Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
@derived_from(pd_Resampler)
def max(self):
return self._agg("max")
@derived_from(pd.core.groupby.SeriesGroupBy)
def unique(self, split_every=None, split_out=1):
name = self._meta.obj.name
return self._aca_agg(
token="unique",
func=M.unique,
aggfunc=_unique_aggregate,
aggregate_kwargs={"name": name},
split_every=split_every,
split_out=split_out,
)
if len(dsk) > 0:
is_dataframe = (
is_dataframe_like(dsk[0])
or is_series_like(dsk[0])
or is_index_like(dsk[0])
)
if array_wrap and (is_dataframe or not IS_NEP18_ACTIVE):
return dsk[0]._elemwise(__array_wrap__, numpy_ufunc, *args, **kwargs)
else:
return dsk[0]._elemwise(numpy_ufunc, *args, **kwargs)
else:
return numpy_ufunc(*args, **kwargs)
# functools.wraps cannot wrap ufunc in Python 2.x
wrapped.__name__ = numpy_ufunc.__name__
return derived_from(source)(wrapped)
@derived_from(pd.Series)
def cov(self, other, min_periods=None, split_every=False):
from .multi import concat
if not isinstance(other, Series):
raise TypeError("other must be a dask.dataframe.Series")
df = concat([self, other], axis=1)
return cov_corr(df, min_periods, scalar=True, split_every=split_every)
@derived_from(BaseSearchCV)
def score(self, X, y=None):
if self.scorer_ is None:
raise ValueError(
"No score function explicitly defined, "
"and the estimator doesn't provide one %s" % self.best_estimator_
)
return self.scorer_(self.best_estimator_, X, y)
@derived_from(pd_Resampler)
def count(self):
return self._agg("count", fill_value=0)
@derived_from(BaseSearchCV)
def predict_proba(self, X):
self._check_is_fitted('predict_proba')
return self.best_estimator_.predict_proba(X)
@derived_from(np.ndarray)
def max(self, axis=None, keepdims=False, split_every=None, out=None):
from .reductions import max
return max(self, axis=axis, keepdims=keepdims, split_every=split_every,
out=out)
@derived_from(np.ndarray)
def choose(self, choices):
from .routines import choose
return choose(self, choices)
@derived_from(pd.core.strings.StringMethods)
def cat(self, others=None, sep=None, na_rep=None):
from .core import Series, Index
if others is None:
raise NotImplementedError("x.str.cat() with `others == None`")
valid_types = (Series, Index, pd.Series, pd.Index)
if isinstance(others, valid_types):
others = [others]
elif not all(isinstance(a, valid_types) for a in others):
raise TypeError("others must be Series/Index")
return self._series.map_partitions(
str_cat, *others, sep=sep, na_rep=na_rep, meta=self._series._meta
)