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.. _developer:
.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
np.random.seed(123456)
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.options.display.max_rows = 15
*********
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 value stored as :
.. code-block:: text
{'index_columns': ['__index_level_0__', '__index_level_1__', ...],
'column_indexes': [<ci0>, <ci1>, ..., <ciN>],
'columns': [<c0>, <c1>, ...],
'pandas_version': $VERSION}
Here, ``<c0>``/``<ci0>`` 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': 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 ``metadata`` field 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__'],
'column_indexes': [
{'name': None,
'pandas_type': 'string',
'numpy_type': 'object',
'metadata': None}
],
'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'}
|
