apache_beam.dataframe.schemas module¶

Utilities for relating schema-aware PCollections and dataframe transforms.

Imposes a mapping between native Python typings (specifically those compatible with apache_beam.typehints.schemas), and common pandas dtypes:

pandas dtype                    Python typing
np.int{8,16,32,64}      <-----> np.int{8,16,32,64}*
pd.Int{8,16,32,64}Dtype <-----> Optional[np.int{8,16,32,64}]*
np.float{32,64}         <-----> Optional[np.float{32,64}]
                           \--- np.float{32,64}
Not supported           <------ Optional[bytes]
np.bool                 <-----> np.bool

* int, float, bool are treated the same as np.int64, np.float64, np.bool

Any unknown or unsupported types are treated as Any and shunted to np.object:

np.object               <-----> Any

bytes, unicode strings and nullable Booleans are handled differently when using pandas 0.x vs. 1.x. pandas 0.x has no mapping for these types, so they are shunted to np.object.

pandas 1.x Only:

np.dtype('S')     <-----> bytes
pd.BooleanDType() <-----> Optional[bool]
pd.StringDType()  <-----> Optional[str]
                     \--- str

Pandas does not support hierarchical data natively. Currently, all structured types (Sequence, Mapping, nested NamedTuple types), are shunted to np.object like all other unknown types. In the future these types may be given special consideration.

class apache_beam.dataframe.schemas.BatchRowsAsDataFrame(*args, proxy=None, **kwargs)[source]¶

Bases: apache_beam.transforms.ptransform.PTransform

A transform that batches schema-aware PCollection elements into DataFrames

Batching parameters are inherited from BatchElements.

expand(pcoll)[source]¶

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]¶

default_label()¶

default_type_hints()¶

display_data()¶

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

Return type: Dict[str, Any]

classmethod from_runner_api(proto, context)¶

get_type_hints()¶

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs)¶

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)¶

label¶

pipeline = None¶

classmethod register_urn(urn, parameter_type, constructor=None)¶

runner_api_requires_keyed_input()¶

side_inputs = ()¶

to_runner_api(context, has_parts=False, **extra_kwargs)¶

to_runner_api_parameter(unused_context)¶

to_runner_api_pickled(unused_context)¶

type_check_inputs(pvalueish)¶

type_check_inputs_or_outputs(pvalueish, input_or_output)¶

type_check_outputs(pvalueish)¶

with_input_types(input_type_hint)¶

Annotates the input type of a PTransform with a type-hint.

Parameters:	input_type_hint (type) – An instance of an allowed built-in type, a custom class, or an instance of a `TypeConstraint`.
Raises:	`TypeError` – If input_type_hint is not a valid type-hint. See `apache_beam.typehints.typehints.validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

with_output_types(type_hint)¶

Annotates the output type of a PTransform with a type-hint.

Parameters:	type_hint (type) – An instance of an allowed built-in type, a custom class, or a `TypeConstraint`.
Raises:	`TypeError` – If type_hint is not a valid type-hint. See `validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

apache_beam.dataframe.schemas.generate_proxy(element_type)[source]¶

Generate a proxy pandas object for the given PCollection element_type.

Currently only supports generating a DataFrame proxy from a schema-aware PCollection or a Series proxy from a primitively typed PCollection.

apache_beam.dataframe.schemas.element_type_from_dataframe(proxy, include_indexes=False)[source]¶

Generate an element_type for an element-wise PCollection from a proxy pandas object. Currently only supports converting the element_type for a schema-aware PCollection to a proxy DataFrame.

Currently only supports generating a DataFrame proxy from a schema-aware PCollection.

class apache_beam.dataframe.schemas.UnbatchPandas(proxy, include_indexes=False)[source]¶