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'}

Registering Custom Accessors

Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor(), pandas.api.extensions.register_series_accessor(), and pandas.api.extensions.register_index_accessor(), to add additional “namespaces” to pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The class’s __init__ method gets the object being decorated. For example:

class GeoAccessor(object):
    def __init__(self, pandas_obj):
        self._obj = pandas_obj

    def center(self):
        # return the geographic center point of this DataFarme
        lon = self._obj.latitude
        lat = self._obj.longitude
        return (float(lon.mean()), float(lat.mean()))

    def plot(self):
        # plot this array's data on a map, e.g., using Cartopy

Now users can access your methods using the geo namespace:

>>> ds = pd.DataFrame({'longitude': np.linspace(0, 10),
...                    'latitude': np.linspace(0, 20)})
(5.0, 10.0)
>>> ds.geo.plot()
# plots data on a map

This can be a convenient way to extend pandas objects without subclassing them. If you write a custom accessor, make a pull request adding it to our pandas Ecosystem page.

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