#
# 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 inspect
import weakref
from typing import TYPE_CHECKING
from typing import Any
from typing import Dict
from typing import Tuple
from typing import Union
from apache_beam import pvalue
from apache_beam.dataframe import expressions
from apache_beam.dataframe import frame_base
from apache_beam.dataframe import schemas
from apache_beam.dataframe import transforms
if TYPE_CHECKING:
# pylint: disable=ungrouped-imports
from typing import Optional
import pandas
# TODO: Or should this be called as_dataframe?
[docs]def to_dataframe(
pcoll, # type: pvalue.PCollection
proxy=None, # type: Optional[pandas.core.generic.NDFrame]
label=None, # type: Optional[str]
):
# type: (...) -> frame_base.DeferredFrame
"""Converts a PCollection to a deferred dataframe-like object, which can
manipulated with pandas methods like `filter` and `groupby`.
For example, one might write::
pcoll = ...
df = to_dataframe(pcoll, proxy=...)
result = df.groupby('col').sum()
pcoll_result = to_pcollection(result)
A proxy object must be given if the schema for the PCollection is not known.
"""
if proxy is None:
if pcoll.element_type is None:
raise ValueError(
"Cannot infer a proxy because the input PCollection does not have a "
"schema defined. Please make sure a schema type is specified for "
"the input PCollection, or provide a proxy.")
# If no proxy is given, assume this is an element-wise schema-aware
# PCollection that needs to be batched.
if label is None:
# Attempt to come up with a reasonable, stable label by retrieving
# the name of these variables in the calling context.
label = 'BatchElements(%s)' % _var_name(pcoll, 2)
proxy = schemas.generate_proxy(pcoll.element_type)
pcoll = pcoll | label >> schemas.BatchRowsAsDataFrame(proxy=proxy)
return frame_base.DeferredFrame.wrap(
expressions.PlaceholderExpression(proxy, pcoll))
# PCollections generated by to_pcollection are memoized, keyed by expression id.
# WeakValueDictionary is used so the caches are cleaned up with the parent
# pipelines
# Note that the pipeline (indirectly) holds references to the transforms which
# keeps both the PCollections and expressions alive. This ensures the
# expression's ids are never accidentally re-used.
TO_PCOLLECTION_CACHE = weakref.WeakValueDictionary(
) # type: weakref.WeakValueDictionary[str, pvalue.PCollection]
UNBATCHED_CACHE = weakref.WeakValueDictionary(
) # type: weakref.WeakValueDictionary[str, pvalue.PCollection]
def _make_unbatched_pcoll(
pc: pvalue.PCollection, expr: expressions.Expression,
include_indexes: bool):
label = f"Unbatch '{expr._id}'"
if include_indexes:
label += " with indexes"
if label not in UNBATCHED_CACHE:
UNBATCHED_CACHE[label] = pc | label >> schemas.UnbatchPandas(
expr.proxy(), include_indexes=include_indexes)
# Note unbatched cache is keyed by the expression id as well as parameters
# for the unbatching (i.e. include_indexes)
return UNBATCHED_CACHE[label]
# TODO: Or should this be called from_dataframe?
[docs]def to_pcollection(
*dataframes, # type: frame_base.DeferredFrame
**kwargs):
# type: (...) -> Union[pvalue.PCollection, Tuple[pvalue.PCollection, ...]]
"""Converts one or more deferred dataframe-like objects back to a PCollection.
This method creates and applies the actual Beam operations that compute
the given deferred dataframes, returning a PCollection of their results. By
default the resulting PCollections are schema-aware PCollections where each
element is one row from the output dataframes, excluding indexes. This
behavior can be modified with the `yield_elements` and `include_indexes`
arguments.
If more than one (related) result is desired, it can be more efficient to
pass them all at the same time to this method.
Args:
always_return_tuple: (optional, default: False) If true, always return
a tuple of PCollections, even if there's only one output.
yield_elements: (optional, default: "schemas") If set to "pandas", return
PCollections containing the raw Pandas objects (DataFrames or Series),
if set to "schemas", return an element-wise PCollection, where DataFrame
and Series instances are expanded to one element per row. DataFrames are
converted to schema-aware PCollections, where column values can be
accessed by attribute.
include_indexes: (optional, default: False) When yield_elements="schemas",
if include_indexes=True, attempt to include index columns in the output
schema for expanded DataFrames. Raises an error if any of the index
levels are unnamed (name=None), or if any of the names are not unique
among all column and index names.
"""
label = kwargs.pop('label', None)
always_return_tuple = kwargs.pop('always_return_tuple', False)
yield_elements = kwargs.pop('yield_elements', 'schemas')
if not yield_elements in ("pandas", "schemas"):
raise ValueError(
"Invalid value for yield_elements argument, '%s'. "
"Allowed values are 'pandas' and 'schemas'" % yield_elements)
include_indexes = kwargs.pop('include_indexes', False)
assert not kwargs # TODO(BEAM-7372): Use PEP 3102
if label is None:
# Attempt to come up with a reasonable, stable label by retrieving the name
# of these variables in the calling context.
label = 'ToPCollection(%s)' % ', '.join(_var_name(e, 3) for e in dataframes)
def extract_input(placeholder):
if not isinstance(placeholder._reference, pvalue.PCollection):
raise TypeError(
'Expression roots must have been created with to_dataframe.')
return placeholder._reference
placeholders = frozenset.union(
frozenset(), *[df._expr.placeholders() for df in dataframes])
# Exclude any dataframes that have already been converted to PCollections.
# We only want to convert each DF expression once, then re-use.
new_dataframes = [
df for df in dataframes if df._expr._id not in TO_PCOLLECTION_CACHE
]
if len(new_dataframes):
new_results = {p: extract_input(p)
for p in placeholders
} | label >> transforms._DataframeExpressionsTransform({
ix: df._expr
for (ix, df) in enumerate(new_dataframes)
}) # type: Dict[Any, pvalue.PCollection]
TO_PCOLLECTION_CACHE.update(
{new_dataframes[ix]._expr._id: pc
for ix, pc in new_results.items()})
raw_results = {
ix: TO_PCOLLECTION_CACHE[df._expr._id]
for ix,
df in enumerate(dataframes)
}
if yield_elements == "schemas":
def maybe_unbatch(pc, value):
if isinstance(value, frame_base._DeferredScalar):
return pc
else:
return _make_unbatched_pcoll(pc, value._expr, include_indexes)
results = {
ix: maybe_unbatch(pc, dataframes[ix])
for (ix, pc) in raw_results.items()
}
else:
results = raw_results
if len(results) == 1 and not always_return_tuple:
return results[0]
else:
return tuple(value for key, value in sorted(results.items()))
def _var_name(obj, level):
frame = inspect.currentframe()
for _ in range(level):
if frame is None:
return '...'
frame = frame.f_back
for key, value in frame.f_locals.items():
if obj is value:
return key
for key, value in frame.f_globals.items():
if obj is value:
return key
return '...'