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)
