xorbits.pandas.Series.searchsorted#
- Series.searchsorted(value: NumpyValueArrayLike | ExtensionArray, side: Literal['left', 'right'] = 'left', sorter: NumpySorter | None = None) npt.NDArray[np.intp] | np.intp [source]#
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted Series self such that, if the corresponding elements in value were inserted before the indices, the order of self would be preserved.
Note
The Series must be monotonically sorted, otherwise wrong locations will likely be returned. Pandas does not check this for you.
- Parameters
value (array-like or scalar) – Values to insert into self.
side ({'left', 'right'}, optional) – If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index. If there is no suitable index, return either 0 or N (where N is the length of self).
sorter (1-D array-like, optional) – Optional array of integer indices that sort self into ascending order. They are typically the result of
np.argsort
.
- Returns
A scalar or array of insertion points with the same shape as value.
- Return type
int or array of int
See also
sort_values
Sort by the values along either axis.
numpy.searchsorted
Similar method from NumPy.
Notes
Binary search is used to find the required insertion points.
Examples
>>> ser = pd.Series([1, 2, 3]) >>> ser 0 1 1 2 2 3 dtype: int64
>>> ser.searchsorted(4) 3
>>> ser.searchsorted([0, 4]) array([0, 3])
>>> ser.searchsorted([1, 3], side='left') array([0, 2])
>>> ser.searchsorted([1, 3], side='right') array([1, 3])
>>> ser = pd.Series(pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'])) >>> ser 0 2000-03-11 1 2000-03-12 2 2000-03-13 dtype: datetime64[ns]
>>> ser.searchsorted('3/14/2000') 3
>>> ser = pd.Categorical( ... ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True ... ) >>> ser ['apple', 'bread', 'bread', 'cheese', 'milk'] Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
>>> ser.searchsorted('bread') 1
>>> ser.searchsorted(['bread'], side='right') array([3])
If the values are not monotonically sorted, wrong locations may be returned:
>>> ser = pd.Series([2, 1, 3]) >>> ser 0 2 1 1 2 3 dtype: int64
>>> ser.searchsorted(1) 0 # wrong result, correct would be 1
Warning
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.series.Series.