def argmax(self, axis=None): """ return a ndarray of the maximum argument indexer See also -------- numpy.ndarray.argmax """ return nanops.nanargmax(self.values)
def argmax(self, axis=None, skipna=True, *args, **kwargs): """ Return int position of the largest value in the Series. If the maximum 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 maximum values. See Also -------- numpy.ndarray.argmax : Equivalent method for numpy arrays. Series.argmin : Similar method, but returning the minimum. 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 The maximum cereal calories is in the third element, since series is zero-indexed. """ 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, 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, 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, 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)