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+---
+Title: '.searchsorted()'
+Description: 'Returns the index where a value should be inserted to maintain order.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Binary Search'
+  - 'NumPy'
+  - 'Search'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+In NumPy, the **`.searchsorted()`** function returns the index where a value should be inserted to maintain order.
+
+## Syntax
+
+```pseudo
+ndarray.searchsorted(value, side='left', sorter=None)
+```
+
+**Parameters:**
+
+- `value` (array_like): Values to insert into the array.
+- `side` (default=`'left'`, optional): Determines whether to return the first suitable location (`'left'`) or last (`'right'`).
+- `sorter` (1-D array_like, optional): Specifies a pre-sorted index array for the search (used if the array isn’t sorted).
+
+**Return value:**
+
+The index (or indices) where `values` should be inserted to maintain order.
+
+## Example
+
+In this example, `.searchsorted()` finds the index where the value `3` should be inserted to keep the array sorted:
+
+```py
+import numpy as np
+
+arr = np.array([11, 22, 33, 44, 55])
+index = arr.searchsorted(33)
+print(index)
+```
+
+The result is `2`, which is the position of the first `33` in the sorted array:
+
+```shell
+2
+```
+
+## Codebyte Example
+
+In this codebyte example, `.searchsorted()` finds where a new test score should be inserted in a sorted list of scores:
+
+```codebyte/python
+import numpy as np
+
+scores = np.array([55, 60, 65, 70, 75])
+new_score = 68
+position = scores.searchsorted(new_score)
+print(position)
+```