[Term Entry] Python NumPy - ndarray: clip() (#7918)

* [Term Entry] Python NumPy - ndarray: clip()

* minor changes

* minor content fixes

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Author: johnmoore84

content/numpy/concepts/ndarray/terms/clip/clip.md

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+---
+Title: '.clip()'
+Description: 'Limits the values in an array to a specified range.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Arrays'
+  - 'Math'
+  - 'Methods'
+  - 'NumPy'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/computer-science'
+---
+
+Numpy's **`.clip()`** method limits the values in an [array](https://www.codecademy.com/resources/docs/numpy/ndarray) to a specified range by replacing values below a minimum or above a maximum with those boundary values.
+
+## Syntax
+
+```pseudo
+ndarray.clip(min=None, max=None, out=None)
+```
+
+**Parameters:**
+
+- `min`: The minimum value to clip array elements to. All values below this will be set to `min`.
+- `max`: The maximum value to clip array elements to. All values above this will be set to `max`.
+- `out`: Output array for storing the result. Must have the same shape as the input array.
+
+**Return value:**
+
+Returns an array in which all values are clipped to the specified range. If `out` is provided, the result is stored in it and a reference to `out` is returned.
+
+## Example 1: Clipping an Array Using `.clip()`
+
+In this example, `.clip()` is used without the `out` parameter to restrict all values of an array to a given range:
+
+```py
+import numpy as np
+
+# Create an array
+np_array = np.array([0, 1, 1, 2, 3, 5, 8, 13, 21])
+
+# Clip values between 3 and 9
+clipped_array = np_array.clip(min=3, max=9)
+
+# Print clipped array
+print("Clipped Array: ", clipped_array)
+```
+
+The output of this code is:
+
+```shell
+Clipped Array:  [3 3 3 3 3 5 8 9 9]
+```
+
+## Example 2: Element-Wise Clipping Using `.clip()`
+
+This example demonstrates using arrays for `min` and `max` to clip values element-wise:
+
+```py
+import numpy as np
+
+np_array = np.array([[[20, -1, 12], [2, -3, 50]]])
+output_array = np.empty_like(np_array)
+
+min_vals = np.array([[[-1, 4, 7], [10, -13, 16]]])
+max_vals = np.array([[[2, 5, 11], [13, 17, 19]]])
+
+np_array.clip(min_vals, max_vals, out=output_array)
+
+print("Clipped Array:\n", output_array)
+```
+
+The output of this code is:
+
+```shell
+Clipped Array:
+ [[[ 2  4 11]
+  [10 -3 19]]]
+```
+
+## Codebyte Example
+
+In this example, `.clip()` is provided with an integer for `min` and an array for `max`:
+
+```codebyte/python
+import numpy as np
+
+# Create an array of 10 integers
+np_array = np.array([4, 3, 7, -23, 5, 6, 4, 324, -94, 2])
+print("Array: ", np_array)
+
+# Provide an integer for min and an array of 10 for max
+clipped_array = np_array.clip(-4, [0, 1, 1, 2, 4, 7, 13, 24, 44, 81])
+print("Clipped Array: ", clipped_array)
+```