def max(self, axis=None, *args, **kwargs): """ Return the maximum value of the Index or maximum along an axis. See also -------- numpy.ndarray.max """ nv.validate_max(args, kwargs) try: i8 = self.asi8 # quick check if len(i8) and self.is_monotonic: if i8[-1] != iNaT: return self._box_func(i8[-1]) if self.hasnans: max_stamp = self[~self._isnan].asi8.max() else: max_stamp = i8.max() return self._box_func(max_stamp) except ValueError: return self._na_value
def max(self, axis=None, skipna=True, *args, **kwargs): """ Return the maximum value of the Index or maximum along an axis. See Also -------- numpy.ndarray.max Series.max : Return the maximum value in a Series. """ nv.validate_max(args, kwargs) nv.validate_minmax_axis(axis) if not len(self): return self._na_value i8 = self.asi8 try: # quick check if len(i8) and self.is_monotonic: if i8[-1] != iNaT: return self._box_func(i8[-1]) if self.hasnans: if skipna: max_stamp = self[~self._isnan].asi8.max() else: return self._na_value else: max_stamp = i8.max() return self._box_func(max_stamp) except ValueError: return self._na_value
def max(self, axis=None, skipna=True, *args, **kwargs): """ Return the maximum value of the Index. Parameters ---------- axis : int, optional For compatibility with NumPy. Only 0 or None are allowed. skipna : bool, default True Returns ------- scalar Maximum value. See Also -------- Index.min : Return the minimum value in an Index. Series.max : Return the maximum value in a Series. DataFrame.max : Return the maximum values in a DataFrame. Examples -------- >>> idx = pd.Index([3, 2, 1]) >>> idx.max() 3 >>> idx = pd.Index(['c', 'b', 'a']) >>> idx.max() 'c' For a MultiIndex, the maximum is determined lexicographically. >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)]) >>> idx.max() ('b', 2) """ nv.validate_minmax_axis(axis) nv.validate_max(args, kwargs) return nanops.nanmax(self._values, skipna=skipna)
def max(self, axis=None, out=None, keepdims=False, skipna=True): nv.validate_max((), dict(out=out, keepdims=keepdims)) return nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
def max(self, axis=None, skipna=True, *args, **kwargs): """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) nv.validate_max(args, kwargs) return self._minmax('max')
def max(self, axis=None, skipna=True, *args, **kwargs) -> int: """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) nv.validate_max(args, kwargs) return self._minmax("max")
def max(self, *, skipna=True, **kwargs): nv.validate_max((), kwargs) return super()._reduce("max", skipna=skipna)
def max(self, axis=None, out=None, keepdims=False, skipna=True): nv.validate_max((), dict(out=out, keepdims=keepdims)) return nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
def max(self, *, axis=None, skipna: bool = True, **kwargs) -> Scalar: nv.validate_max((), kwargs) result = nanops.nanmax( values=self._ndarray, axis=axis, mask=self.isna(), skipna=skipna ) return self._wrap_reduction_result(axis, result)