searchsorted(self, value: 'NumpyValueArrayLike | ExtensionArray', side: "Literal['left', 'right']" = 'left', sorter: 'NumpySorter' = None) -> 'npt.NDArray[np.intp] | np.intp'

Find the indices into a sorted Series :None:None:`self` such that, if the corresponding elements in :None:None:`value` were inserted before the indices, the order of :None:None:`self` would be preserved.

Notes

Binary search is used to find the required insertion points.

Parameters

value : array-like or scalar

Values to insert into :None:None:`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 :None:None:`self`).

sorter : 1-D array-like, optional

Optional array of integer indices that sort :None:None:`self` into ascending order. They are typically the result of np.argsort .

Returns

int or array of int

A scalar or array of insertion points with the same shape as :None:None:`value`.

Find indices where elements should be inserted to maintain order.

See Also

numpy.searchsorted

Similar method from NumPy.

sort_values

Sort by the values along either axis.

Examples

This example is valid syntax, but we were not able to check execution

>>> ser = pd.Series([1, 2, 3])
... ser
0    1
1    2
2    3
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted(4)
3

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted([0, 4])
array([0, 3])

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted([1, 3], side='left')
array([0, 2])

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted([1, 3], side='right')
array([1, 3])

This example is valid syntax, but we were not able to check execution

>>> 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]

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted('3/14/2000')
3

This example is valid syntax, but we were not able to check execution

>>> ser = pd.Categorical(
...     ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True
... )
... ser
['apple', 'bread', 'bread', 'cheese', 'milk']
Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted('bread')
1

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted(['bread'], side='right')
array([3])

If the values are not monotonically sorted, wrong locations may be returned:

This example is valid syntax, but we were not able to check execution

>>> ser = pd.Series([2, 1, 3])
... ser
0    2
1    1
2    3
dtype: int64

This example is valid syntax, but we were not able to check execution

>>> ser.searchsorted(1)  # doctest: +SKIP
0  # wrong result, correct would be 1

See :

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File: /pandas/core/series.py#2837
type: <class 'function'>
Commit: