yeandy commented on code in PR #22575: URL: https://github.com/apache/beam/pull/22575#discussion_r958421050
########## sdks/python/apache_beam/typehints/pandas_type_compatibility.py: ########## @@ -0,0 +1,299 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +r"""Utilities for converting between Beam schemas and pandas DataFrames. + +Imposes a mapping between native Python typings (specifically those compatible +with :mod:`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 + np.dtype('S') <-----> bytes + pd.BooleanDType() <-----> Optional[bool] + pd.StringDType() <-----> Optional[str] + \--- str + np.object <-----> Any + + * int, float, bool are treated the same as np.int64, np.float64, np.bool + +Note that when converting to pandas dtypes, any types not specified here are +shunted to ``np.object``. + +Similarly when converting from pandas to Python types, types that aren't +otherwise specified here are shunted to ``Any``. Notably, this includes +``np.datetime64``. + +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. + +Note utilities in this package are for internal use only, we make no backward +compatibility guarantees, except for the type mapping itself. +""" + +from typing import Any +from typing import List +from typing import Optional + +import numpy as np +import pandas as pd + +from apache_beam.typehints.batch import BatchConverter +from apache_beam.typehints.row_type import RowTypeConstraint +from apache_beam.typehints.typehints import is_nullable +from apache_beam.typehints.typehints import normalize + +# No public API currently, this just exists to register BatchConverter +# implementations. +__all__ = [] + +# Name for a valueless field-level option which, when present, indicates that +# a field should map to an index in the Beam DataFrame API. +INDEX_OPTION_NAME = 'beam:dataframe:index' + +# Generate type map (presented visually in the docstring) +_BIDIRECTIONAL = [ + (bool, bool), + (np.int8, np.int8), + (np.int16, np.int16), + (np.int32, np.int32), + (np.int64, np.int64), + (pd.Int8Dtype(), Optional[np.int8]), + (pd.Int16Dtype(), Optional[np.int16]), + (pd.Int32Dtype(), Optional[np.int32]), + (pd.Int64Dtype(), Optional[np.int64]), + (np.float32, Optional[np.float32]), + (np.float64, Optional[np.float64]), + (object, Any), + (pd.StringDtype(), Optional[str]), + (pd.BooleanDtype(), Optional[bool]), +] + +PANDAS_TO_BEAM = { + pd.Series([], dtype=dtype).dtype: fieldtype + for dtype, + fieldtype in _BIDIRECTIONAL +} +BEAM_TO_PANDAS = {fieldtype: dtype for dtype, fieldtype in _BIDIRECTIONAL} + +# Shunt non-nullable Beam types to the same pandas types as their non-nullable +# equivalents for FLOATs, DOUBLEs, and STRINGs. pandas has no non-nullable dtype +# for these. +OPTIONAL_SHUNTS = [np.float32, np.float64, str] + +for typehint in OPTIONAL_SHUNTS: + BEAM_TO_PANDAS[typehint] = BEAM_TO_PANDAS[Optional[typehint]] + +# int, float -> int64, np.float64 +BEAM_TO_PANDAS[int] = BEAM_TO_PANDAS[np.int64] +BEAM_TO_PANDAS[Optional[int]] = BEAM_TO_PANDAS[Optional[np.int64]] +BEAM_TO_PANDAS[float] = BEAM_TO_PANDAS[np.float64] +BEAM_TO_PANDAS[Optional[float]] = BEAM_TO_PANDAS[Optional[np.float64]] + +BEAM_TO_PANDAS[bytes] = 'bytes' + +# Add shunts for normalized (Beam) typehints as well +BEAM_TO_PANDAS.update({ + normalize(typehint): pandas_dtype + for (typehint, pandas_dtype) in BEAM_TO_PANDAS.items() +}) + + +def dtype_from_typehint(typehint): + # Default to np.object. This is lossy, we won't be able to recover + # the type at the output. + return BEAM_TO_PANDAS.get(typehint, object) + + +def dtype_to_fieldtype(dtype): + fieldtype = PANDAS_TO_BEAM.get(dtype) + + if fieldtype is not None: + return fieldtype + elif dtype.kind == 'S': + return bytes + else: + return Any + + [email protected] +def create_pandas_batch_converter( + element_type: type, batch_type: type) -> BatchConverter: + if batch_type == pd.DataFrame: + return DataFrameBatchConverter.from_typehints( + element_type=element_type, batch_type=batch_type) + elif batch_type == pd.Series: + return SeriesBatchConverter.from_typehints( + element_type=element_type, batch_type=batch_type) + + return None + + +class DataFrameBatchConverter(BatchConverter): + def __init__( + self, + element_type: RowTypeConstraint, + ): + super().__init__(pd.DataFrame, element_type) + self._columns = [name for name, _ in element_type._fields] + + @staticmethod + def from_typehints(element_type, + batch_type) -> Optional['DataFrameBatchConverter']: + if not batch_type == pd.DataFrame: + return None + + if not isinstance(element_type, RowTypeConstraint): + element_type = RowTypeConstraint.from_user_type(element_type) + if element_type is None: + return None + + index_columns = [ + field_name + for (field_name, field_options) in element_type._field_options.items() + if any(key == INDEX_OPTION_NAME for key, value in field_options) + ] + + if index_columns: + return DataFrameBatchConverterKeepIndex(element_type, index_columns) + else: + return DataFrameBatchConverterDropIndex(element_type) + + def _get_series(self, batch: pd.DataFrame): + raise NotImplementedError + + def explode_batch(self, batch: pd.DataFrame): + # TODO: Only do null checks for nullable types + def make_null_checking_generator(series): + nulls = pd.isnull(series) + return (None if isnull else value for isnull, value in zip(nulls, series)) + + all_series = self._get_series(batch) + iterators = [make_null_checking_generator(series) for series in all_series] + + for values in zip(*iterators): Review Comment: I was originally thinking of ``` for values, columns in zip(*iterators, self._columns): ... ``` But I had to take a look again to wrap my head around it. Looks like you're zipping to create the rows first, and then in the second zip, you line them up with the column names. The length of an individual iterator in `iterators` isn't necessarily the same as the length of `self._columns`. Plus, we'd probably get `too many values to unpack` error if we had `values, columns`. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
