#
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# 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
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from __future__ import absolute_import
import inspect
from typing import Dict
import pandas as pd
from apache_beam.dataframe import expressions
[docs]class DeferredFrame(object):
_pandas_type_map = {} # type: Dict[type, type]
def __init__(self, expr):
self._expr = expr
@classmethod
def _register_for(cls, pandas_type):
def wrapper(deferred_type):
cls._pandas_type_map[pandas_type] = deferred_type
return deferred_type
return wrapper
[docs] @classmethod
def wrap(cls, expr):
return cls._pandas_type_map[type(expr.proxy())](expr)
def _elementwise(self, func, name=None, other_args=(), inplace=False):
return _elementwise_function(func, name, inplace=inplace)(self, *other_args)
@property
def dtypes(self):
return self._expr.proxy().dtypes
[docs]def name_and_func(method):
if isinstance(method, str):
return method, lambda df, *args, **kwargs: getattr(df, method)(*args, **
kwargs)
else:
return method.__name__, method
def _elementwise_method(func, name=None, restrictions=None, inplace=False):
if name is None:
name, func = name_and_func(func)
if restrictions is None:
restrictions = {}
return _elementwise_function(func, name, restrictions)
def _elementwise_function(func, name=None, restrictions=None, inplace=False):
if name is None:
name = func.__name__
if restrictions is None:
restrictions = {}
def wrapper(*args, **kwargs):
for key, values in restrictions.items():
if key in kwargs:
value = kwargs[key]
else:
try:
# pylint: disable=deprecated-method
ix = inspect.getargspec(func).args.index(key)
except ValueError:
# TODO: fix for delegation?
continue
if len(args) <= ix:
continue
value = args[ix]
if not isinstance(values, list):
values = [values]
if value not in values:
raise NotImplementedError(
'%s=%s not supported for %s' % (key, value, name))
deferred_arg_indices = []
deferred_arg_exprs = []
constant_args = [None] * len(args)
for ix, arg in enumerate(args):
if isinstance(arg, DeferredFrame):
deferred_arg_indices.append(ix)
deferred_arg_exprs.append(arg._expr)
elif isinstance(arg, pd.core.generic.NDFrame):
deferred_arg_indices.append(ix)
deferred_arg_exprs.append(expressions.ConstantExpression(arg, arg[0:0]))
else:
constant_args[ix] = arg
if inplace:
actual_func = copy_and_mutate(func)
else:
actual_func = func
def apply(*actual_args):
full_args = list(constant_args)
for ix, arg in zip(deferred_arg_indices, actual_args):
full_args[ix] = arg
return actual_func(*full_args, **kwargs)
result_expr = expressions.elementwise_expression(
name, apply, deferred_arg_exprs)
if inplace:
args[0]._expr = result_expr
return args[0]
else:
return DeferredFrame.wrap(result_expr)
return wrapper
[docs]def copy_and_mutate(func):
def wrapper(self, *args, **kwargs):
copy = self.copy()
func(copy, *args, **kwargs)
return copy
return wrapper
[docs]class WontImplementError(NotImplementedError):
"""An subclass of NotImplementedError to raise indicating that implementing
the given method is infeasible.
Raising this error will also prevent this doctests from being validated
when run with the beam dataframe validation doctest runner.
"""
pass