1. Using np.clip for Value Limitation

The clip() function is a handy tool for constraining the values within an array to a defined minimum and maximum. It requires three parameters: the target array, a minimum threshold, and a maximum threshold. Any values falling below the minimum are replaced with the minimum value, and those exceeding the maximum are capped at the maximum value.

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])

np.clip(arr, 3, 7)

Output:

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

2. Finding Indices with np.where

The where() function is useful for identifying the indices of elements in an array that satisfy a specific condition. It takes a boolean condition as input and can return different values based on whether that condition is met or not. If the alternative values are omitted, it simply returns a tuple of arrays containing the indices of the elements that meet the condition.

import numpy as np

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])

np.where(arr > 5)

Output:

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

3. Extracting Unique Elements with np.unique

The unique() function retrieves the distinct elements from an array. It accepts a single array as an argument and returns the unique values sorted in ascending order.

import numpy as np

arr = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])

np.unique(arr)

Output:

array([1, 2, 3, 4])

4. Counting Occurrences with np.bincount

The bincount() function counts how many times each integer appears in an array of non-negative integers. It takes one input: an array consisting of non-negative integers. The output is an array that enumerates the occurrences of each integer from the input.

import numpy as np

arr = np.array([1, 2, 2, 3, 3, 3, 4, 4, 4, 4])

np.bincount(arr)

Output:

array([0, 1, 2, 3, 4])

5. Applying Functions with np.vectorize

The vectorize() function allows you to apply a specific function to every element in an array. You need to provide a function that accepts a scalar input and returns a scalar output. The resulting function can then be used to apply the initial function to each element of the target array.

import numpy as np

def add_five(x):

return x + 5

arr = np.array([1, 2, 3, 4, 5])

np.vectorize(add_five)(arr)

Output:

array([ 6, 7, 8, 9, 10])