Using np.array(), np.zeros(), np.ones(), np.empty(), and np.full in NumPy Framework

NumPy provides several functions to create arrays with different values. This tutorial covers np.array(), np.zeros(), np.ones(), np.empty(), and np.full() with examples.

1. Creating an Array Using np.array()

The np.array() function is used to create arrays from Python lists.

            import numpy as np
            
            arr = np.array([1, 2, 3, 4, 5])
            print(arr)

Output:

            [1 2 3 4 5]

2. Creating an Array of Zeros Using np.zeros()

The np.zeros() function creates an array filled with zeros.

            zeros_array = np.zeros((3, 3))
            print(zeros_array)

Output:

            [[0. 0. 0.]
             [0. 0. 0.]
             [0. 0. 0.]]

3. Creating an Array of Ones Using np.ones()

The np.ones() function creates an array filled with ones.

            ones_array = np.ones((2, 4))
            print(ones_array)

Output:

            [[1. 1. 1. 1.]
             [1. 1. 1. 1.]]

4. Creating an Empty Array Using np.empty()

The np.empty() function creates an uninitialized array, meaning it contains arbitrary values.

            empty_array = np.empty((2, 3))
            print(empty_array)

Output: (Values may vary)

            [[4.67296746e-307 1.69121096e-306 1.33511562e-306]
             [2.22507386e-307 1.33511969e-306 3.56043056e-307]]

5. Creating an Array with a Constant Value Using np.full()

The np.full() function creates an array filled with a specified constant value.

            full_array = np.full((3, 3), 7)
            print(full_array)

Output:

            [[7 7 7]
             [7 7 7]
             [7 7 7]]

Conclusion

NumPy provides various functions to create arrays efficiently. Understanding these functions helps in handling numerical data effectively.




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