robertwb commented on a change in pull request #10760: [BEAM-9545] Dataframe transforms URL: https://github.com/apache/beam/pull/10760#discussion_r395226313
########## File path: sdks/python/apache_beam/dataframe/transforms.py ########## @@ -0,0 +1,244 @@ +# +# 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. + +from __future__ import absolute_import + +import pandas as pd + +import apache_beam as beam +from apache_beam import transforms +from apache_beam.dataframe import expressions +from apache_beam.dataframe import frame_base + + +class DataframeTransform(transforms.PTransform): + """A PTransform for applying function that takes and returns dataframes + to one or more PCollections. + + For example, if pcoll is a PCollection of dataframes, one could write:: + + pcoll | DataframeTransform(lambda df: df.group_by('key').sum()) + + To pass multiple PCollections, pass a tuple of PCollections wich will be + passed to the callable as positional arguments, or a dictionary of + PCollections, in which case they will be passed as keyword arguments. + """ + def __init__(self, func, proxy): + self._func = func + self._proxy = proxy + + def expand(self, input_pcolls): + def wrap_as_dict(values): + if isinstance(values, dict): + return dict + elif isinstance(values, tuple): + return dict(enumerate(values)) + else: + return {None: values} + + # TODO: Infer the proxy from the input schema. + def proxy(key): + if key is None: + return self._proxy + else: + return self._proxy[key] + + # The input can be a dictionary, tuple, or plain PCollection. + # Wrap as a dict for homogeneity. + # TODO: Possibly inject batching here. + input_dict = wrap_as_dict(input_pcolls) + placeholders = { + key: frame_base.DeferredFrame.wrap( + expressions.PlaceholderExpression(proxy(key))) + for key in input_dict.keys() + } + + # The calling convention of the user-supplied func varies according to the + # type of the input. + if isinstance(input_pcolls, dict): + result_frames = self._func(**placeholders) + elif isinstance(input_pcolls, tuple): + self._func(*(value for _, value in sorted(placeholders.items()))) Review comment: Yeah. And now it's tested. ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
