cudf.Series.searchsorted#

Series.searchsorted(values, side: Literal['left', 'right'] = 'left', sorter=None, ascending: bool = True, na_position: Literal['first', 'last'] = 'last') → ScalarLike | cupy.ndarray[source]#

Find indices where elements should be inserted to maintain order

Parameters:

valueFrame (Shape must be consistent with self): Values to be hypothetically inserted into Self
sidestr {‘left’, ‘right’} optional, default ‘left’: If ‘left’, the index of the first suitable location found is given If ‘right’, return the last such index
sorter1-D array-like, optional: Optional array of integer indices that sort self into ascending order. They are typically the result of np.argsort. Currently not supported.
ascendingbool optional, default True: Sorted Frame is in ascending order (otherwise descending)
na_positionstr {‘last’, ‘first’} optional, default ‘last’: Position of null values in sorted order

Returns:

1-D cupy array of insertion points

Examples

>>> s = cudf.Series([1, 2, 3])
>>> s.searchsorted(4)
3
>>> s.searchsorted([0, 4])
array([0, 3], dtype=int32)
>>> s.searchsorted([1, 3], side='left')
array([0, 2], dtype=int32)
>>> s.searchsorted([1, 3], side='right')
array([1, 3], dtype=int32)

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

>>> s = cudf.Series([2, 1, 3])
>>> s.searchsorted(1)
0   # wrong result, correct would be 1

>>> df = cudf.DataFrame({'a': [1, 3, 5, 7], 'b': [10, 12, 14, 16]})
>>> df
   a   b
0  1  10
1  3  12
2  5  14
3  7  16
>>> values_df = cudf.DataFrame({'a': [0, 2, 5, 6],
... 'b': [10, 11, 13, 15]})
>>> values_df
   a   b
0  0  10
1  2  17
2  5  13
3  6  15
>>> df.searchsorted(values_df, ascending=False)
array([4, 4, 4, 0], dtype=int32)