
        that you prefer? ``tafra`` is flexible:
        
        .. code-block:: python
        
            >>> df = pd.DataFrame(np.c_[
            ...     np.array([1, 2, 3, 4]),
            ...     np.array(['one', 'two', 'one', 'two'])
            ... ], columns=['x', 'y'])
        
            >>> t = Tafra.from_dataframe(df)
        
        
        And going back is just as simple:
        
        .. code-block:: python
        
            >>> df = pd.DataFrame(t.data)
        
        
        Timings
        =======
        
        In this case, lightweight also means performant. Beyond any additional
        features added to the library, ``tafra`` should provide the necessary
        base for organizing data structures for numerical processing. One of the
        most important aspects is fast access to the data itself. By minizing
        abstraction to access the underlying ``numpy`` arrays, ``tafra`` provides
        over an order of magnitude increase in performance.
        
        -   **Import note** If you assign directly to the ``Tafra.data`` or
            ``Tafra._data`` attributes, you *must* call ``Tafra._coalesce_dtypes``
            afterwards in order to ensure the typing is consistent.
        
        Construct a ``Tafra`` and a ``DataFrame``:
        
        .. code-block:: python
        
            >>> tf = Tafra({
            ...     'x': np.array([1., 2., 3., 4., 5., 6.]),
            ...     'y': np.array(['one', 'two', 'one', 'two', 'one', 'two'], dtype='object'),
            ...     'z': np.array([0, 0, 0, 1, 1, 1])
            ... })
        
            >>> df = pd.DataFrame(t.data)
        
        Read Operations
        ---------------
        
        Direct access:
        
        .. code-block:: python
        
            >>> %timemit x = t._data['x']
            55.3 ns Â± 5.64 ns per loop (mean Â± std. dev. of 7 runs, 10000000 loops each)
        
        
        Indirect with some penalty to support ``Tafra`` slicing and ``numpy``'s
        advanced indexing:
        
        .. code-block:: python
        
            >>> %timemit x = t['x']
            219 ns Â± 71.6 ns per loop (mean Â± std. dev. of 7 runs, 1000000 loops each)
        
        
        ``pandas`` timing:
        
        .. code-block:: python
        
            >>> %timemit x = df['x']
            1.55 Âµs Â± 105 ns per loop (mean Â± std. dev. of 7 runs, 1000000 loops each)
        
        
        This is the fastest methed for accessing the numpy array among alternatives of
        ``df.values()``, ``df.to_numpy()``, and ``df.loc[]``.
        
        
        Assignment Operations
        ---------------------
        
        Direct access is not recommended as it avoids the validation steps, but it
        does provide fast access to the data attribute:
        
        .. code-block:: python
        
            >>> x = np.arange(6)
        
            >>> %timeit tf._data['x'] = x
            65 ns Â± 5.55 ns per loop (mean Â± std. dev. of 7 runs, 10000000 loops each)
        
        
        Indidrect access has a performance penalty due to the validation checks to
        ensure consistency of the ``tafra``:
        
        .. code-block:: python
        
            >>> %timeit tf['x'] = x
            7.39 Âµs Â± 950 ns per loop (mean Â± std. dev. of 7 runs, 100000 loops each)
        
        Even so, there is considerable performance improvement over ``pandas``.
        
        ``pandas`` timing:
        
        .. code-block:: python
        
            >>> %timeit df['x'] = x
            47.8 Âµs Â± 3.53 Âµs per loop (mean Â± std. dev. of 7 runs, 10000 loops each)
        
        
        Grouping Operations
        -------------------
        
        ``tafra`` also excels at aggregation methods, the primary of which are a
        SQL-like ``GROUP BY`` and the split-apply-combine equivalent to a SQL-like
        ``GROUP BY`` following by a ``LEFT JOIN`` back to the original table.
        
        .. code-block:: python
        
            >>> %timeit tf.group_by(['y', 'z'], {'x': sum})
            138 Âµs Â± 4.03 Âµs per loop (mean Â± std. dev. of 7 runs, 10000 loops each)
        
            >>> %timeit tf.transform(['y', 'z'], {'sum_x': (sum, 'x')})
            161 Âµs Â± 2.31 Âµs per loop (mean Â± std. dev. of 7 runs, 10000 loops each)
        
        The equivalent ``pandas`` functions are given below. They require a chain
        of several object methods to perform the same role, and the transform requires
        a copy operation and assignment into the copied ``DataFrame`` in order to
        preserve immutability.
        
            >>> %timeit gdf = df.groupby(['y', 'z'])[['x']].apply(sum).reset_index()
            3.79 ms Â± 99.9 Âµs per loop (mean Â± std. dev. of 7 runs, 100 loops each)
        
            >>> %%timeit
            >>> tdf = df.copy()
            >>> df.groupby(['y', 'z'])[['x']].transform(sum)
            2.81 ms Â± 143 Âµs per loop (mean Â± std. dev. of 7 runs, 100 loops each)
        
        
        Version History
        ===============
        
        
        
        1.0.2
        -----
        
        * Add object_formatter to expose user formatting for dtype=object
        * Improvements to indexing and slicing
        
        1.0.1
        -----
        
        * Add iter functions
        * Add map functions
        * Various constructor improvements
        
        
        1.0.0
        -----
        
        * Initial Release
        
Keywords: tafra,dataframe,sql,group-by,performance
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries
Classifier: Typing :: Typed
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
