def min(self, axis=None, *args, **kwargs):
"""
Return the minimum value of the Index or minimum along
an axis.
See also
--------
numpy.ndarray.min
"""
nv.validate_min(args, kwargs)
try:
i8 = self.asi8
# quick check
if len(i8) and self.is_monotonic:
if i8[0] != iNaT:
return self._box_func(i8[0])
if self.hasnans:
min_stamp = self[~self._isnan].asi8.min()
else:
min_stamp = i8.min()
return self._box_func(min_stamp)
except ValueError:
return self._na_value
def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Index or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Series.min : Return the minimum value in a Series.
"""
nv.validate_min(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[0] != iNaT:
return self._box_func(i8[0])
if self.hasnans:
if skipna:
min_stamp = self[~self._isnan].asi8.min()
else:
return self._na_value
else:
min_stamp = i8.min()
return self._box_func(min_stamp)
except ValueError:
return self._na_value
def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Index or minimum along
an axis.
See Also
--------
numpy.ndarray.min
Series.min : Return the minimum value in a Series.
"""
nv.validate_min(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[0] != iNaT:
return self._box_func(i8[0])
if self.hasnans:
if skipna:
min_stamp = self[~self._isnan].asi8.min()
else:
return self._na_value
else:
min_stamp = i8.min()
return self._box_func(min_stamp)
except ValueError:
return self._na_value
def min(self, axis=None, *args, **kwargs):
"""
Return the minimum value of the Index or minimum along
an axis.
See also
--------
numpy.ndarray.min
"""
nv.validate_min(args, kwargs)
try:
i8 = self.asi8
# quick check
if len(i8) and self.is_monotonic:
if i8[0] != iNaT:
return self._box_func(i8[0])
if self.hasnans:
min_stamp = self[~self._isnan].asi8.min()
else:
min_stamp = i8.min()
return self._box_func(min_stamp)
except ValueError:
return self._na_value
def min(self, *, axis=None, skipna: bool = True, **kwargs) -> Scalar:
nv.validate_min((), kwargs)
result = nanops.nanmin(values=self._ndarray,
axis=axis,
mask=self.isna(),
skipna=skipna)
return self._wrap_reduction_result(axis, result)
def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
nv.validate_min((), kwargs)
return masked_reductions.min(
self._data,
self._mask,
skipna=skipna,
axis=axis,
)
def min(self, axis=None, skipna: bool = True, *args, **kwargs):
"""
Return the minimum value of the Index.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series.
skipna : bool, default True
Exclude NA/null values when showing the result.
*args, **kwargs
Additional arguments and keywords for compatibility with NumPy.
Returns
-------
scalar
Minimum value.
See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.min()
1
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.min()
'a'
For a MultiIndex, the minimum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.min()
('a', 1)
"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return nanops.nanmin(self._values, skipna=skipna)
def min(self, axis=None, skipna=True, *args, **kwargs):
"""
Return the minimum value of the Index.
Parameters
----------
axis : {None}
Dummy argument for consistency with Series
skipna : bool, default True
Returns
-------
scalar
Minimum value.
See Also
--------
Index.max : Return the maximum value of the object.
Series.min : Return the minimum value in a Series.
DataFrame.min : Return the minimum values in a DataFrame.
Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.min()
1
>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.min()
'a'
For a MultiIndex, the minimum is determined lexicographically.
>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.min()
('a', 1)
"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return nanops.nanmin(self._values, skipna=skipna)
def min(self, axis=None, skipna: bool = True, **kwargs) -> Scalar:
nv.validate_min((), kwargs)
result = masked_reductions.min(values=self.to_numpy(),
mask=self.isna(),
skipna=skipna)
return self._wrap_reduction_result(axis, result)
def min(self, *, skipna=True, **kwargs):
nv.validate_min((), kwargs)
return super()._reduce("min", skipna=skipna)
def min(self, axis=None, skipna=True, *args, **kwargs) -> int:
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return self._minmax("min")
def min(self, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_min((), dict(out=out, keepdims=keepdims))
return nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
def min(self, axis=None, out=None, keepdims=False, skipna=True):
nv.validate_min((), dict(out=out, keepdims=keepdims))
return nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
def min(self, *, skipna=True, axis: int | None = 0, **kwargs):
nv.validate_min((), kwargs)
return super()._reduce("min", skipna=skipna, axis=axis)
def min(self, axis=None, skipna=True, *args, **kwargs):
"""The minimum value of the RangeIndex"""
nv.validate_minmax_axis(axis)
nv.validate_min(args, kwargs)
return self._minmax('min')
def min(self, *, skipna: bool = True, **kwargs) -> Scalar:
nv.validate_min((), kwargs)
return masked_reductions.min(
values=self.to_numpy(), mask=self.isna(), skipna=skipna
)
