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# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team 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.
"""Module houses default binary functions builder class."""
from .any import AnyDefault
import pandas
from pandas.core.dtypes.common import is_list_like
class BinaryDefault(AnyDefault):
"""Build default-to-pandas methods which executes binary functions."""
@classmethod
def build_default_to_pandas(cls, fn, fn_name):
"""
Build function that do fallback to pandas for passed binary `fn`.
Parameters
----------
fn : callable
Binary function to apply to the casted to pandas frame and other operand.
fn_name : str
Function name which will be shown in default-to-pandas warning message.
Returns
-------
callable
Function that takes query compiler, does fallback to pandas and applies binary `fn`
to the casted to pandas frame.
"""
def bin_ops_wrapper(df, other, *args, **kwargs):
"""Apply specified binary function to the passed operands."""
squeeze_other = kwargs.pop("broadcast", False) or kwargs.pop(
"squeeze_other", False
)
squeeze_self = kwargs.pop("squeeze_self", False)
if squeeze_other:
other = other.squeeze(axis=1)
if squeeze_self:
df = df.squeeze(axis=1)
result = fn(df, other, *args, **kwargs)
if (
not isinstance(result, pandas.Series)
and not isinstance(result, pandas.DataFrame)
and is_list_like(result)
):
result = pandas.DataFrame(result)
return result
return super().build_default_to_pandas(bin_ops_wrapper, fn_name)