def wrapper(self, *args):
# It is possible for the function `f` takes other arguments than Spark Column.
# To cover this case, explicitly check if the argument is Koalas Series and
# extract Spark Column. For other arguments, they are used as are.
cols = [arg for arg in args if isinstance(arg, IndexOpsMixin)]
if all(same_anchor(self, col) for col in cols):
# Same DataFrame anchors
args = [arg.spark.column if isinstance(arg, IndexOpsMixin) else arg for arg in args]
scol = f(self.spark.column, *args)
scol = booleanize_null(self.spark.column, scol, f)
kser = self._with_new_scol(scol)
else:
# Different DataFrame anchors
def apply_func(this_column, *that_columns):
scol = f(this_column, *that_columns)
return booleanize_null(this_column, scol, f)
kser = align_diff_series(apply_func, self, *args, how="full")
if not all(self.name == col.name for col in cols):
kser = kser.rename()
return kser
def wrapper(self, *args):
# It is possible for the function `f` takes other arguments than Spark Column.
# To cover this case, explicitly check if the argument is Koalas Series and
# extract Spark Column. For other arguments, they are used as are.
cols = [arg for arg in args if isinstance(arg, IndexOpsMixin)]
if all(self._kdf is col._kdf for col in cols):
# Same DataFrame anchors
args = [arg._scol if isinstance(arg, IndexOpsMixin) else arg for arg in args]
scol = f(self._scol, *args)
return self._with_new_scol(scol)
else:
# Different DataFrame anchors
def apply_func(this_column, *that_columns):
return f(this_column, *that_columns)
return align_diff_series(apply_func, self, *args, how="full")
def wrapper(self, *args):
# It is possible for the function `f` takes other arguments than Spark Column.
# To cover this case, explicitly check if the argument is Koalas Series and
# extract Spark Column. For other arguments, they are used as are.
cols = [arg for arg in args if isinstance(arg, IndexOpsMixin)]
if all(self._kdf is col._kdf for col in cols):
# Same DataFrame anchors
args = [
arg._scol if isinstance(arg, IndexOpsMixin) else arg
for arg in args
]
scol = f(self._scol, *args)
# check if `f` is a comparison operator
comp_ops = ['eq', 'ne', 'lt', 'le', 'ge', 'gt']
is_comp_op = any(
f == getattr(spark.Column, '__{}__'.format(comp_op))
for comp_op in comp_ops)
if is_comp_op:
filler = f == spark.Column.__ne__
scol = F.when(scol.isNull(), filler).otherwise(scol)
elif f == spark.Column.__or__:
scol = F.when(self._scol.isNull() | scol.isNull(),
False).otherwise(scol)
elif f == spark.Column.__and__:
scol = F.when(scol.isNull(), False).otherwise(scol)
return self._with_new_scol(scol)
else:
# Different DataFrame anchors
def apply_func(this_column, *that_columns):
return f(this_column, *that_columns)
return align_diff_series(apply_func, self, *args, how="full")
