#
# 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.
"""A module providing various functionality from the top-level pandas namespace.
"""
import re
from typing import Mapping
import pandas as pd
from apache_beam.dataframe import expressions
from apache_beam.dataframe import frame_base
from apache_beam.dataframe import partitionings
def _call_on_first_arg(name):
def wrapper(target, *args, **kwargs):
if isinstance(target, frame_base.DeferredBase):
return getattr(target, name)(*args, **kwargs)
else:
return getattr(pd, name)(target, *args, **kwargs)
return staticmethod(wrapper)
def _maybe_wrap_constant_expr(res):
if type(res) in frame_base.DeferredBase._pandas_type_map.keys():
return frame_base.DeferredBase.wrap(
expressions.ConstantExpression(res, res[0:0]))
else:
return res
def _defer_to_pandas(name):
func = getattr(pd, name)
def wrapper(*args, **kwargs):
res = func(*args, **kwargs)
return _maybe_wrap_constant_expr(res)
return staticmethod(wrapper)
def _defer_to_pandas_maybe_elementwise(name):
""" Same as _defer_to_pandas, except it handles DeferredBase args, assuming
the function can be processed elementwise. """
func = getattr(pd, name)
def wrapper(*args, **kwargs):
if any(isinstance(arg, frame_base.DeferredBase)
for arg in args + tuple(kwargs.values())):
return frame_base._elementwise_function(func, name)(*args, **kwargs)
res = func(*args, **kwargs)
return _maybe_wrap_constant_expr(res)
return staticmethod(wrapper)
def _is_top_level_function(o):
return (
callable(o) and not isinstance(o, type) and hasattr(o, '__name__') and
re.match('[a-z].*', o.__name__))
[docs]class DeferredPandasModule(object):
array = _defer_to_pandas('array')
bdate_range = _defer_to_pandas('bdate_range')
[docs] @staticmethod
@frame_base.args_to_kwargs(pd)
@frame_base.populate_defaults(pd)
def concat(
objs,
axis,
join,
ignore_index,
keys,
levels,
names,
verify_integrity,
sort,
copy):
if ignore_index:
raise NotImplementedError('concat(ignore_index)')
if levels:
raise NotImplementedError('concat(levels)')
if isinstance(objs, Mapping):
if keys is None:
keys = list(objs.keys())
objs = [objs[k] for k in keys]
else:
objs = list(objs)
if keys is None:
preserves_partitioning = partitionings.Arbitrary()
else:
# Index 0 will be a new index for keys, only partitioning by the original
# indexes (1 to N) will be preserved.
nlevels = min(o._expr.proxy().index.nlevels for o in objs)
preserves_partitioning = partitionings.Index(
[i for i in range(1, nlevels + 1)])
deferred_none = expressions.ConstantExpression(None)
exprs = [deferred_none if o is None else o._expr for o in objs]
if axis in (1, 'columns'):
required_partitioning = partitionings.Index()
elif verify_integrity:
required_partitioning = partitionings.Index()
else:
required_partitioning = partitionings.Arbitrary()
return frame_base.DeferredBase.wrap(
expressions.ComputedExpression(
'concat',
lambda *objs: pd.concat(
objs,
axis=axis,
join=join,
ignore_index=ignore_index,
keys=keys,
levels=levels,
names=names,
verify_integrity=verify_integrity), # yapf break
exprs,
requires_partition_by=required_partitioning,
preserves_partition_by=preserves_partitioning))
date_range = _defer_to_pandas('date_range')
describe_option = _defer_to_pandas('describe_option')
factorize = _call_on_first_arg('factorize')
get_option = _defer_to_pandas('get_option')
interval_range = _defer_to_pandas('interval_range')
isna = _call_on_first_arg('isna')
isnull = _call_on_first_arg('isnull')
json_normalize = _defer_to_pandas('json_normalize')
melt = _call_on_first_arg('melt')
merge = _call_on_first_arg('merge')
melt = _call_on_first_arg('melt')
merge_ordered = frame_base.wont_implement_method('order-sensitive')
notna = _call_on_first_arg('notna')
notnull = _call_on_first_arg('notnull')
option_context = _defer_to_pandas('option_context')
period_range = _defer_to_pandas('period_range')
pivot = _call_on_first_arg('pivot')
pivot_table = _call_on_first_arg('pivot_table')
show_versions = _defer_to_pandas('show_versions')
test = frame_base.wont_implement_method('test')
timedelta_range = _defer_to_pandas('timedelta_range')
to_pickle = frame_base.wont_implement_method('order-sensitive')
to_datetime = _defer_to_pandas_maybe_elementwise('to_datetime')
notna = _call_on_first_arg('notna')
def __getattr__(self, name):
if name.startswith('read_'):
return frame_base.wont_implement_method(
'Use p | apache_beam.dataframe.io.%s' % name)
res = getattr(pd, name)
if _is_top_level_function(res):
return frame_base.not_implemented_method(name)
else:
return res
pd_wrapper = DeferredPandasModule()