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: 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, *, 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 )