cudf.Series.searchsorted#

Series.searchsorted(values, side: Literal['left', 'right'] = 'left', ascending: bool = True, na_position: Literal['first', 'last'] = 'last')#

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

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)
```