#
# 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.
"""Analogs for :class:`pandas.DataFrame` and :class:`pandas.Series`:
:class:`DeferredDataFrame` and :class:`DeferredSeries`.
These classes are effectively wrappers around a `schema-aware`_
:class:`~apache_beam.pvalue.PCollection` that provide a set of operations
compatible with the `pandas`_ API.
Note that we aim for the Beam DataFrame API to be completely compatible with
the pandas API, but there are some features that are currently unimplemented
for various reasons. Pay particular attention to the **'Differences from
pandas'** section for each operation to understand where we diverge.
.. _schema-aware:
https://beam.apache.org/documentation/programming-guide/#what-is-a-schema
.. _pandas:
https://pandas.pydata.org/
"""
import collections
import inspect
import itertools
import math
import re
import warnings
from typing import List
from typing import Optional
import numpy as np
import pandas as pd
from pandas.core.groupby.generic import DataFrameGroupBy
from apache_beam.dataframe import expressions
from apache_beam.dataframe import frame_base
from apache_beam.dataframe import io
from apache_beam.dataframe import partitionings
__all__ = [
'DeferredSeries',
'DeferredDataFrame',
]
def populate_not_implemented(pd_type):
def wrapper(deferred_type):
for attr in dir(pd_type):
# Don't auto-define hidden methods or dunders
if attr.startswith('_'):
continue
if not hasattr(deferred_type, attr):
pd_value = getattr(pd_type, attr)
if isinstance(pd_value, property) or inspect.isclass(pd_value):
# Some of the properties on pandas types (cat, dt, sparse), are
# actually attributes with class values, not properties
setattr(
deferred_type,
attr,
property(frame_base.not_implemented_method(attr)))
elif callable(pd_value):
setattr(deferred_type, attr, frame_base.not_implemented_method(attr))
return deferred_type
return wrapper
def _fillna_alias(method):
def wrapper(self, *args, **kwargs):
return self.fillna(*args, method=method, **kwargs)
wrapper.__name__ = method
wrapper.__doc__ = (
f'{method} is only supported for axis="columns". '
'axis="index" is order-sensitive.')
return frame_base.with_docs_from(pd.DataFrame)(
frame_base.args_to_kwargs(pd.DataFrame)(
frame_base.populate_defaults(pd.DataFrame)(wrapper)))
LIFTABLE_AGGREGATIONS = ['all', 'any', 'max', 'min', 'prod', 'sum']
LIFTABLE_WITH_SUM_AGGREGATIONS = ['size', 'count']
UNLIFTABLE_AGGREGATIONS = [
'mean',
'median',
'quantile',
'describe',
# TODO: The below all have specialized distributed
# implementations, but they require tracking
# multiple intermediate series, which is difficult
# to lift in groupby
'std',
'var',
'corr',
'cov',
'nunique'
]
ALL_AGGREGATIONS = (
LIFTABLE_AGGREGATIONS + LIFTABLE_WITH_SUM_AGGREGATIONS +
UNLIFTABLE_AGGREGATIONS)
def _agg_method(base, func):
def wrapper(self, *args, **kwargs):
return self.agg(func, *args, **kwargs)
if func in UNLIFTABLE_AGGREGATIONS:
wrapper.__doc__ = (
f"``{func}`` cannot currently be parallelized. It will "
"require collecting all data on a single node.")
wrapper.__name__ = func
return frame_base.with_docs_from(base)(wrapper)
class DeferredDataFrameOrSeries(frame_base.DeferredFrame):
__array__ = frame_base.wont_implement_method(
pd.Series, '__array__', reason="non-deferred-result")
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop(self, labels, axis, index, columns, errors, **kwargs):
"""drop is not parallelizable when dropping from the index and
``errors="raise"`` is specified. It requires collecting all data on a single
node in order to detect if one of the index values is missing."""
if labels is not None:
if index is not None or columns is not None:
raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
if axis in (0, 'index'):
index = labels
columns = None
elif axis in (1, 'columns'):
index = None
columns = labels
else:
raise ValueError(
"axis must be one of (0, 1, 'index', 'columns'), "
"got '%s'" % axis)
if columns is not None:
# Compute the proxy based on just the columns that are dropped.
proxy = self._expr.proxy().drop(columns=columns, errors=errors)
else:
proxy = self._expr.proxy()
if index is not None and errors == 'raise':
# In order to raise an error about missing index values, we'll
# need to collect the entire dataframe.
# TODO: This could be parallelized by putting index values in a
# ConstantExpression and partitioning by index.
requires = partitionings.Singleton(
reason=(
"drop(errors='raise', axis='index') is not currently "
"parallelizable. This requires collecting all data on a single "
f"node in order to detect if one of {index!r} is missing."))
else:
requires = partitionings.Arbitrary()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'drop',
lambda df: df.drop(
axis=axis,
index=index,
columns=columns,
errors=errors,
**kwargs), [self._expr],
proxy=proxy,
requires_partition_by=requires))
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def droplevel(self, level, axis):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'droplevel',
lambda df: df.droplevel(level, axis=axis), [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()
if axis in (1, 'column') else partitionings.Singleton()))
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def fillna(self, value, method, axis, limit, **kwargs):
"""When ``axis="index"``, both ``method`` and ``limit`` must be ``None``.
otherwise this operation is order-sensitive."""
# Default value is None, but is overriden with index.
axis = axis or 'index'
if axis in (0, 'index'):
if method is not None:
raise frame_base.WontImplementError(
f"fillna(method={method!r}, axis={axis!r}) is not supported "
"because it is order-sensitive. Only fillna(method=None) is "
f"supported with axis={axis!r}.",
reason="order-sensitive")
if limit is not None:
raise frame_base.WontImplementError(
f"fillna(limit={method!r}, axis={axis!r}) is not supported because "
"it is order-sensitive. Only fillna(limit=None) is supported with "
f"axis={axis!r}.",
reason="order-sensitive")
if isinstance(value, frame_base.DeferredBase):
value_expr = value._expr
else:
value_expr = expressions.ConstantExpression(value)
return frame_base.DeferredFrame.wrap(
# yapf: disable
expressions.ComputedExpression(
'fillna',
lambda df,
value: df.fillna(
value, method=method, axis=axis, limit=limit, **kwargs),
[self._expr, value_expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary()))
ffill = _fillna_alias('ffill')
bfill = _fillna_alias('bfill')
backfill = _fillna_alias('backfill')
pad = _fillna_alias('pad')
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def groupby(self, by, level, axis, as_index, group_keys, **kwargs):
"""``as_index`` and ``group_keys`` must both be ``True``.
Aggregations grouping by a categorical column with ``observed=False`` set
are not currently parallelizable
(`BEAM-11190<https://issues.apache.org/jira/browse/BEAM-11190>_`).
"""
if not as_index:
raise NotImplementedError('groupby(as_index=False)')
if not group_keys:
raise NotImplementedError('groupby(group_keys=False)')
if axis in (1, 'columns'):
return _DeferredGroupByCols(
expressions.ComputedExpression(
'groupbycols',
lambda df: df.groupby(by, axis=axis, **kwargs), [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
if level is None and by is None:
raise TypeError("You have to supply one of 'by' and 'level'")
elif level is not None:
if isinstance(level, (list, tuple)):
grouping_indexes = level
else:
grouping_indexes = [level]
grouping_columns = []
index = self._expr.proxy().index
# Translate to level numbers only
grouping_indexes = [
l if isinstance(l, int) else index.names.index(l)
for l in grouping_indexes
]
if index.nlevels == 1:
to_group_with_index = self._expr
to_group = self._expr
else:
levels_to_drop = [
i for i in range(index.nlevels) if i not in grouping_indexes
]
# Reorder so the grouped indexes are first
to_group_with_index = self.reorder_levels(
grouping_indexes + levels_to_drop)
grouping_indexes = list(range(len(grouping_indexes)))
levels_to_drop = list(range(len(grouping_indexes), index.nlevels))
if levels_to_drop:
to_group = to_group_with_index.droplevel(levels_to_drop)._expr
else:
to_group = to_group_with_index._expr
to_group_with_index = to_group_with_index._expr
elif callable(by):
def map_index(df):
df = df.copy()
df.index = df.index.map(by)
return df
to_group = expressions.ComputedExpression(
'map_index',
map_index, [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
orig_nlevels = self._expr.proxy().index.nlevels
to_group_with_index = expressions.ComputedExpression(
'map_index_keep_orig',
lambda df: df.set_index([df.index.map(by), df.index], drop=False),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
# Partitioning by the original indexes is preserved
preserves_partition_by=partitionings.Index(
list(range(1, orig_nlevels + 1))))
grouping_columns = []
# The index we need to group by is the last one
grouping_indexes = [0]
elif isinstance(by, DeferredSeries):
# TODO(BEAM-11305)
raise NotImplementedError(
"grouping by a Series is not yet implemented. You can group by a "
"DataFrame column by specifying its name.")
elif isinstance(by, np.ndarray):
raise frame_base.WontImplementError(
"Grouping by a concrete ndarray is order sensitive.",
reason="order-sensitive")
elif isinstance(self, DeferredDataFrame):
if not isinstance(by, list):
by = [by]
# Find the columns that we need to move into the index so we can group by
# them
column_names = self._expr.proxy().columns
grouping_columns = list(set(by).intersection(column_names))
index_names = self._expr.proxy().index.names
for label in by:
if label not in index_names and label not in self._expr.proxy().columns:
raise KeyError(label)
grouping_indexes = list(set(by).intersection(index_names))
if grouping_indexes:
if set(by) == set(index_names):
to_group = self._expr
elif set(by).issubset(index_names):
to_group = self.droplevel(index_names.difference(by))._expr
else:
to_group = self.reset_index(grouping_indexes).set_index(by)._expr
else:
to_group = self.set_index(by)._expr
if grouping_columns:
# TODO(BEAM-11711): It should be possible to do this without creating an
# expression manually, by using DeferredDataFrame.set_index, i.e.:
# to_group_with_index = self.set_index([self.index] +
# grouping_columns)._expr
to_group_with_index = expressions.ComputedExpression(
'move_grouped_columns_to_index',
lambda df: df.set_index([df.index] + grouping_columns, drop=False),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Index(
list(range(self._expr.proxy().index.nlevels))))
else:
to_group_with_index = self._expr
else:
raise NotImplementedError(by)
return DeferredGroupBy(
expressions.ComputedExpression(
'groupbyindex',
lambda df: df.groupby(
level=list(range(df.index.nlevels)), **kwargs), [to_group],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary()),
kwargs,
to_group,
to_group_with_index,
grouping_columns=grouping_columns,
grouping_indexes=grouping_indexes)
abs = frame_base._elementwise_method('abs', base=pd.core.generic.NDFrame)
astype = frame_base._elementwise_method(
'astype', base=pd.core.generic.NDFrame)
copy = frame_base._elementwise_method('copy', base=pd.core.generic.NDFrame)
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def tz_localize(self, ambiguous, **kwargs):
"""``ambiguous`` cannot be set to ``"infer"`` as its semantics are
order-sensitive. Similarly, specifying ``ambiguous`` as an
:class:`~numpy.ndarray` is order-sensitive, but you can achieve similar
functionality by specifying ``ambiguous`` as a Series."""
if isinstance(ambiguous, np.ndarray):
raise frame_base.WontImplementError(
"tz_localize(ambiguous=ndarray) is not supported because it makes "
"this operation sensitive to the order of the data. Please use a "
"DeferredSeries instead.",
reason="order-sensitive")
elif isinstance(ambiguous, frame_base.DeferredFrame):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'tz_localize',
lambda df,
ambiguous: df.tz_localize(ambiguous=ambiguous, **kwargs),
[self._expr, ambiguous._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Singleton()))
elif ambiguous == 'infer':
# infer attempts to infer based on the order of the timestamps
raise frame_base.WontImplementError(
f"tz_localize(ambiguous={ambiguous!r}) is not allowed because it "
"makes this operation sensitive to the order of the data.",
reason="order-sensitive")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'tz_localize',
lambda df: df.tz_localize(ambiguous=ambiguous, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton()))
@property # type: ignore
@frame_base.with_docs_from(pd.DataFrame)
def size(self):
sizes = expressions.ComputedExpression(
'get_sizes',
# Wrap scalar results in a Series for easier concatenation later
lambda df: pd.Series(df.size),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'sum_sizes',
lambda sizes: sizes.sum(), [sizes],
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
@property
def empty(self):
empties = expressions.ComputedExpression(
'get_empties',
# Wrap scalar results in a Series for easier concatenation later
lambda df: pd.Series(df.empty),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'check_all_empty',
lambda empties: empties.all(), [empties],
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
@frame_base.with_docs_from(pd.DataFrame)
def bool(self):
# TODO: Documentation about DeferredScalar
# Will throw if any partition has >1 element
bools = expressions.ComputedExpression(
'get_bools',
# Wrap scalar results in a Series for easier concatenation later
lambda df: pd.Series([], dtype=bool)
if df.empty else pd.Series([df.bool()]),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
with expressions.allow_non_parallel_operations(True):
# Will throw if overall dataset has != 1 element
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'combine_all_bools',
lambda bools: bools.bool(), [bools],
proxy=bool(),
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
@frame_base.with_docs_from(pd.DataFrame)
def equals(self, other):
intermediate = expressions.ComputedExpression(
'equals_partitioned',
# Wrap scalar results in a Series for easier concatenation later
lambda df,
other: pd.Series(df.equals(other)),
[self._expr, other._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Singleton())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'aggregate_equals',
lambda df: df.all(), [intermediate],
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def sort_values(self, axis, **kwargs):
"""``sort_values`` is not implemented.
It is not implemented for ``axis=index`` because it imposes an ordering on
the dataset, and we cannot guarantee it will be maintained (see
https://s.apache.org/dataframe-order-sensitive-operations).
It is not implemented for ``axis=columns`` because it makes the order of
the columns depend on the data (see
https://s.apache.org/dataframe-non-deferred-column-names)."""
if axis in (0, 'index'):
# axis=index imposes an ordering on the DataFrame rows which we do not
# support
raise frame_base.WontImplementError(
"sort_values(axis=index) is not supported because it imposes an "
"ordering on the dataset which we cannot guarantee will be "
"preserved.",
reason="order-sensitive")
else:
# axis=columns will reorder the columns based on the data
raise frame_base.WontImplementError(
"sort_values(axis=columns) is not supported because the order of the "
"columns in the result depends on the data.",
reason="non-deferred-columns")
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def sort_index(self, axis, **kwargs):
"""``axis=index`` is not allowed because it imposes an ordering on the
dataset, and we cannot guarantee it will be maintained (see
https://s.apache.org/dataframe-order-sensitive-operations). Only
``axis=columns`` is allowed."""
if axis in (0, 'index'):
# axis=rows imposes an ordering on the DataFrame which we do not support
raise frame_base.WontImplementError(
"sort_index(axis=index) is not supported because it imposes an "
"ordering on the dataset which we cannot guarantee will be "
"preserved.",
reason="order-sensitive")
# axis=columns reorders the columns by name
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'sort_index',
lambda df: df.sort_index(axis, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary(),
))
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def where(self, cond, other, errors, **kwargs):
"""where is not parallelizable when ``errors="ignore"`` is specified."""
requires = partitionings.Arbitrary()
deferred_args = {}
actual_args = {}
# TODO(bhulette): This is very similar to the logic in
# frame_base.elementwise_method, can we unify it?
if isinstance(cond, frame_base.DeferredFrame):
deferred_args['cond'] = cond
requires = partitionings.Index()
else:
actual_args['cond'] = cond
if isinstance(other, frame_base.DeferredFrame):
deferred_args['other'] = other
requires = partitionings.Index()
else:
actual_args['other'] = other
if errors == "ignore":
# We need all data in order to ignore errors and propagate the original
# data.
requires = partitionings.Singleton(
reason=(
f"where(errors={errors!r}) is currently not parallelizable, "
"because all data must be collected on one node to determine if "
"the original data should be propagated instead."))
actual_args['errors'] = errors
def where_execution(df, *args):
runtime_values = {
name: value
for (name, value) in zip(deferred_args.keys(), args)
}
return df.where(**runtime_values, **actual_args, **kwargs)
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
"where",
where_execution,
[self._expr] + [df._expr for df in deferred_args.values()],
requires_partition_by=requires,
preserves_partition_by=partitionings.Index(),
))
@frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def mask(self, cond, **kwargs):
"""mask is not parallelizable when ``errors="ignore"`` is specified."""
return self.where(~cond, **kwargs)
@property
def dtype(self):
return self._expr.proxy().dtype
isin = frame_base._elementwise_method('isin', base=pd.DataFrame)
combine_first = frame_base._elementwise_method(
'combine_first', base=pd.DataFrame)
combine = frame_base._proxy_method(
'combine',
base=pd.DataFrame,
requires_partition_by=expressions.partitionings.Singleton(
reason="combine() is not parallelizable because func might operate "
"on the full dataset."),
preserves_partition_by=expressions.partitionings.Singleton())
@property # type: ignore
@frame_base.with_docs_from(pd.DataFrame)
def ndim(self):
return self._expr.proxy().ndim
@property # type: ignore
@frame_base.with_docs_from(pd.DataFrame)
def index(self):
return _DeferredIndex(self)
@index.setter
def _set_index(self, value):
# TODO: assigning the index is generally order-sensitive, but we could
# support it in some rare cases, e.g. when assigning the index from one
# of a DataFrame's columns
raise NotImplementedError(
"Assigning an index is not yet supported. "
"Consider using set_index() instead.")
hist = frame_base.wont_implement_method(
pd.DataFrame, 'hist', reason="plotting-tools")
attrs = property(
frame_base.wont_implement_method(
pd.DataFrame, 'attrs', reason='experimental'))
reorder_levels = frame_base._proxy_method(
'reorder_levels',
base=pd.DataFrame,
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
[docs]@populate_not_implemented(pd.Series)
@frame_base.DeferredFrame._register_for(pd.Series)
class DeferredSeries(DeferredDataFrameOrSeries):
@property # type: ignore
@frame_base.with_docs_from(pd.Series)
def name(self):
return self._expr.proxy().name
@name.setter
def name(self, value):
def fn(s):
s = s.copy()
s.name = value
return s
self._expr = expressions.ComputedExpression(
'series_set_name',
fn, [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
@property # type: ignore
@frame_base.with_docs_from(pd.Series)
def dtype(self):
return self._expr.proxy().dtype
dtypes = dtype
def __getitem__(self, key):
if _is_null_slice(key) or key is Ellipsis:
return self
elif (isinstance(key, int) or _is_integer_slice(key)
) and self._expr.proxy().index._should_fallback_to_positional():
raise frame_base.WontImplementError(
"Accessing an item by an integer key is order sensitive for this "
"Series.",
reason="order-sensitive")
elif isinstance(key, slice) or callable(key):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
# yapf: disable
'getitem',
lambda df: df[key],
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
elif isinstance(key, DeferredSeries) and key._expr.proxy().dtype == bool:
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
# yapf: disable
'getitem',
lambda df,
indexer: df[indexer],
[self._expr, key._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary()))
elif pd.core.series.is_iterator(key) or pd.core.common.is_bool_indexer(key):
raise frame_base.WontImplementError(
"Accessing a DeferredSeries with an iterator is sensitive to the "
"order of the data.",
reason="order-sensitive")
else:
# We could consider returning a deferred scalar, but that might
# be more surprising than a clear error.
raise frame_base.WontImplementError(
f"Indexing a series with key of type {type(key)} is not supported "
"because it produces a non-deferred result.",
reason="non-deferred-result")
[docs] @frame_base.with_docs_from(pd.Series)
def keys(self):
return self.index
# Series.T == transpose, which is a no-op
T = frame_base._elementwise_method('T', base=pd.Series)
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def append(self, to_append, ignore_index, verify_integrity, **kwargs):
"""``ignore_index=True`` is not supported, because it requires generating an
order-sensitive index."""
if not isinstance(to_append, DeferredSeries):
raise frame_base.WontImplementError(
"append() only accepts DeferredSeries instances, received " +
str(type(to_append)))
if ignore_index:
raise frame_base.WontImplementError(
"append(ignore_index=True) is order sensitive because it requires "
"generating a new index based on the order of the data.",
reason="order-sensitive")
if verify_integrity:
# We can verify the index is non-unique within index partitioned data.
requires = partitionings.Index()
else:
requires = partitionings.Arbitrary()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'append',
lambda s,
to_append: s.append(
to_append, verify_integrity=verify_integrity, **kwargs),
[self._expr, to_append._expr],
requires_partition_by=requires,
preserves_partition_by=partitionings.Arbitrary()))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def align(self, other, join, axis, level, method, **kwargs):
"""Aligning per-level is not yet supported. Only the default,
``level=None``, is allowed.
Filling NaN values via ``method`` is not supported, because it is
sensitive to the order of the data
(see https://s.apache.org/dataframe-order-sensitive-operations). Only the
default, ``method=None``, is allowed.
"""
if level is not None:
raise NotImplementedError('per-level align')
if method is not None:
raise frame_base.WontImplementError(
f"align(method={method!r}) is not supported because it is "
"order sensitive. Only align(method=None) is supported.",
reason="order-sensitive")
# We're using pd.concat here as expressions don't yet support
# multiple return values.
aligned = frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'align',
lambda x,
y: pd.concat([x, y], axis=1, join='inner'),
[self._expr, other._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary()))
return aligned.iloc[:, 0], aligned.iloc[:, 1]
array = property(
frame_base.wont_implement_method(
pd.Series, 'array', reason="non-deferred-result"))
ravel = frame_base.wont_implement_method(
pd.Series, 'ravel', reason="non-deferred-result")
rename = frame_base._elementwise_method('rename', base=pd.Series)
between = frame_base._elementwise_method('between', base=pd.Series)
add_suffix = frame_base._proxy_method(
'add_suffix',
base=pd.DataFrame,
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
add_prefix = frame_base._proxy_method(
'add_prefix',
base=pd.DataFrame,
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
[docs] @frame_base.with_docs_from(pd.DataFrame)
def dot(self, other):
"""``other`` must be a :class:`DeferredDataFrame` or :class:`DeferredSeries`
instance. Computing the dot product with an array-like is not supported
because it is order-sensitive."""
left = self._expr
if isinstance(other, DeferredSeries):
right = expressions.ComputedExpression(
'to_dataframe',
pd.DataFrame, [other._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
right_is_series = True
elif isinstance(other, DeferredDataFrame):
right = other._expr
right_is_series = False
else:
raise frame_base.WontImplementError(
"other must be a DeferredDataFrame or DeferredSeries instance. "
"Passing a concrete list or numpy array is not supported. Those "
"types have no index and must be joined based on the order of the "
"data.",
reason="order-sensitive")
dots = expressions.ComputedExpression(
'dot',
# Transpose so we can sum across rows.
(lambda left, right: pd.DataFrame(left @ right).T),
[left, right],
requires_partition_by=partitionings.Index())
with expressions.allow_non_parallel_operations(True):
sums = expressions.ComputedExpression(
'sum',
lambda dots: dots.sum(), #
[dots],
requires_partition_by=partitionings.Singleton())
if right_is_series:
result = expressions.ComputedExpression(
'extract',
lambda df: df[0], [sums],
requires_partition_by=partitionings.Singleton())
else:
result = sums
return frame_base.DeferredFrame.wrap(result)
__matmul__ = dot
[docs] @frame_base.with_docs_from(pd.Series)
def std(self, *args, **kwargs):
# Compute variance (deferred scalar) with same args, then sqrt it
return self.var(*args, **kwargs).apply(lambda var: math.sqrt(var))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def var(self, axis, skipna, level, ddof, **kwargs):
"""Per-level aggregation is not yet supported (BEAM-11777). Only the
default, ``level=None``, is allowed."""
if level is not None:
raise NotImplementedError("per-level aggregation")
if skipna is None or skipna:
self = self.dropna() # pylint: disable=self-cls-assignment
# See the online, numerically stable formulae at
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
# and
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
def compute_moments(x):
n = len(x)
m = x.std(ddof=0)**2 * n
s = x.sum()
return pd.DataFrame(dict(m=[m], s=[s], n=[n]))
def combine_moments(data):
m = s = n = 0.0
for datum in data.itertuples():
if datum.n == 0:
continue
elif n == 0:
m, s, n = datum.m, datum.s, datum.n
else:
delta = s / n - datum.s / datum.n
m += datum.m + delta**2 * n * datum.n / (n + datum.n)
s += datum.s
n += datum.n
if n <= ddof:
return float('nan')
else:
return m / (n - ddof)
moments = expressions.ComputedExpression(
'compute_moments',
compute_moments, [self._expr],
requires_partition_by=partitionings.Arbitrary())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'combine_moments',
combine_moments, [moments],
requires_partition_by=partitionings.Singleton()))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def corr(self, other, method, min_periods):
"""Only ``method='pearson'`` is currently parallelizable."""
if method == 'pearson': # Note that this is the default.
x, y = self.dropna().align(other.dropna(), 'inner')
return x._corr_aligned(y, min_periods)
else:
reason = (
f"Encountered corr(method={method!r}) which cannot be "
"parallelized. Only corr(method='pearson') is currently "
"parallelizable.")
# The rank-based correlations are not obviously parallelizable, though
# perhaps an approximation could be done with a knowledge of quantiles
# and custom partitioning.
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'corr',
lambda df,
other: df.corr(other, method=method, min_periods=min_periods),
[self._expr, other._expr],
requires_partition_by=partitionings.Singleton(reason=reason)))
def _corr_aligned(self, other, min_periods):
std_x = self.std()
std_y = other.std()
cov = self._cov_aligned(other, min_periods)
return cov.apply(
lambda cov, std_x, std_y: cov / (std_x * std_y), args=[std_x, std_y])
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def cov(self, other, min_periods, ddof):
x, y = self.dropna().align(other.dropna(), 'inner')
return x._cov_aligned(y, min_periods, ddof)
def _cov_aligned(self, other, min_periods, ddof=1):
# Use the formulae from
# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance
def compute_co_moments(x, y):
n = len(x)
if n <= 1:
c = 0
else:
c = x.cov(y) * (n - 1)
sx = x.sum()
sy = y.sum()
return pd.DataFrame(dict(c=[c], sx=[sx], sy=[sy], n=[n]))
def combine_co_moments(data):
c = sx = sy = n = 0.0
for datum in data.itertuples():
if datum.n == 0:
continue
elif n == 0:
c, sx, sy, n = datum.c, datum.sx, datum.sy, datum.n
else:
c += (
datum.c + (sx / n - datum.sx / datum.n) *
(sy / n - datum.sy / datum.n) * n * datum.n / (n + datum.n))
sx += datum.sx
sy += datum.sy
n += datum.n
if n < max(2, ddof, min_periods or 0):
return float('nan')
else:
return c / (n - ddof)
moments = expressions.ComputedExpression(
'compute_co_moments',
compute_co_moments, [self._expr, other._expr],
requires_partition_by=partitionings.Index())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'combine_co_moments',
combine_co_moments, [moments],
requires_partition_by=partitionings.Singleton()))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def dropna(self, **kwargs):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'dropna',
lambda df: df.dropna(**kwargs), [self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary()))
isnull = isna = frame_base._elementwise_method('isna', base=pd.Series)
notnull = notna = frame_base._elementwise_method('notna', base=pd.Series)
items = frame_base.wont_implement_method(
pd.Series, 'items', reason="non-deferred-result")
iteritems = frame_base.wont_implement_method(
pd.Series, 'iteritems', reason="non-deferred-result")
tolist = frame_base.wont_implement_method(
pd.Series, 'tolist', reason="non-deferred-result")
to_numpy = frame_base.wont_implement_method(
pd.Series, 'to_numpy', reason="non-deferred-result")
to_string = frame_base.wont_implement_method(
pd.Series, 'to_string', reason="non-deferred-result")
def _wrap_in_df(self):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'wrap_in_df',
lambda s: pd.DataFrame(s),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary(),
))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def duplicated(self, keep):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
# Re-use the DataFrame based duplcated, extract the series back out
df = self._wrap_in_df()
return df.duplicated(keep=keep)[df.columns[0]]
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def drop_duplicates(self, keep):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
# Re-use the DataFrame based drop_duplicates, extract the series back out
df = self._wrap_in_df()
return df.drop_duplicates(keep=keep)[df.columns[0]]
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def aggregate(self, func, axis, *args, **kwargs):
"""Some aggregation methods cannot be parallelized, and computing
them will require collecting all data on a single machine."""
if kwargs.get('skipna', False):
# Eagerly generate a proxy to make sure skipna is a valid argument
# for this aggregation method
_ = self._expr.proxy().aggregate(func, axis, *args, **kwargs)
kwargs.pop('skipna')
return self.dropna().aggregate(func, axis, *args, **kwargs)
if isinstance(func, list) and len(func) > 1:
# level arg is ignored for multiple aggregations
_ = kwargs.pop('level', None)
# Aggregate with each method separately, then stick them all together.
rows = [self.agg([f], *args, **kwargs) for f in func]
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'join_aggregate',
lambda *rows: pd.concat(rows), [row._expr for row in rows]))
else:
# We're only handling a single column. It could be 'func' or ['func'],
# which produce different results. 'func' produces a scalar, ['func']
# produces a single element Series.
base_func = func[0] if isinstance(func, list) else func
if (_is_numeric(base_func) and
not pd.core.dtypes.common.is_numeric_dtype(self.dtype)):
warnings.warn(
f"Performing a numeric aggregation, {base_func!r}, on "
f"Series {self._expr.proxy().name!r} with non-numeric type "
f"{self.dtype!r}. This can result in runtime errors or surprising "
"results.")
if 'level' in kwargs:
# Defer to groupby.agg for level= mode
return self.groupby(
level=kwargs.pop('level'), axis=axis).agg(func, *args, **kwargs)
singleton_reason = None
if 'min_count' in kwargs:
# Eagerly generate a proxy to make sure min_count is a valid argument
# for this aggregation method
_ = self._expr.proxy().agg(func, axis, *args, **kwargs)
singleton_reason = (
"Aggregation with min_count= requires collecting all data on a "
"single node.")
# We have specialized distributed implementations for these
if base_func in ('quantile', 'std', 'var', 'nunique'):
result = getattr(self, base_func)(*args, axis=axis, **kwargs)
if isinstance(func, list):
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'wrap_aggregate',
lambda x: pd.Series(x, index=[base_func]), [result._expr],
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
else:
return result
agg_kwargs = kwargs.copy()
if ((_is_associative(base_func) or _is_liftable_with_sum(base_func)) and
singleton_reason is None):
intermediate = expressions.ComputedExpression(
'pre_aggregate',
# Coerce to a Series, if the result is scalar we still want a Series
# so we can combine and do the final aggregation next.
lambda s: pd.Series(s.agg(func, *args, **kwargs)),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
allow_nonparallel_final = True
if _is_associative(base_func):
agg_func = func
else:
agg_func = ['sum'] if isinstance(func, list) else 'sum'
else:
intermediate = self._expr
allow_nonparallel_final = None # i.e. don't change the value
agg_func = func
singleton_reason = (
f"Aggregation function {func!r} cannot currently be "
"parallelized. It requires collecting all data for "
"this Series on a single node.")
with expressions.allow_non_parallel_operations(allow_nonparallel_final):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'aggregate',
lambda s: s.agg(agg_func, *args, **agg_kwargs), [intermediate],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Singleton(
reason=singleton_reason)))
agg = aggregate
@property # type: ignore
@frame_base.with_docs_from(pd.Series)
def axes(self):
return [self.index]
clip = frame_base._elementwise_method('clip', base=pd.Series)
all = _agg_method(pd.Series, 'all')
any = _agg_method(pd.Series, 'any')
# TODO(BEAM-12074): Document that Series.count(level=) will drop NaN's
count = _agg_method(pd.Series, 'count')
describe = _agg_method(pd.Series, 'describe')
min = _agg_method(pd.Series, 'min')
max = _agg_method(pd.Series, 'max')
prod = product = _agg_method(pd.Series, 'prod')
sum = _agg_method(pd.Series, 'sum')
mean = _agg_method(pd.Series, 'mean')
median = _agg_method(pd.Series, 'median')
argmax = frame_base.wont_implement_method(
pd.Series, 'argmax', reason='order-sensitive')
argmin = frame_base.wont_implement_method(
pd.Series, 'argmin', reason='order-sensitive')
cummax = frame_base.wont_implement_method(
pd.Series, 'cummax', reason='order-sensitive')
cummin = frame_base.wont_implement_method(
pd.Series, 'cummin', reason='order-sensitive')
cumprod = frame_base.wont_implement_method(
pd.Series, 'cumprod', reason='order-sensitive')
cumsum = frame_base.wont_implement_method(
pd.Series, 'cumsum', reason='order-sensitive')
diff = frame_base.wont_implement_method(
pd.Series, 'diff', reason='order-sensitive')
first = frame_base.wont_implement_method(
pd.Series, 'first', reason='order-sensitive')
head = frame_base.wont_implement_method(
pd.Series, 'head', reason='order-sensitive')
interpolate = frame_base.wont_implement_method(
pd.Series, 'interpolate', reason='order-sensitive')
last = frame_base.wont_implement_method(
pd.Series, 'last', reason='order-sensitive')
searchsorted = frame_base.wont_implement_method(
pd.Series, 'searchsorted', reason='order-sensitive')
shift = frame_base.wont_implement_method(
pd.Series, 'shift', reason='order-sensitive')
tail = frame_base.wont_implement_method(
pd.Series, 'tail', reason='order-sensitive')
filter = frame_base._elementwise_method('filter', base=pd.Series)
memory_usage = frame_base.wont_implement_method(
pd.Series, 'memory_usage', reason="non-deferred-result")
# In Series __contains__ checks the index
__contains__ = frame_base.wont_implement_method(
pd.Series, '__contains__', reason="non-deferred-result")
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nlargest(self, keep, **kwargs):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
# TODO(robertwb): Document 'any' option.
# TODO(robertwb): Consider (conditionally) defaulting to 'any' if no
# explicit keep parameter is requested.
if keep == 'any':
keep = 'first'
elif keep != 'all':
raise frame_base.WontImplementError(
"nlargest(keep={keep!r}) is not supported because it is "
"order sensitive. Only keep=\"all\" is supported.",
reason="order-sensitive")
kwargs['keep'] = keep
per_partition = expressions.ComputedExpression(
'nlargest-per-partition',
lambda df: df.nlargest(**kwargs), [self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'nlargest',
lambda df: df.nlargest(**kwargs), [per_partition],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Singleton()))
[docs] @frame_base.with_docs_from(pd.Series)
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
def nsmallest(self, keep, **kwargs):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
if keep == 'any':
keep = 'first'
elif keep != 'all':
raise frame_base.WontImplementError(
"nsmallest(keep={keep!r}) is not supported because it is "
"order sensitive. Only keep=\"all\" is supported.",
reason="order-sensitive")
kwargs['keep'] = keep
per_partition = expressions.ComputedExpression(
'nsmallest-per-partition',
lambda df: df.nsmallest(**kwargs), [self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'nsmallest',
lambda df: df.nsmallest(**kwargs), [per_partition],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Singleton()))
@property # type: ignore
@frame_base.with_docs_from(pd.Series)
def is_unique(self):
def set_index(s):
s = s[:]
s.index = s
return s
self_index = expressions.ComputedExpression(
'set_index',
set_index, [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton())
is_unique_distributed = expressions.ComputedExpression(
'is_unique_distributed',
lambda s: pd.Series(s.is_unique), [self_index],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Singleton())
with expressions.allow_non_parallel_operations():
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'combine',
lambda s: s.all(), [is_unique_distributed],
requires_partition_by=partitionings.Singleton(),
preserves_partition_by=partitionings.Singleton()))
plot = frame_base.wont_implement_method(
pd.Series, 'plot', reason="plotting-tools")
pop = frame_base.wont_implement_method(
pd.Series, 'pop', reason="non-deferred-result")
rename_axis = frame_base._elementwise_method('rename_axis', base=pd.Series)
[docs] @frame_base.with_docs_from(pd.Series, name='is_unique')
@frame_base.args_to_kwargs(pd.Series)
@frame_base.populate_defaults(pd.Series)
@frame_base.maybe_inplace
def replace(self, to_replace, value, limit, method, **kwargs):
"""``method`` is not supported in the Beam DataFrame API because it is
order-sensitive. It cannot be specified.
If ``limit`` is specified this operation is not parallelizable."""
if method is not None and not isinstance(to_replace,
dict) and value is None:
# pandas only relies on method if to_replace is not a dictionary, and
# value is None
raise frame_base.WontImplementError(
f"replace(method={method!r}) is not supported because it is "
"order sensitive. Only replace(method=None) is supported.",
reason="order-sensitive")
if limit is None:
requires_partition_by = partitionings.Arbitrary()
else:
requires_partition_by = partitionings.Singleton(
reason=(
f"replace(limit={limit!r}) cannot currently be parallelized. It "
"requires collecting all data on a single node."))
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'replace',
lambda df: df.replace(
to_replace=to_replace,
value=value,
limit=limit,
method=method,
**kwargs), [self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=requires_partition_by))
round = frame_base._elementwise_method('round', base=pd.Series)
take = frame_base.wont_implement_method(
pd.Series, 'take', reason='deprecated')
to_dict = frame_base.wont_implement_method(
pd.Series, 'to_dict', reason="non-deferred-result")
to_frame = frame_base._elementwise_method('to_frame', base=pd.Series)
[docs] @frame_base.with_docs_from(pd.Series)
def unique(self, as_series=False):
"""unique is not supported by default because it produces a
non-deferred result: an :class:`~numpy.ndarray`. You can use the
Beam-specific argument ``unique(as_series=True)`` to get the result as
a :class:`DeferredSeries`"""
if not as_series:
raise frame_base.WontImplementError(
"unique() is not supported by default because it produces a "
"non-deferred result: a numpy array. You can use the Beam-specific "
"argument unique(as_series=True) to get the result as a "
"DeferredSeries",
reason="non-deferred-result")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'unique',
lambda df: pd.Series(df.unique()), [self._expr],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Singleton(
reason="unique() cannot currently be parallelized.")))
[docs] @frame_base.with_docs_from(pd.Series)
def update(self, other):
self._expr = expressions.ComputedExpression(
'update',
lambda df,
other: df.update(other) or df, [self._expr, other._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Index())
unstack = frame_base.wont_implement_method(
pd.Series, 'unstack', reason='non-deferred-columns')
values = property(
frame_base.wont_implement_method(
pd.Series, 'values', reason="non-deferred-result"))
view = frame_base.wont_implement_method(
pd.Series,
'view',
explanation=(
"because it relies on memory-sharing semantics that are "
"not compatible with the Beam model"))
@property # type: ignore
@frame_base.with_docs_from(pd.Series)
def str(self):
return _DeferredStringMethods(self._expr)
apply = frame_base._elementwise_method('apply', base=pd.Series)
map = frame_base._elementwise_method('map', base=pd.Series)
# TODO(BEAM-11636): Implement transform using type inference to determine the
# proxy
#transform = frame_base._elementwise_method('transform', base=pd.Series)
[docs]@populate_not_implemented(pd.DataFrame)
@frame_base.DeferredFrame._register_for(pd.DataFrame)
class DeferredDataFrame(DeferredDataFrameOrSeries):
@property
def T(self):
return self.transpose()
@property
def columns(self):
return self._expr.proxy().columns
@columns.setter
def columns(self, columns):
def set_columns(df):
df = df.copy()
df.columns = columns
return df
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'set_columns',
set_columns, [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
[docs] def keys(self):
return self.columns
def __getattr__(self, name):
# Column attribute access.
if name in self._expr.proxy().columns:
return self[name]
else:
return object.__getattribute__(self, name)
def __getitem__(self, key):
# TODO: Replicate pd.DataFrame.__getitem__ logic
if isinstance(key, DeferredSeries) and key._expr.proxy().dtype == bool:
return self.loc[key]
elif isinstance(key, frame_base.DeferredBase):
# Fail early if key is a DeferredBase as it interacts surprisingly with
# key in self._expr.proxy().columns
raise NotImplementedError(
"Indexing with a non-bool deferred frame is not yet supported. "
"Consider using df.loc[...]")
elif isinstance(key, slice):
if _is_null_slice(key):
return self
elif _is_integer_slice(key):
# This depends on the contents of the index.
raise frame_base.WontImplementError(
"Integer slices are not supported as they are ambiguous. Please "
"use iloc or loc with integer slices.")
else:
return self.loc[key]
elif (
(isinstance(key, list) and all(key_column in self._expr.proxy().columns
for key_column in key)) or
key in self._expr.proxy().columns):
return self._elementwise(lambda df: df[key], 'get_column')
else:
raise NotImplementedError(key)
def __contains__(self, key):
# Checks if proxy has the given column
return self._expr.proxy().__contains__(key)
def __setitem__(self, key, value):
if isinstance(
key, str) or (isinstance(key, list) and
all(isinstance(c, str)
for c in key)) or (isinstance(key, DeferredSeries) and
key._expr.proxy().dtype == bool):
# yapf: disable
return self._elementwise(
lambda df, key, value: df.__setitem__(key, value),
'set_column',
(key, value),
inplace=True)
else:
raise NotImplementedError(key)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def align(self, other, join, axis, copy, level, method, **kwargs):
if not copy:
raise frame_base.WontImplementError(
"align(copy=False) is not supported because it might be an inplace "
"operation depending on the data. Please prefer the default "
"align(copy=True).")
if method is not None:
raise frame_base.WontImplementError(
f"align(method={method!r}) is not supported because it is "
"order sensitive. Only align(method=None) is supported.",
reason="order-sensitive")
if kwargs:
raise NotImplementedError('align(%s)' % ', '.join(kwargs.keys()))
if level is not None:
# Could probably get by partitioning on the used levels.
requires_partition_by = partitionings.Singleton(reason=(
f"align(level={level}) is not currently parallelizable. Only "
"align(level=None) can be parallelized."))
elif axis in ('columns', 1):
requires_partition_by = partitionings.Arbitrary()
else:
requires_partition_by = partitionings.Index()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'align',
lambda df, other: df.align(other, join=join, axis=axis),
[self._expr, other._expr],
requires_partition_by=requires_partition_by,
preserves_partition_by=partitionings.Arbitrary()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def append(self, other, ignore_index, verify_integrity, sort, **kwargs):
if not isinstance(other, DeferredDataFrame):
raise frame_base.WontImplementError(
"append() only accepts DeferredDataFrame instances, received " +
str(type(other)))
if ignore_index:
raise frame_base.WontImplementError(
"append(ignore_index=True) is order sensitive because it requires "
"generating a new index based on the order of the data.",
reason="order-sensitive")
if verify_integrity:
# We can verify the index is non-unique within index partitioned data.
requires = partitionings.Index()
else:
requires = partitionings.Arbitrary()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'append',
lambda s, other: s.append(other, sort=sort,
verify_integrity=verify_integrity,
**kwargs),
[self._expr, other._expr],
requires_partition_by=requires,
preserves_partition_by=partitionings.Arbitrary()
)
)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def set_index(self, keys, **kwargs):
if isinstance(keys, str):
keys = [keys]
if any(isinstance(k, (_DeferredIndex, frame_base.DeferredFrame))
for k in keys):
raise NotImplementedError("set_index with Index or Series instances is "
"not yet supported (BEAM-11711)")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'set_index',
lambda df: df.set_index(keys, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton()))
@property
def loc(self):
return _DeferredLoc(self)
@property
def iloc(self):
return _DeferredILoc(self)
@property
def axes(self):
return (self.index, self.columns)
@property
def dtypes(self):
return self._expr.proxy().dtypes
[docs] def assign(self, **kwargs):
for name, value in kwargs.items():
if not callable(value) and not isinstance(value, DeferredSeries):
raise frame_base.WontImplementError(
f"Unsupported value for new column '{name}': '{value}'. Only "
"callables and DeferredSeries instances are supported. Other types "
"make this operation sensitive to the order of the data",
reason="order-sensitive")
return self._elementwise(
lambda df, *args, **kwargs: df.assign(*args, **kwargs),
'assign',
other_kwargs=kwargs)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def explode(self, column, ignore_index):
# ignoring the index will not preserve it
preserves = (partitionings.Singleton() if ignore_index
else partitionings.Index())
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'explode',
lambda df: df.explode(column, ignore_index),
[self._expr],
preserves_partition_by=preserves,
requires_partition_by=partitionings.Arbitrary()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def insert(self, value, **kwargs):
if isinstance(value, list):
raise frame_base.WontImplementMethod(
"insert(value=list) is not supported because it joins the input "
"list to the deferred DataFrame based on the order of the data.",
reason="order-sensitive")
if isinstance(value, pd.core.generic.NDFrame):
value = frame_base.DeferredFrame.wrap(
expressions.ConstantExpression(value))
if isinstance(value, frame_base.DeferredFrame):
def func_zip(df, value):
df = df.copy()
df.insert(value=value, **kwargs)
return df
inserted = frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'insert',
func_zip,
[self._expr, value._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary()))
else:
def func_elementwise(df):
df = df.copy()
df.insert(value=value, **kwargs)
return df
inserted = frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'insert',
func_elementwise,
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
self._expr = inserted._expr
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def duplicated(self, keep, subset):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
# TODO(BEAM-12074): Document keep="any"
if keep == 'any':
keep = 'first'
elif keep is not False:
raise frame_base.WontImplementError(
f"duplicated(keep={keep!r}) is not supported because it is "
"sensitive to the order of the data. Only keep=False and "
"keep=\"any\" are supported.",
reason="order-sensitive")
by = subset or list(self.columns)
# Workaround a bug where groupby.apply() that returns a single-element
# Series moves index label to column
return self.groupby(by).apply(
lambda df: pd.DataFrame(df.duplicated(keep=keep, subset=subset),
columns=[None]))[None]
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def drop_duplicates(self, keep, subset, ignore_index):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
# TODO(BEAM-12074): Document keep="any"
if keep == 'any':
keep = 'first'
elif keep is not False:
raise frame_base.WontImplementError(
f"drop_duplicates(keep={keep!r}) is not supported because it is "
"sensitive to the order of the data. Only keep=False and "
"keep=\"any\" are supported.",
reason="order-sensitive")
if ignore_index is not False:
raise frame_base.WontImplementError(
"drop_duplicates(ignore_index=False) is not supported because it "
"requires generating a new index that is sensitive to the order of "
"the data.",
reason="order-sensitive")
by = subset or list(self.columns)
return self.groupby(by).apply(
lambda df: df.drop_duplicates(keep=keep, subset=subset)).droplevel(by)
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def aggregate(self, func, axis, *args, **kwargs):
if axis is None:
# Aggregate across all elements by first aggregating across columns,
# then across rows.
return self.agg(func, *args, **dict(kwargs, axis=1)).agg(
func, *args, **dict(kwargs, axis=0))
elif axis in (1, 'columns'):
# This is an easy elementwise aggregation.
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'aggregate',
lambda df: df.agg(func, axis=1, *args, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary()))
elif len(self._expr.proxy().columns) == 0:
# For this corner case, just colocate everything.
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'aggregate',
lambda df: df.agg(func, *args, **kwargs),
[self._expr],
requires_partition_by=partitionings.Singleton()))
else:
# In the general case, we will compute the aggregation of each column
# separately, then recombine.
# First, handle any kwargs that cause a projection, by eagerly generating
# the proxy, and only including the columns that are in the output.
PROJECT_KWARGS = ('numeric_only', 'bool_only', 'include', 'exclude')
proxy = self._expr.proxy().agg(func, axis, *args, **kwargs)
if isinstance(proxy, pd.DataFrame):
projected = self[list(proxy.columns)]
elif isinstance(proxy, pd.Series):
projected = self[list(proxy.index)]
else:
projected = self
nonnumeric_columns = [name for (name, dtype) in projected.dtypes.items()
if not
pd.core.dtypes.common.is_numeric_dtype(dtype)]
if _is_numeric(func) and nonnumeric_columns:
if 'numeric_only' in kwargs and kwargs['numeric_only'] is False:
# User has opted in to execution with non-numeric columns, they
# will accept runtime errors
pass
else:
raise frame_base.WontImplementError(
f"Numeric aggregation ({func!r}) on a DataFrame containing "
f"non-numeric columns ({*nonnumeric_columns,!r} is not "
"supported, unless `numeric_only=` is specified.\n"
"Use `numeric_only=True` to only aggregate over numeric "
"columns.\nUse `numeric_only=False` to aggregate over all "
"columns. Note this is not recommended, as it could result in "
"execution time errors.")
for key in PROJECT_KWARGS:
if key in kwargs:
kwargs.pop(key)
if not isinstance(func, dict):
col_names = list(projected._expr.proxy().columns)
func = {col: func for col in col_names}
else:
col_names = list(func.keys())
aggregated_cols = []
has_lists = any(isinstance(f, list) for f in func.values())
for col in col_names:
funcs = func[col]
if has_lists and not isinstance(funcs, list):
# If any of the columns do multiple aggregations, they all must use
# "list" style output
funcs = [funcs]
aggregated_cols.append(projected[col].agg(funcs, *args, **kwargs))
# The final shape is different depending on whether any of the columns
# were aggregated by a list of aggregators.
with expressions.allow_non_parallel_operations():
if isinstance(proxy, pd.Series):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'join_aggregate',
lambda *cols: pd.Series(
{col: value for col, value in zip(col_names, cols)}),
[col._expr for col in aggregated_cols],
requires_partition_by=partitionings.Singleton(),
proxy=projected._expr.proxy().agg(func, *args, **kwargs)))
elif isinstance(proxy, pd.DataFrame):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'join_aggregate',
lambda *cols: pd.DataFrame(
{col: value for col, value in zip(col_names, cols)}),
[col._expr for col in aggregated_cols],
requires_partition_by=partitionings.Singleton()))
else:
raise AssertionError("Unexpected proxy type for "
f"DataFrame.aggregate!: proxy={proxy!r}, "
f"type(proxy)={type(proxy)!r}")
agg = aggregate
applymap = frame_base._elementwise_method('applymap', base=pd.DataFrame)
add_prefix = frame_base._elementwise_method('add_prefix', base=pd.DataFrame)
add_suffix = frame_base._elementwise_method('add_suffix', base=pd.DataFrame)
memory_usage = frame_base.wont_implement_method(
pd.DataFrame, 'memory_usage', reason="non-deferred-result")
info = frame_base.wont_implement_method(
pd.DataFrame, 'info', reason="non-deferred-result")
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def clip(self, axis, **kwargs):
"""``lower`` and ``upper`` must be :class:`DeferredSeries` instances, or
constants. Array-like arguments are not supported because they are
order-sensitive."""
if any(isinstance(kwargs.get(arg, None), frame_base.DeferredFrame)
for arg in ('upper', 'lower')) and axis not in (0, 'index'):
raise frame_base.WontImplementError(
"axis must be 'index' when upper and/or lower are a DeferredFrame",
reason='order-sensitive')
return frame_base._elementwise_method('clip', base=pd.DataFrame)(self,
axis=axis,
**kwargs)
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def corr(self, method, min_periods):
"""Only ``method="pearson"`` can be parallelized. Other methods require
collecting all data on a single worker (see
https://s.apache.org/dataframe-non-parallelizable-operations for details).
"""
if method == 'pearson':
proxy = self._expr.proxy().corr()
columns = list(proxy.columns)
args = []
arg_indices = []
for col1, col2 in itertools.combinations(columns, 2):
arg_indices.append((col1, col2))
args.append(self[col1].corr(self[col2], method=method,
min_periods=min_periods))
def fill_matrix(*args):
data = collections.defaultdict(dict)
for col in columns:
data[col][col] = 1.0
for ix, (col1, col2) in enumerate(arg_indices):
data[col1][col2] = data[col2][col1] = args[ix]
return pd.DataFrame(data, columns=columns, index=columns)
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'fill_matrix',
fill_matrix,
[arg._expr for arg in args],
requires_partition_by=partitionings.Singleton(),
proxy=proxy))
else:
reason = (f"Encountered corr(method={method!r}) which cannot be "
"parallelized. Only corr(method='pearson') is currently "
"parallelizable.")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'corr',
lambda df: df.corr(method=method, min_periods=min_periods),
[self._expr],
requires_partition_by=partitionings.Singleton(reason=reason)))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def cov(self, min_periods, ddof):
proxy = self._expr.proxy().corr()
columns = list(proxy.columns)
args = []
arg_indices = []
for col in columns:
arg_indices.append((col, col))
std = self[col].std(ddof)
args.append(std.apply(lambda x: x*x, 'square'))
for ix, col1 in enumerate(columns):
for col2 in columns[ix+1:]:
arg_indices.append((col1, col2))
# Note that this set may be different for each pair.
no_na = self.loc[self[col1].notna() & self[col2].notna()]
args.append(no_na[col1]._cov_aligned(no_na[col2], min_periods, ddof))
def fill_matrix(*args):
data = collections.defaultdict(dict)
for ix, (col1, col2) in enumerate(arg_indices):
data[col1][col2] = data[col2][col1] = args[ix]
return pd.DataFrame(data, columns=columns, index=columns)
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'fill_matrix',
fill_matrix,
[arg._expr for arg in args],
requires_partition_by=partitionings.Singleton(),
proxy=proxy))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def corrwith(self, other, axis, drop, method):
if axis not in (0, 'index'):
raise NotImplementedError('corrwith(axis=%r)' % axis)
if not isinstance(other, frame_base.DeferredFrame):
other = frame_base.DeferredFrame.wrap(
expressions.ConstantExpression(other))
if isinstance(other, DeferredSeries):
proxy = self._expr.proxy().corrwith(other._expr.proxy(), method=method)
self, other = self.align(other, axis=0, join='inner')
col_names = proxy.index
other_cols = [other] * len(col_names)
elif isinstance(other, DeferredDataFrame):
proxy = self._expr.proxy().corrwith(
other._expr.proxy(), method=method, drop=drop)
self, other = self.align(other, axis=0, join='inner')
col_names = list(
set(self.columns)
.intersection(other.columns)
.intersection(proxy.index))
other_cols = [other[col_name] for col_name in col_names]
else:
# Raise the right error.
self._expr.proxy().corrwith(other._expr.proxy())
# Just in case something else becomes valid.
raise NotImplementedError('corrwith(%s)' % type(other._expr.proxy))
# Generate expressions to compute the actual correlations.
corrs = [
self[col_name].corr(other_col, method)
for col_name, other_col in zip(col_names, other_cols)]
# Combine the results
def fill_dataframe(*args):
result = proxy.copy(deep=True)
for col, value in zip(proxy.index, args):
result[col] = value
return result
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'fill_dataframe',
fill_dataframe,
[corr._expr for corr in corrs],
requires_partition_by=partitionings.Singleton(),
proxy=proxy))
cummax = frame_base.wont_implement_method(pd.DataFrame, 'cummax',
reason='order-sensitive')
cummin = frame_base.wont_implement_method(pd.DataFrame, 'cummin',
reason='order-sensitive')
cumprod = frame_base.wont_implement_method(pd.DataFrame, 'cumprod',
reason='order-sensitive')
cumsum = frame_base.wont_implement_method(pd.DataFrame, 'cumsum',
reason='order-sensitive')
# TODO(BEAM-12071): Consider adding an order-insensitive implementation for
# diff that relies on the index
diff = frame_base.wont_implement_method(pd.DataFrame, 'diff',
reason='order-sensitive')
first = frame_base.wont_implement_method(pd.DataFrame, 'first',
reason='order-sensitive')
head = frame_base.wont_implement_method(pd.DataFrame, 'head',
reason='order-sensitive')
interpolate = frame_base.wont_implement_method(pd.DataFrame, 'interpolate',
reason='order-sensitive')
last = frame_base.wont_implement_method(pd.DataFrame, 'last',
reason='order-sensitive')
tail = frame_base.wont_implement_method(pd.DataFrame, 'tail',
reason='order-sensitive')
[docs] def dot(self, other):
# We want to broadcast the right hand side to all partitions of the left.
# This is OK, as its index must be the same size as the columns set of self,
# so cannot be too large.
class AsScalar(object):
def __init__(self, value):
self.value = value
if isinstance(other, frame_base.DeferredFrame):
proxy = other._expr.proxy()
with expressions.allow_non_parallel_operations():
side = expressions.ComputedExpression(
'as_scalar',
lambda df: AsScalar(df),
[other._expr],
requires_partition_by=partitionings.Singleton())
else:
proxy = pd.DataFrame(columns=range(len(other[0])))
side = expressions.ConstantExpression(AsScalar(other))
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'dot',
lambda left, right: left @ right.value,
[self._expr, side],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary(),
proxy=proxy))
__matmul__ = dot
[docs] def mode(self, axis=0, *args, **kwargs):
if axis == 1 or axis == 'columns':
# Number of columns is max(number mode values for each row), so we can't
# determine how many there will be before looking at the data.
raise frame_base.WontImplementError(
"mode(axis=columns) is not supported because it produces a variable "
"number of columns depending on the data.",
reason="non-deferred-columns")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'mode',
lambda df: df.mode(*args, **kwargs),
[self._expr],
#TODO(BEAM-12181): Can we add an approximate implementation?
requires_partition_by=partitionings.Singleton(reason=(
"mode(axis='index') cannot currently be parallelized. See "
"BEAM-12181 tracking the possble addition of an approximate, "
"parallelizable implementation of mode."
)),
preserves_partition_by=partitionings.Singleton()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def dropna(self, axis, **kwargs):
# TODO(robertwb): This is a common pattern. Generalize?
if axis in (1, 'columns'):
requires_partition_by = partitionings.Singleton(reason=(
"dropna(axis=1) cannot currently be parallelized. It requires "
"checking all values in each column for NaN values, to determine "
"if that column should be dropped."
))
else:
requires_partition_by = partitionings.Arbitrary()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'dropna',
lambda df: df.dropna(axis=axis, **kwargs),
[self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=requires_partition_by))
def _eval_or_query(self, name, expr, inplace, **kwargs):
for key in ('local_dict', 'global_dict', 'level', 'target', 'resolvers'):
if key in kwargs:
raise NotImplementedError(f"Setting '{key}' is not yet supported")
# look for '@<py identifier>'
if re.search(r'\@[^\d\W]\w*', expr, re.UNICODE):
raise NotImplementedError("Accessing locals with @ is not yet supported "
"(BEAM-11202)")
result_expr = expressions.ComputedExpression(
name,
lambda df: getattr(df, name)(expr, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
if inplace:
self._expr = result_expr
else:
return frame_base.DeferredFrame.wrap(result_expr)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def eval(self, expr, inplace, **kwargs):
return self._eval_or_query('eval', expr, inplace, **kwargs)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def query(self, expr, inplace, **kwargs):
return self._eval_or_query('query', expr, inplace, **kwargs)
isnull = isna = frame_base._elementwise_method('isna', base=pd.DataFrame)
notnull = notna = frame_base._elementwise_method('notna', base=pd.DataFrame)
items = frame_base.wont_implement_method(pd.DataFrame, 'items',
reason="non-deferred-result")
itertuples = frame_base.wont_implement_method(pd.DataFrame, 'itertuples',
reason="non-deferred-result")
iterrows = frame_base.wont_implement_method(pd.DataFrame, 'iterrows',
reason="non-deferred-result")
iteritems = frame_base.wont_implement_method(pd.DataFrame, 'iteritems',
reason="non-deferred-result")
def _cols_as_temporary_index(self, cols, suffix=''):
original_index_names = list(self._expr.proxy().index.names)
new_index_names = [
'__apache_beam_temp_%d_%s' % (ix, suffix)
for (ix, _) in enumerate(original_index_names)]
def reindex(df):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'reindex',
lambda df:
df.rename_axis(index=new_index_names, copy=False)
.reset_index().set_index(cols),
[df._expr],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Arbitrary()))
def revert(df):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'join_restoreindex',
lambda df:
df.reset_index().set_index(new_index_names)
.rename_axis(index=original_index_names, copy=False),
[df._expr],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Arbitrary()))
return reindex, revert
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def join(self, other, on, **kwargs):
if on is not None:
reindex, revert = self._cols_as_temporary_index(on)
return revert(reindex(self).join(other, **kwargs))
if isinstance(other, list):
other_is_list = True
else:
other = [other]
other_is_list = False
placeholder = object()
other_exprs = [
df._expr for df in other if isinstance(df, frame_base.DeferredFrame)]
const_others = [
placeholder if isinstance(df, frame_base.DeferredFrame) else df
for df in other]
def fill_placeholders(values):
values = iter(values)
filled = [
next(values) if df is placeholder else df for df in const_others]
if other_is_list:
return filled
else:
return filled[0]
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'join',
lambda df, *deferred_others: df.join(
fill_placeholders(deferred_others), **kwargs),
[self._expr] + other_exprs,
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Index()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def merge(
self,
right,
on,
left_on,
right_on,
left_index,
right_index,
suffixes,
**kwargs):
self_proxy = self._expr.proxy()
right_proxy = right._expr.proxy()
# Validate with a pandas call.
_ = self_proxy.merge(
right_proxy,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
**kwargs)
if kwargs.get('how', None) == 'cross':
raise NotImplementedError("cross join is not yet implemented (BEAM-9547)")
if not any([on, left_on, right_on, left_index, right_index]):
on = [col for col in self_proxy.columns if col in right_proxy.columns]
if not left_on:
left_on = on
if left_on and not isinstance(left_on, list):
left_on = [left_on]
if not right_on:
right_on = on
if right_on and not isinstance(right_on, list):
right_on = [right_on]
if left_index:
indexed_left = self
else:
indexed_left = self.set_index(left_on, drop=False)
if right_index:
indexed_right = right
else:
indexed_right = right.set_index(right_on, drop=False)
if left_on and right_on:
common_cols = set(left_on).intersection(right_on)
if len(common_cols):
# When merging on the same column name from both dfs, we need to make
# sure only one df has the column. Otherwise we end up with
# two duplicate columns, one with lsuffix and one with rsuffix.
# It's safe to drop from either because the data has already been duped
# to the index.
indexed_right = indexed_right.drop(columns=common_cols)
merged = frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'merge',
lambda left, right: left.merge(right,
left_index=True,
right_index=True,
suffixes=suffixes,
**kwargs),
[indexed_left._expr, indexed_right._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Index()))
if left_index or right_index:
return merged
else:
return merged.reset_index(drop=True)
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nlargest(self, keep, **kwargs):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
if keep == 'any':
keep = 'first'
elif keep != 'all':
raise frame_base.WontImplementError(
"nlargest(keep={keep!r}) is not supported because it is "
"order sensitive. Only keep=\"all\" is supported.",
reason="order-sensitive")
kwargs['keep'] = keep
per_partition = expressions.ComputedExpression(
'nlargest-per-partition',
lambda df: df.nlargest(**kwargs),
[self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'nlargest',
lambda df: df.nlargest(**kwargs),
[per_partition],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Singleton()))
[docs] @frame_base.with_docs_from(pd.DataFrame)
@frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def nsmallest(self, keep, **kwargs):
"""Only ``keep=False`` and ``keep="any"`` are supported. Other values of
``keep`` make this an order-sensitive operation. Note ``keep="any"`` is
a Beam-specific option that guarantees only one duplicate will be kept, but
unlike ``"first"`` and ``"last"`` it makes no guarantees about _which_
duplicate element is kept."""
if keep == 'any':
keep = 'first'
elif keep != 'all':
raise frame_base.WontImplementError(
"nsmallest(keep={keep!r}) is not supported because it is "
"order sensitive. Only keep=\"all\" is supported.",
reason="order-sensitive")
kwargs['keep'] = keep
per_partition = expressions.ComputedExpression(
'nsmallest-per-partition',
lambda df: df.nsmallest(**kwargs),
[self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary())
with expressions.allow_non_parallel_operations(True):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'nsmallest',
lambda df: df.nsmallest(**kwargs),
[per_partition],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=partitionings.Singleton()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
def nunique(self, **kwargs):
if kwargs.get('axis', None) in (1, 'columns'):
requires_partition_by = partitionings.Arbitrary()
preserves_partition_by = partitionings.Index()
else:
# TODO(BEAM-9547): This could be implemented in a distributed fashion,
# perhaps by deferring to a distributed drop_duplicates
requires_partition_by = partitionings.Singleton(reason=(
"nunique(axis='index') is not currently parallelizable."
))
preserves_partition_by = partitionings.Singleton()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'nunique',
lambda df: df.nunique(**kwargs),
[self._expr],
preserves_partition_by=preserves_partition_by,
requires_partition_by=requires_partition_by))
plot = frame_base.wont_implement_method(pd.DataFrame, 'plot',
reason="plotting-tools")
[docs] def pop(self, item):
result = self[item]
self._expr = expressions.ComputedExpression(
'popped',
lambda df: df.drop(columns=[item]),
[self._expr],
preserves_partition_by=partitionings.Arbitrary(),
requires_partition_by=partitionings.Arbitrary())
return result
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def quantile(self, q, axis, **kwargs):
if axis in (1, 'columns'):
if isinstance(q, list):
raise frame_base.WontImplementError(
"quantile(axis=columns) with multiple q values is not supported "
"because it transposes the input DataFrame. Note computing "
"an individual quantile across columns (e.g. "
f"df.quantile(q={q[0]!r}, axis={axis!r}) is supported.",
reason="non-deferred-columns")
else:
requires = partitionings.Arbitrary()
else: # axis='index'
# TODO(BEAM-12167): Provide an option for approximate distributed
# quantiles
requires = partitionings.Singleton(reason=(
"Computing quantiles across index cannot currently be parallelized. "
"See BEAM-12167 tracking the possible addition of an approximate, "
"parallelizable implementation of quantile."
))
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'quantile',
lambda df: df.quantile(q=q, axis=axis, **kwargs),
[self._expr],
requires_partition_by=requires,
preserves_partition_by=partitionings.Singleton()))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.maybe_inplace
def rename(self, **kwargs):
rename_index = (
'index' in kwargs
or kwargs.get('axis', None) in (0, 'index')
or ('columns' not in kwargs and 'axis' not in kwargs))
rename_columns = (
'columns' in kwargs
or kwargs.get('axis', None) in (1, 'columns'))
if rename_index:
# Technically, it's still partitioned by index, but it's no longer
# partitioned by the hash of the index.
preserves_partition_by = partitionings.Singleton()
else:
preserves_partition_by = partitionings.Index()
if kwargs.get('errors', None) == 'raise' and rename_index:
# TODO: We could do this in parallel by creating a ConstantExpression
# with a series created from the mapper dict. Then Index() partitioning
# would co-locate the necessary index values and we could raise
# individually within each partition. Execution time errors are
# discouraged anyway so probably not worth the effort.
requires_partition_by = partitionings.Singleton(reason=(
"rename(errors='raise', axis='index') requires collecting all "
"data on a single node in order to detect missing index values."
))
else:
requires_partition_by = partitionings.Arbitrary()
proxy = None
if rename_index:
# The proxy can't be computed by executing rename, it will error
# renaming the index.
if rename_columns:
# Note if both are being renamed, index and columns must be specified
# (not axis)
proxy = self._expr.proxy().rename(**{k: v for (k, v) in kwargs.items()
if not k == 'index'})
else:
# No change in columns, reuse proxy
proxy = self._expr.proxy()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'rename',
lambda df: df.rename(**kwargs),
[self._expr],
proxy=proxy,
preserves_partition_by=preserves_partition_by,
requires_partition_by=requires_partition_by))
rename_axis = frame_base._elementwise_method('rename_axis', base=pd.DataFrame)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def replace(self, limit, **kwargs):
if limit is None:
requires_partition_by = partitionings.Arbitrary()
else:
requires_partition_by = partitionings.Singleton(reason=(
f"replace(limit={limit!r}) cannot currently be parallelized. It "
"requires collecting all data on a single node."))
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'replace',
lambda df: df.replace(limit=limit, **kwargs),
[self._expr],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=requires_partition_by))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
@frame_base.maybe_inplace
def reset_index(self, level=None, **kwargs):
# TODO: Docs should note that the index is not in the same order as it would
# be with pandas. Technically an order sensitive operation
if level is not None and not isinstance(level, (tuple, list)):
level = [level]
if level is None or len(level) == self._expr.proxy().index.nlevels:
# TODO(BEAM-12182): Could do distributed re-index with offsets.
requires_partition_by = partitionings.Singleton(reason=(
"reset_index(level={level!r}) drops the entire index and creates a "
"new one, so it cannot currently be parallelized (BEAM-12182)."
))
else:
requires_partition_by = partitionings.Arbitrary()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'reset_index',
lambda df: df.reset_index(level=level, **kwargs),
[self._expr],
preserves_partition_by=partitionings.Singleton(),
requires_partition_by=requires_partition_by))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def round(self, decimals, *args, **kwargs):
if isinstance(decimals, frame_base.DeferredFrame):
# Disallow passing a deferred Series in, our current partitioning model
# prevents us from using it correctly.
raise NotImplementedError("Passing a deferred series to round() is not "
"supported, please use a concrete pd.Series "
"instance or a dictionary")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'round',
lambda df: df.round(decimals, *args, **kwargs),
[self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Index()
)
)
select_dtypes = frame_base._elementwise_method('select_dtypes',
base=pd.DataFrame)
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def shift(self, axis, freq, **kwargs):
if axis in (1, 'columns'):
preserves = partitionings.Arbitrary()
proxy = None
else:
if freq is None or 'fill_value' in kwargs:
fill_value = kwargs.get('fill_value', 'NOT SET')
raise frame_base.WontImplementError(
f"shift(axis={axis!r}) is only supported with freq defined, and "
f"fill_value undefined (got freq={freq!r},"
f"fill_value={fill_value!r}). Other configurations are sensitive "
"to the order of the data because they require populating shifted "
"rows with `fill_value`.",
reason="order-sensitive")
# proxy generation fails in pandas <1.2
# Seems due to https://github.com/pandas-dev/pandas/issues/14811,
# bug with shift on empty indexes.
# Fortunately the proxy should be identical to the input.
proxy = self._expr.proxy().copy()
# index is modified, so no partitioning is preserved.
preserves = partitionings.Singleton()
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'shift',
lambda df: df.shift(axis=axis, freq=freq, **kwargs),
[self._expr],
proxy=proxy,
preserves_partition_by=preserves,
requires_partition_by=partitionings.Arbitrary()))
shape = property(frame_base.wont_implement_method(
pd.DataFrame, 'shape', reason="non-deferred-result"))
stack = frame_base._elementwise_method('stack', base=pd.DataFrame)
all = _agg_method(pd.DataFrame, 'all')
any = _agg_method(pd.DataFrame, 'any')
count = _agg_method(pd.DataFrame, 'count')
describe = _agg_method(pd.DataFrame, 'describe')
max = _agg_method(pd.DataFrame, 'max')
min = _agg_method(pd.DataFrame, 'min')
prod = product = _agg_method(pd.DataFrame, 'prod')
sum = _agg_method(pd.DataFrame, 'sum')
mean = _agg_method(pd.DataFrame, 'mean')
median = _agg_method(pd.DataFrame, 'median')
std = _agg_method(pd.DataFrame, 'std')
var = _agg_method(pd.DataFrame, 'var')
take = frame_base.wont_implement_method(pd.DataFrame, 'take',
reason='deprecated')
to_records = frame_base.wont_implement_method(pd.DataFrame, 'to_records',
reason="non-deferred-result")
to_dict = frame_base.wont_implement_method(pd.DataFrame, 'to_dict',
reason="non-deferred-result")
to_numpy = frame_base.wont_implement_method(pd.DataFrame, 'to_numpy',
reason="non-deferred-result")
to_string = frame_base.wont_implement_method(pd.DataFrame, 'to_string',
reason="non-deferred-result")
to_sparse = frame_base.wont_implement_method(pd.DataFrame, 'to_sparse',
reason="non-deferred-result")
transpose = frame_base.wont_implement_method(
pd.DataFrame, 'transpose', reason='non-deferred-columns')
[docs] def unstack(self, *args, **kwargs):
if self._expr.proxy().index.nlevels == 1:
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'unstack',
lambda df: df.unstack(*args, **kwargs),
[self._expr],
requires_partition_by=partitionings.Index()))
else:
raise frame_base.WontImplementError(
"unstack() is not supported on DataFrames with a multiple indexes, "
"because the columns in the output depend on the input data.",
reason="non-deferred-columns")
update = frame_base._proxy_method(
'update',
inplace=True,
base=pd.DataFrame,
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary())
values = property(frame_base.wont_implement_method(
pd.DataFrame, 'values', reason="non-deferred-result"))
[docs] @frame_base.args_to_kwargs(pd.DataFrame)
@frame_base.populate_defaults(pd.DataFrame)
def melt(self, ignore_index, **kwargs):
if ignore_index:
raise frame_base.WontImplementError(
"melt(ignore_index=True) is order sensitive because it requires "
"generating a new index based on the order of the data.",
reason="order-sensitive")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'melt',
lambda df: df.melt(ignore_index=False, **kwargs), [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Singleton()))
for io_func in dir(io):
if io_func.startswith('to_'):
setattr(DeferredDataFrame, io_func, getattr(io, io_func))
setattr(DeferredSeries, io_func, getattr(io, io_func))
for meth in ('filter', ):
setattr(DeferredDataFrame, meth,
frame_base._elementwise_method(meth, base=pd.DataFrame))
@populate_not_implemented(DataFrameGroupBy)
class DeferredGroupBy(frame_base.DeferredFrame):
def __init__(self, expr, kwargs,
ungrouped: expressions.Expression,
ungrouped_with_index: expressions.Expression,
grouping_columns,
grouping_indexes,
projection=None):
"""This object represents the result of::
ungrouped.groupby(level=[grouping_indexes + grouping_columns],
**kwargs)[projection]
:param expr: An expression to compute a pandas GroupBy object. Convenient
for unliftable aggregations.
:param ungrouped: An expression to compute the DataFrame pre-grouping, the
(Multi)Index contains only the grouping columns/indexes.
:param ungrouped_with_index: Same as ungrouped, except the index includes
all of the original indexes as well as any grouping columns. This is
important for operations that expose the original index, e.g. .apply(),
but we only use it when necessary to avoid unnessary data transfer and
GBKs.
:param grouping_columns: list of column labels that were in the original
groupby(..) ``by`` parameter. Only relevant for grouped DataFrames.
:param grouping_indexes: list of index names (or index level numbers) to be
grouped.
:param kwargs: Keywords args passed to the original groupby(..) call."""
super(DeferredGroupBy, self).__init__(expr)
self._ungrouped = ungrouped
self._ungrouped_with_index = ungrouped_with_index
self._projection = projection
self._grouping_columns = grouping_columns
self._grouping_indexes = grouping_indexes
self._kwargs = kwargs
def __getattr__(self, name):
return DeferredGroupBy(
expressions.ComputedExpression(
'groupby_project',
lambda gb: getattr(gb, name), [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()),
self._kwargs,
self._ungrouped,
self._ungrouped_with_index,
self._grouping_columns,
self._grouping_indexes,
projection=name)
def __getitem__(self, name):
return DeferredGroupBy(
expressions.ComputedExpression(
'groupby_project',
lambda gb: gb[name], [self._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()),
self._kwargs,
self._ungrouped,
self._ungrouped_with_index,
self._grouping_columns,
self._grouping_indexes,
projection=name)
def agg(self, fn, *args, **kwargs):
if _is_associative(fn):
return _liftable_agg(fn)(self, *args, **kwargs)
elif _is_liftable_with_sum(fn):
return _liftable_agg(fn, postagg_meth='sum')(self, *args, **kwargs)
elif _is_unliftable(fn):
return _unliftable_agg(fn)(self, *args, **kwargs)
elif callable(fn):
return DeferredDataFrame(
expressions.ComputedExpression(
'agg',
lambda gb: gb.agg(fn, *args, **kwargs), [self._expr],
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Singleton()))
else:
raise NotImplementedError(f"GroupBy.agg(func={fn!r})")
def apply(self, fn, *args, **kwargs):
project = _maybe_project_func(self._projection)
grouping_indexes = self._grouping_indexes
grouping_columns = self._grouping_columns
# Unfortunately pandas does not execute fn to determine the right proxy.
# We run user fn on a proxy here to detect the return type and generate the
# proxy.
fn_input = project(self._ungrouped_with_index.proxy().reset_index(
grouping_columns, drop=True))
result = fn(fn_input)
if isinstance(result, pd.core.generic.NDFrame):
if result.index is fn_input.index:
proxy = result
else:
proxy = result[:0]
def index_to_arrays(index):
return [index.get_level_values(level)
for level in range(index.nlevels)]
# The final result will have the grouped indexes + the indexes from the
# result
proxy.index = pd.MultiIndex.from_arrays(
index_to_arrays(self._ungrouped.proxy().index) +
index_to_arrays(proxy.index),
names=self._ungrouped.proxy().index.names + proxy.index.names)
else:
# The user fn returns some non-pandas type. The expected result is a
# Series where each element is the result of one user fn call.
dtype = pd.Series([result]).dtype
proxy = pd.Series([], dtype=dtype, index=self._ungrouped.proxy().index)
def do_partition_apply(df):
# Remove columns from index, we only needed them there for partitioning
df = df.reset_index(grouping_columns, drop=True)
gb = df.groupby(level=grouping_indexes or None,
by=grouping_columns or None)
gb = project(gb)
return gb.apply(fn, *args, **kwargs)
return DeferredDataFrame(
expressions.ComputedExpression(
'apply',
do_partition_apply,
[self._ungrouped_with_index],
proxy=proxy,
requires_partition_by=partitionings.Index(grouping_indexes +
grouping_columns),
preserves_partition_by=partitionings.Index(grouping_indexes)))
aggregate = agg
hist = frame_base.wont_implement_method(DataFrameGroupBy, 'hist',
reason="plotting-tools")
plot = frame_base.wont_implement_method(DataFrameGroupBy, 'plot',
reason="plotting-tools")
boxplot = frame_base.wont_implement_method(DataFrameGroupBy, 'boxplot',
reason="plotting-tools")
first = frame_base.wont_implement_method(
DataFrameGroupBy, 'first', reason='order-sensitive')
last = frame_base.wont_implement_method(
DataFrameGroupBy, 'last', reason='order-sensitive')
head = frame_base.wont_implement_method(
DataFrameGroupBy, 'head', reason='order-sensitive')
tail = frame_base.wont_implement_method(
DataFrameGroupBy, 'tail', reason='order-sensitive')
nth = frame_base.wont_implement_method(
DataFrameGroupBy, 'nth', reason='order-sensitive')
cumcount = frame_base.wont_implement_method(
DataFrameGroupBy, 'cumcount', reason='order-sensitive')
cummax = frame_base.wont_implement_method(
DataFrameGroupBy, 'cummax', reason='order-sensitive')
cummin = frame_base.wont_implement_method(
DataFrameGroupBy, 'cummin', reason='order-sensitive')
cumsum = frame_base.wont_implement_method(
DataFrameGroupBy, 'cumsum', reason='order-sensitive')
cumprod = frame_base.wont_implement_method(
DataFrameGroupBy, 'cumprod', reason='order-sensitive')
diff = frame_base.wont_implement_method(DataFrameGroupBy, 'diff',
reason='order-sensitive')
shift = frame_base.wont_implement_method(DataFrameGroupBy, 'shift',
reason='order-sensitive')
# TODO(BEAM-12169): Consider allowing this for categorical keys.
__len__ = frame_base.wont_implement_method(
DataFrameGroupBy, '__len__', reason="non-deferred-result")
groups = property(frame_base.wont_implement_method(
DataFrameGroupBy, 'groups', reason="non-deferred-result"))
def _maybe_project_func(projection: Optional[List[str]]):
""" Returns identity func if projection is empty or None, else returns
a function that projects the specified columns. """
if projection:
return lambda df: df[projection]
else:
return lambda x: x
def _liftable_agg(meth, postagg_meth=None):
agg_name, _ = frame_base.name_and_func(meth)
if postagg_meth is None:
post_agg_name = agg_name
else:
post_agg_name, _ = frame_base.name_and_func(postagg_meth)
def wrapper(self, *args, **kwargs):
assert isinstance(self, DeferredGroupBy)
if 'min_count' in kwargs:
return _unliftable_agg(meth)(self, *args, **kwargs)
to_group = self._ungrouped.proxy().index
is_categorical_grouping = any(to_group.get_level_values(i).is_categorical()
for i in self._grouping_indexes)
groupby_kwargs = self._kwargs
# Don't include un-observed categorical values in the preagg
preagg_groupby_kwargs = groupby_kwargs.copy()
preagg_groupby_kwargs['observed'] = True
project = _maybe_project_func(self._projection)
pre_agg = expressions.ComputedExpression(
'pre_combine_' + agg_name,
lambda df: getattr(
project(
df.groupby(level=list(range(df.index.nlevels)),
**preagg_groupby_kwargs)
),
agg_name)(**kwargs),
[self._ungrouped],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
post_agg = expressions.ComputedExpression(
'post_combine_' + post_agg_name,
lambda df: getattr(
df.groupby(level=list(range(df.index.nlevels)),
**groupby_kwargs),
post_agg_name)(**kwargs),
[pre_agg],
requires_partition_by=(partitionings.Singleton(reason=(
"Aggregations grouped by a categorical column are not currently "
"parallelizable (BEAM-11190)."
))
if is_categorical_grouping
else partitionings.Index()),
preserves_partition_by=partitionings.Arbitrary())
return frame_base.DeferredFrame.wrap(post_agg)
return wrapper
def _unliftable_agg(meth):
agg_name, _ = frame_base.name_and_func(meth)
def wrapper(self, *args, **kwargs):
assert isinstance(self, DeferredGroupBy)
to_group = self._ungrouped.proxy().index
is_categorical_grouping = any(to_group.get_level_values(i).is_categorical()
for i in self._grouping_indexes)
groupby_kwargs = self._kwargs
project = _maybe_project_func(self._projection)
post_agg = expressions.ComputedExpression(
agg_name,
lambda df: getattr(project(
df.groupby(level=list(range(df.index.nlevels)),
**groupby_kwargs),
), agg_name)(**kwargs),
[self._ungrouped],
requires_partition_by=(partitionings.Singleton(reason=(
"Aggregations grouped by a categorical column are not currently "
"parallelizable (BEAM-11190)."
))
if is_categorical_grouping
else partitionings.Index()),
# Some aggregation methods (e.g. corr/cov) add additional index levels.
# We only preserve the ones that existed _before_ the groupby.
preserves_partition_by=partitionings.Index(
list(range(self._ungrouped.proxy().index.nlevels))))
return frame_base.DeferredFrame.wrap(post_agg)
return wrapper
for meth in LIFTABLE_AGGREGATIONS:
setattr(DeferredGroupBy, meth, _liftable_agg(meth))
for meth in LIFTABLE_WITH_SUM_AGGREGATIONS:
setattr(DeferredGroupBy, meth, _liftable_agg(meth, postagg_meth='sum'))
for meth in UNLIFTABLE_AGGREGATIONS:
setattr(DeferredGroupBy, meth, _unliftable_agg(meth))
def _check_str_or_np_builtin(agg_func, func_list):
return agg_func in func_list or (
getattr(agg_func, '__name__', None) in func_list
and agg_func.__module__ in ('numpy', 'builtins'))
def _is_associative(agg_func):
return _check_str_or_np_builtin(agg_func, LIFTABLE_AGGREGATIONS)
def _is_liftable_with_sum(agg_func):
return _check_str_or_np_builtin(agg_func, LIFTABLE_WITH_SUM_AGGREGATIONS)
def _is_unliftable(agg_func):
return _check_str_or_np_builtin(agg_func, UNLIFTABLE_AGGREGATIONS)
NUMERIC_AGGREGATIONS = ['max', 'min', 'prod', 'sum', 'mean', 'median', 'std',
'var']
def _is_numeric(agg_func):
return _check_str_or_np_builtin(agg_func, NUMERIC_AGGREGATIONS)
@populate_not_implemented(DataFrameGroupBy)
class _DeferredGroupByCols(frame_base.DeferredFrame):
# It's not clear that all of these make sense in Pandas either...
agg = aggregate = frame_base._elementwise_method('agg', base=DataFrameGroupBy)
any = frame_base._elementwise_method('any', base=DataFrameGroupBy)
all = frame_base._elementwise_method('all', base=DataFrameGroupBy)
boxplot = frame_base.wont_implement_method(
DataFrameGroupBy, 'boxplot', reason="plotting-tools")
describe = frame_base.not_implemented_method('describe')
diff = frame_base._elementwise_method('diff', base=DataFrameGroupBy)
fillna = frame_base._elementwise_method('fillna', base=DataFrameGroupBy)
filter = frame_base._elementwise_method('filter', base=DataFrameGroupBy)
first = frame_base.wont_implement_method(
DataFrameGroupBy, 'first', reason="order-sensitive")
get_group = frame_base._elementwise_method('get_group', base=DataFrameGroupBy)
head = frame_base.wont_implement_method(
DataFrameGroupBy, 'head', reason="order-sensitive")
hist = frame_base.wont_implement_method(
DataFrameGroupBy, 'hist', reason="plotting-tools")
idxmax = frame_base._elementwise_method('idxmax', base=DataFrameGroupBy)
idxmin = frame_base._elementwise_method('idxmin', base=DataFrameGroupBy)
last = frame_base.wont_implement_method(
DataFrameGroupBy, 'last', reason="order-sensitive")
mad = frame_base._elementwise_method('mad', base=DataFrameGroupBy)
max = frame_base._elementwise_method('max', base=DataFrameGroupBy)
mean = frame_base._elementwise_method('mean', base=DataFrameGroupBy)
median = frame_base._elementwise_method('median', base=DataFrameGroupBy)
min = frame_base._elementwise_method('min', base=DataFrameGroupBy)
nunique = frame_base._elementwise_method('nunique', base=DataFrameGroupBy)
plot = frame_base.wont_implement_method(
DataFrameGroupBy, 'plot', reason="plotting-tools")
prod = frame_base._elementwise_method('prod', base=DataFrameGroupBy)
quantile = frame_base._elementwise_method('quantile', base=DataFrameGroupBy)
shift = frame_base._elementwise_method('shift', base=DataFrameGroupBy)
size = frame_base._elementwise_method('size', base=DataFrameGroupBy)
skew = frame_base._elementwise_method('skew', base=DataFrameGroupBy)
std = frame_base._elementwise_method('std', base=DataFrameGroupBy)
sum = frame_base._elementwise_method('sum', base=DataFrameGroupBy)
tail = frame_base.wont_implement_method(
DataFrameGroupBy, 'tail', reason="order-sensitive")
take = frame_base.wont_implement_method(
DataFrameGroupBy, 'take', reason='deprecated')
tshift = frame_base._elementwise_method('tshift', base=DataFrameGroupBy)
var = frame_base._elementwise_method('var', base=DataFrameGroupBy)
@property
def groups(self):
return self._expr.proxy().groups
@property
def indices(self):
return self._expr.proxy().indices
@property
def ndim(self):
return self._expr.proxy().ndim
@property
def ngroups(self):
return self._expr.proxy().ngroups
@populate_not_implemented(pd.core.indexes.base.Index)
class _DeferredIndex(object):
def __init__(self, frame):
self._frame = frame
@property
def names(self):
return self._frame._expr.proxy().index.names
@names.setter
def names(self, value):
def set_index_names(df):
df = df.copy()
df.index.names = value
return df
self._frame._expr = expressions.ComputedExpression(
'set_index_names',
set_index_names,
[self._frame._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary())
@property
def ndim(self):
return self._frame._expr.proxy().index.ndim
@property
def nlevels(self):
return self._frame._expr.proxy().index.nlevels
def __getattr__(self, name):
raise NotImplementedError('index.%s' % name)
@populate_not_implemented(pd.core.indexing._LocIndexer)
class _DeferredLoc(object):
def __init__(self, frame):
self._frame = frame
def __getitem__(self, index):
if isinstance(index, tuple):
rows, cols = index
return self[rows][cols]
elif isinstance(index, list) and index and isinstance(index[0], bool):
# Aligned by numerical index.
raise NotImplementedError(type(index))
elif isinstance(index, list):
# Select rows, but behaves poorly on missing values.
raise NotImplementedError(type(index))
elif isinstance(index, slice):
args = [self._frame._expr]
func = lambda df: df.loc[index]
elif isinstance(index, frame_base.DeferredFrame):
args = [self._frame._expr, index._expr]
func = lambda df, index: df.loc[index]
elif callable(index):
def checked_callable_index(df):
computed_index = index(df)
if isinstance(computed_index, tuple):
row_index, _ = computed_index
else:
row_index = computed_index
if isinstance(row_index, list) and row_index and isinstance(
row_index[0], bool):
raise NotImplementedError(type(row_index))
elif not isinstance(row_index, (slice, pd.Series)):
raise NotImplementedError(type(row_index))
return computed_index
args = [self._frame._expr]
func = lambda df: df.loc[checked_callable_index]
else:
raise NotImplementedError(type(index))
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'loc',
func,
args,
requires_partition_by=(
partitionings.Index()
if len(args) > 1
else partitionings.Arbitrary()),
preserves_partition_by=partitionings.Arbitrary()))
__setitem__ = frame_base.not_implemented_method('loc.setitem')
@populate_not_implemented(pd.core.indexing._iLocIndexer)
class _DeferredILoc(object):
def __init__(self, frame):
self._frame = frame
def __getitem__(self, index):
if isinstance(index, tuple):
rows, _ = index
if rows != slice(None, None, None):
raise frame_base.WontImplementError(
"Using iloc to select rows is not supported because it's "
"position-based indexing is sensitive to the order of the data.",
reason="order-sensitive")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'iloc',
lambda df: df.iloc[index],
[self._frame._expr],
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
else:
raise frame_base.WontImplementError(
"Using iloc to select rows is not supported because it's "
"position-based indexing is sensitive to the order of the data.",
reason="order-sensitive")
def __setitem__(self, index, value):
raise frame_base.WontImplementError(
"Using iloc to mutate a frame is not supported because it's "
"position-based indexing is sensitive to the order of the data.",
reason="order-sensitive")
class _DeferredStringMethods(frame_base.DeferredBase):
@frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
@frame_base.populate_defaults(pd.core.strings.StringMethods)
def cat(self, others, join, **kwargs):
"""If defined, ``others`` must be a :class:`DeferredSeries` or a ``list`` of
``DeferredSeries``."""
if others is None:
# Concatenate series into a single String
requires = partitionings.Singleton(reason=(
"cat(others=None) concatenates all data in a Series into a single "
"string, so it requires collecting all data on a single node."
))
func = lambda df: df.str.cat(join=join, **kwargs)
args = [self._expr]
elif (isinstance(others, frame_base.DeferredBase) or
(isinstance(others, list) and
all(isinstance(other, frame_base.DeferredBase) for other in others))):
if isinstance(others, frame_base.DeferredBase):
others = [others]
requires = partitionings.Index()
def func(*args):
return args[0].str.cat(others=args[1:], join=join, **kwargs)
args = [self._expr] + [other._expr for other in others]
else:
raise frame_base.WontImplementError(
"others must be None, DeferredSeries, or List[DeferredSeries] "
f"(encountered {type(others)}). Other types are not supported "
"because they make this operation sensitive to the order of the "
"data.", reason="order-sensitive")
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'cat',
func,
args,
requires_partition_by=requires,
preserves_partition_by=partitionings.Arbitrary()))
@frame_base.with_docs_from(pd.core.strings.StringMethods)
@frame_base.args_to_kwargs(pd.core.strings.StringMethods)
def repeat(self, repeats):
"""``repeats`` must be an ``int`` or a :class:`DeferredSeries`. Lists are
not supported because they make this operation order-sensitive."""
if isinstance(repeats, int):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'repeat',
lambda series: series.str.repeat(repeats),
[self._expr],
# TODO(BEAM-11155): Defer to pandas to compute this proxy.
# Currently it incorrectly infers dtype bool, may require upstream
# fix.
proxy=self._expr.proxy(),
requires_partition_by=partitionings.Arbitrary(),
preserves_partition_by=partitionings.Arbitrary()))
elif isinstance(repeats, frame_base.DeferredBase):
return frame_base.DeferredFrame.wrap(
expressions.ComputedExpression(
'repeat',
lambda series, repeats_series: series.str.repeat(repeats_series),
[self._expr, repeats._expr],
# TODO(BEAM-11155): Defer to pandas to compute this proxy.
# Currently it incorrectly infers dtype bool, may require upstream
# fix.
proxy=self._expr.proxy(),
requires_partition_by=partitionings.Index(),
preserves_partition_by=partitionings.Arbitrary()))
elif isinstance(repeats, list):
raise frame_base.WontImplementError(
"str.repeat(repeats=) repeats must be an int or a DeferredSeries. "
"Lists are not supported because they make this operation sensitive "
"to the order of the data.", reason="order-sensitive")
else:
raise TypeError("str.repeat(repeats=) value must be an int or a "
f"DeferredSeries (encountered {type(repeats)}).")
get_dummies = frame_base.wont_implement_method(
pd.core.strings.StringMethods, 'get_dummies',
reason='non-deferred-columns')
ELEMENTWISE_STRING_METHODS = [
'capitalize',
'casefold',
'contains',
'count',
'endswith',
'extract',
'extractall',
'findall',
'fullmatch',
'get',
'isalnum',
'isalpha',
'isdecimal',
'isdigit',
'islower',
'isnumeric',
'isspace',
'istitle',
'isupper',
'join',
'len',
'lower',
'lstrip',
'match',
'pad',
'partition',
'replace',
'rpartition',
'rsplit',
'rstrip',
'slice',
'slice_replace',
'split',
'startswith',
'strip',
'swapcase',
'title',
'upper',
'wrap',
'zfill',
'__getitem__',
]
def make_str_func(method):
def func(df, *args, **kwargs):
try:
df_str = df.str
except AttributeError:
# If there's a non-string value in a Series passed to .str method, pandas
# will generally just replace it with NaN in the result. However if
# there are _only_ non-string values, pandas will raise:
#
# AttributeError: Can only use .str accessor with string values!
#
# This can happen to us at execution time if we split a partition that is
# only non-strings. This branch just replaces all those values with NaN
# in that case.
return df.map(lambda _: np.nan)
else:
return getattr(df_str, method)(*args, **kwargs)
return func
for method in ELEMENTWISE_STRING_METHODS:
setattr(_DeferredStringMethods,
method,
frame_base._elementwise_method(make_str_func(method),
name=method,
base=pd.core.strings.StringMethods))
for base in ['add',
'sub',
'mul',
'div',
'truediv',
'floordiv',
'mod',
'divmod',
'pow',
'and',
'or']:
for p in ['%s', 'r%s', '__%s__', '__r%s__']:
# TODO: non-trivial level?
name = p % base
if hasattr(pd.Series, name):
setattr(
DeferredSeries,
name,
frame_base._elementwise_method(name, restrictions={'level': None},
base=pd.Series))
if hasattr(pd.DataFrame, name):
setattr(
DeferredDataFrame,
name,
frame_base._elementwise_method(name, restrictions={'level': None},
base=pd.DataFrame))
inplace_name = '__i%s__' % base
if hasattr(pd.Series, inplace_name):
setattr(
DeferredSeries,
inplace_name,
frame_base._elementwise_method(inplace_name, inplace=True,
base=pd.Series))
if hasattr(pd.DataFrame, inplace_name):
setattr(
DeferredDataFrame,
inplace_name,
frame_base._elementwise_method(inplace_name, inplace=True,
base=pd.DataFrame))
for name in ['lt', 'le', 'gt', 'ge', 'eq', 'ne']:
for p in '%s', '__%s__':
# Note that non-underscore name is used for both as the __xxx__ methods are
# order-sensitive.
setattr(DeferredSeries, p % name,
frame_base._elementwise_method(name, base=pd.Series))
setattr(DeferredDataFrame, p % name,
frame_base._elementwise_method(name, base=pd.DataFrame))
for name in ['__neg__', '__pos__', '__invert__']:
setattr(DeferredSeries, name,
frame_base._elementwise_method(name, base=pd.Series))
setattr(DeferredDataFrame, name,
frame_base._elementwise_method(name, base=pd.DataFrame))
DeferredSeries.multiply = DeferredSeries.mul # type: ignore
DeferredDataFrame.multiply = DeferredDataFrame.mul # type: ignore
def _slice_parts(s):
yield s.start
yield s.stop
yield s.step
def _is_null_slice(s):
return isinstance(s, slice) and all(x is None for x in _slice_parts(s))
def _is_integer_slice(s):
return isinstance(s, slice) and all(
x is None or isinstance(x, int)
for x in _slice_parts(s)) and not _is_null_slice(s)