# numpy.clip() in Python

`numpy.clip()` function is used to Clip (limit) the values in an array.

Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.

Syntax : numpy.clip(a, a_min, a_max, out=None)

Parameters :
a : Array containing elements to clip.
a_min : Minimum value.
–> If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.
a_max : Maximum value.
–> If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.
–> If a_min or a_max are array_like, then the three arrays will be broadcasted to match their shapes.
out : Results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved.

Return : clipped_array

Code #1 :

 `# Python3 code demonstrate clip() function ` ` `  `# importing the numpy ` `import` `numpy as np ` ` `  `in_array ``=` `[``1``, ``2``, ``3``, ``4``, ``5``, ``6``, ``7``, ``8` `] ` `print` `(``"Input array : "``, in_array) ` ` `  `out_array ``=` `np.clip(in_array, a_min ``=` `2``, a_max ``=` `6``) ` `print` `(``"Output array : "``, out_array) `

Output :

```Input array :  [1, 2, 3, 4, 5, 6, 7, 8]
Output array :  [2 2 3 4 5 6 6 6]```

Code #2 :

 `# Python3 code demonstrate clip() function ` ` `  `# importing the numpy ` `import` `numpy as np ` ` `  `in_array ``=` `[``1``, ``2``, ``3``, ``4``, ``5``, ``6``, ``7``, ``8``, ``9``, ``10``] ` `print` `(``"Input array : "``, in_array) ` ` `  `out_array ``=` `np.clip(in_array, a_min ``=``[``3``, ``4``, ``1``, ``1``, ``1``, ``4``, ``4``, ``4``, ``4``, ``4``], ` `                                                         ``a_max ``=` `9``) ` `print` `(``"Output array : "``, out_array) `

Output :

```Input array :  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
Output array :  [3 4 3 4 5 6 7 8 9 9]
```

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