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) nv.validate_minmax_axis(axis) 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 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 argmax(self, axis=None, skipna=True): """ Return an ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True See Also -------- numpy.ndarray.argmax """ nv.validate_minmax_axis(axis) return nanops.nanargmax(self._values, skipna=skipna)
def argmax(self, axis=None, *args, **kwargs): """ Returns the indices of the maximum values along an axis. See `numpy.ndarray.argmax` for more information on the `axis` parameter. See also -------- numpy.ndarray.argmax """ nv.validate_argmax(args, kwargs) nv.validate_minmax_axis(axis) i8 = self.asi8 if self.hasnans: mask = self._isnan if mask.all(): return -1 i8 = i8.copy() i8[mask] = 0 return i8.argmax()
def argmin(self, axis=None, skipna=True, *args, **kwargs): """ Return a ndarray of the minimum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- numpy.ndarray See Also -------- numpy.ndarray.argmin """ nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs) return nanops.nanargmin(self._values, skipna=skipna)
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 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 argmin(self, axis=None, skipna=True, *args, **kwargs): """ Returns the indices of the minimum values along an axis. See `numpy.ndarray.argmin` for more information on the `axis` parameter. See Also -------- numpy.ndarray.argmin """ nv.validate_argmin(args, kwargs) nv.validate_minmax_axis(axis) i8 = self.asi8 if self.hasnans: mask = self._isnan if mask.all() or not skipna: return -1 i8 = i8.copy() i8[mask] = np.iinfo('int64').max return i8.argmin()
def min(self, axis=None, skipna=True): """ 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) return nanops.nanmin(self._values, skipna=skipna)
def max(self, axis=None, skipna=True): """ 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) return nanops.nanmax(self._values, skipna=skipna)
def argmin(self, axis=None, skipna=True, *args, **kwargs): """ Returns the indices of the minimum values along an axis. See `numpy.ndarray.argmin` for more information on the `axis` parameter. See Also -------- numpy.ndarray.argmin """ nv.validate_argmin(args, kwargs) nv.validate_minmax_axis(axis) i8 = self.asi8 if self.hasnans: mask = self._isnan if mask.all() or not skipna: return -1 i8 = i8.copy() i8[mask] = np.iinfo("int64").max return i8.argmin()
def argmax(self, axis=None, skipna=True, *args, **kwargs): """ Return an ndarray of the maximum argument indexer. Parameters ---------- axis : {None} Dummy argument for consistency with Series skipna : bool, default True Returns ------- numpy.ndarray Indices of the maximum values. See Also -------- numpy.ndarray.argmax """ nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs) return nanops.nanargmax(self._values, skipna=skipna)
def argmax(self, axis=None, *args, **kwargs): """ Returns the indices of the maximum values along an axis. See `numpy.ndarray.argmax` for more information on the `axis` parameter. See Also -------- numpy.ndarray.argmax """ nv.validate_argmax(args, kwargs) nv.validate_minmax_axis(axis) i8 = self.asi8 if self.hasnans: mask = self._isnan if mask.all(): return -1 i8 = i8.copy() i8[mask] = 0 return i8.argmax()
def max(self, axis=None, skipna=True): """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) 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 argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int: """ Return int position of the {value} value in the Series. If the {op}imum is achieved in multiple locations, the first row position is returned. 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 ------- int Row position of the {op}imum value. See Also -------- Series.arg{op} : Return position of the {op}imum value. Series.arg{oppose} : Return position of the {oppose}imum value. numpy.ndarray.arg{op} : Equivalent method for numpy arrays. Series.idxmax : Return index label of the maximum values. Series.idxmin : Return index label of the minimum values. Examples -------- Consider dataset containing cereal calories >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: float64 >>> s.argmax() 2 >>> s.argmin() 0 The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed. """ delegate = self._values nv.validate_minmax_axis(axis) skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs) if isinstance(delegate, ExtensionArray): if not skipna and delegate.isna().any(): return -1 else: return delegate.argmax() else: return nanops.nanargmax(delegate, skipna=skipna)
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 argmin(self, axis=None, skipna=True, *args, **kwargs) -> int: nv.validate_minmax_axis(axis) nv.validate_argmax_with_skipna(skipna, args, kwargs) return nanops.nanargmin(self._values, skipna=skipna)
def max(self, axis=None, skipna=True): """The maximum value of the RangeIndex""" nv.validate_minmax_axis(axis) return self._minmax('max')
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')