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.. 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.

Storing pandas DataFrame objects in Apache Parquet format

The Apache Parquet format provides key-value metadata at the file and column level, stored in the footer of the Parquet file:

5: optional list<KeyValue> key_value_metadata

where KeyValue is

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 :

{'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:

{'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:

{'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'}

Custom Array Types

.. versionadded:: 0.23.0

Warning

Support for custom array types is experimental.

Sometimes the NumPy type system isn't rich enough for your needs. Pandas has made a few extensions internally (e.g. Categorical). While this has worked well for pandas, not all custom data types belong in pandas itself.

Pandas defines an interface for custom arrays. Arrays implementing this interface will be stored correctly in Series or DataFrame. The ABCs that must be implemented are

  1. :class:`ExtensionDtype` A class describing your data type itself. This is similar to a numpy.dtype.
  2. :class:`ExtensionArray`: A container for your data.

Throughout this document, we'll use the example of storing IPv6 addresses. An IPv6 address is 128 bits, so NumPy doesn't have a native data type for it. We'll model it as a structured array with two uint64 fields, which together represent the 128-bit integer that is the IP Address.

Extension Dtype

This class should describe your data type. The most important fields are name and base:

class IPv6Type(ExtensionDtype):
    name = 'IPv6'
    base = np.dtype([('hi', '>u8'), ('lo', '>u8')])
    type = IPTypeType
    kind = 'O'
    fill_value = np.array([(0, 0)], dtype=base)

base describe the underlying storage of individual items in your array. TODO: is this true? Or does .base refer to the original memory this is a view on? Different meanings for np.dtype.base vs. np.ndarray.base?

In our IPAddress case, we're using a NumPy structured array with two fields.

Extension Array

This is the actual array container for your data, similar to a Categorical, and requires the most work to implement correctly. pandas makes no assumptions about how you store the data. You're free to use NumPy arrays or PyArrow arrays, or even just Python lists. That said, several of the methods required by the interface expect NumPy arrays as the return value.

  • dtype: Should be an instance of your custom ExtensionType
  • formtting_values(self): Used for printing Series and DataFrame
  • concat_same_type(concat): Used in :func:`pd.concat`