actually there is a specific method for this, 'clip':

import numpy as np
my_array = np.array([[100, 200], [300, 400]],np.uint16)
my_array.clip(0,255) # clip(min, max)

output:

array([[100, 200],
       [255, 255]], dtype=uint16)

Basic numpy indexing/slicing:

x[1:-1,1:-1]

#array([[ 0.81404643,  0.87641499,  0.29969447],
#       [ 0.02642179,  0.03657303,  0.91060929],
#       [ 0.54428098,  0.21511229,  0.2177599 ]])

You can simply use np.clip as mozway suggested.

The clip function

First, we import the required libraries.

import numpy as np
import matplotlib.pyplot as plt

And then, simply define the clip function that takes the minimum and the maximum values from the user's input.

def clip(array):
    min_val, max_val = [
        float(input(i))
        for i in ["Minimum value: ", "Maximum value: "]
    ]

    return np.clip(array, min_val, max_val)

Output

We will test our clip function on a sample array.

>>> a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
>>> clip(a)

Minimum value: 3
Maximum value: 8

array([3, 3, 3, 4, 5, 6, 7, 8, 8, 8])

Plotting

We will define an arbitrary array with np.random.randint.

a = np.random.randint(0, 100, 100)
x_values = np.arange(len(a))

Finally, we clip and plot the two arrays as follows.

fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True, figsize=(9, 3),
                               tight_layout=True, dpi=144)

ax1.plot(x, a)
ax1.set_title("Unclipped Array")

ax2.plot(x, clip(a))
ax2.set_title("Clipped Array")

plt.show()

Minimum value: 25
Maximum value: 75

The above plot is our final result.