ENH: Draft metadata specification doc for Apache Parquet (pandas-dev#…

…16315)

* Draft metadata specification doc for Apache Parquet

* Tweaks, add pandas version

* Relax metadata key

* Be explicit that the metadata is file-level

* Don't hard code version

* Code reviews

* Move Parquet metadata to developer.rst, account for code reviews

* Code review comments

* Review comments

* Fix typo

doc/source/developer.rst

@@ -16,3 +16,120 @@ Developer
 *********
 
 This section will focus on downstream applications of pandas.
+
+.. _apache.parquet:
+
+Storing pandas DataFrame objects in Apache Parquet format
+---------------------------------------------------------
+
+The `Apache Parquet <https://github.com/apache/parquet-format>`__ format
+provides key-value metadata at the file and column level, stored in the footer
+of the Parquet file:
+
+.. code-block:: shell
+
+  5: optional list<KeyValue> key_value_metadata
+
+where ``KeyValue`` is
+
+.. code-block:: shell
+
+   struct KeyValue {
+     1: required string key
+     2: optional string value
+   }
+
+So that a ``pandas.DataFrame`` can be faithfully reconstructed, we store a
+``pandas`` metadata key in the ``FileMetaData`` with the the value stored as :
+
+.. code-block:: text
+
+   {'index_columns': ['__index_level_0__', '__index_level_1__', ...],
+    'columns': [<c0>, <c1>, ...],
+    'pandas_version': $VERSION}
+
+Here, ``<c0>`` and so forth are dictionaries containing the metadata for each
+column. This has JSON form:
+
+.. code-block:: text
+
+   {'name': column_name,
+    'pandas_type': pandas_type,
+    'numpy_type': numpy_type,
+    'metadata': type_metadata}
+
+``pandas_type`` is the logical type of the column, and is one of:
+
+* Boolean: ``'bool'``
+* Integers: ``'int8', 'int16', 'int32', 'int64', 'uint8', 'uint16', 'uint32', 'uint64'``
+* Floats: ``'float16', 'float32', 'float64'``
+* Date and Time Types: ``'datetime', 'datetimetz'``, ``'timedelta'``
+* String: ``'unicode', 'bytes'``
+* Categorical: ``'categorical'``
+* Other Python objects: ``'object'``
+
+The ``numpy_type`` is the physical storage type of the column, which is the
+result of ``str(dtype)`` for the underlying NumPy array that holds the data. So
+for ``datetimetz`` this is ``datetime64[ns]`` and for categorical, it may be
+any of the supported integer categorical types.
+
+The ``type_metadata`` is ``None`` except for:
+
+* ``datetimetz``: ``{'timezone': zone, 'unit': 'ns'}``, e.g. ``{'timezone',
+  'America/New_York', 'unit': 'ns'}``. The ``'unit'`` is optional, and if
+  omitted it is assumed to be nanoseconds.
+* ``categorical``: ``{'num_categories': K, 'ordered': is_ordered, 'type': $TYPE}``
+
+  * Here ``'type'`` is optional, and can be a nested pandas type specification
+    here (but not categorical)
+
+* ``unicode``: ``{'encoding': encoding}``
+
+  * The encoding is optional, and if not present is UTF-8
+
+* ``object``: ``{'encoding': encoding}``. Objects can be serialized and stored
+  in ``BYTE_ARRAY`` Parquet columns. The encoding can be one of:
+
+  * ``'pickle'``
+  * ``'msgpack'``
+  * ``'bson'``
+  * ``'json'``
+
+* ``timedelta``: ``{'unit': 'ns'}``. The ``'unit'`` is optional, and if omitted
+  it is assumed to be nanoseconds. This metadata is optional altogether
+
+For types other than these, the ``'metadata'`` key can be
+omitted. Implementations can assume ``None`` if the key is not present.
+
+As an example of fully-formed metadata:
+
+.. code-block:: text
+
+   {'index_columns': ['__index_level_0__'],
+    'columns': [
+        {'name': 'c0',
+         'pandas_type': 'int8',
+         'numpy_type': 'int8',
+         'metadata': None},
+        {'name': 'c1',
+         'pandas_type': 'bytes',
+         'numpy_type': 'object',
+         'metadata': None},
+        {'name': 'c2',
+         'pandas_type': 'categorical',
+         'numpy_type': 'int16',
+         'metadata': {'num_categories': 1000, 'ordered': False}},
+        {'name': 'c3',
+         'pandas_type': 'datetimetz',
+         'numpy_type': 'datetime64[ns]',
+         'metadata': {'timezone': 'America/Los_Angeles'}},
+        {'name': 'c4',
+         'pandas_type': 'object',
+         'numpy_type': 'object',
+         'metadata': {'encoding': 'pickle'}},
+        {'name': '__index_level_0__',
+         'pandas_type': 'int64',
+         'numpy_type': 'int64',
+         'metadata': None}
+    ],
+    'pandas_version': '0.20.0'}