Creating NumPy Arrays

NumPy arrays are the foundation of numerical computing in Python. They offer fast operations and efficient memory usage compared to Python lists.

Installing NumPy

Before creating NumPy arrays, ensure that NumPy is installed. You can install it using pip:

            pip install numpy

Creating Arrays Using numpy.array()

NumPy arrays can be created from Python lists using the numpy.array() function.

            import numpy as np
            
            # Creating a 1D array
            arr1 = np.array([1, 2, 3, 4, 5])
            print(arr1)
            
            # Creating a 2D array
            arr2 = np.array([[1, 2, 3], [4, 5, 6]])
            print(arr2)

Output:

            [1 2 3 4 5]
            [[1 2 3]
             [4 5 6]]

Creating Arrays with Predefined Functions

NumPy provides several functions to create arrays with predefined values:

            # Creating an array of zeros
            zeros = np.zeros((2, 3))
            print(zeros)
            
            # Creating an array of ones
            ones = np.ones((3, 2))
            print(ones)
            
            # Creating an array with a range of values
            arange = np.arange(0, 10, 2)
            print(arange)

Output:

            [[0. 0. 0.]
             [0. 0. 0.]]
            
            [[1. 1.]
             [1. 1.]
             [1. 1.]]
            
            [0 2 4 6 8]

Creating Random Arrays

NumPy also provides functions to generate random arrays:

            # Creating a random array with values between 0 and 1
            rand_arr = np.random.rand(2, 3)
            print(rand_arr)
            
            # Creating a random integer array
            rand_int_arr = np.random.randint(1, 10, (2, 3))
            print(rand_int_arr)

Output:

            [[0.5488135  0.71518937 0.60276338]
             [0.54488318 0.4236548  0.64589411]]
            
            [[3 7 1]
             [8 5 2]]

Conclusion

NumPy arrays can be created in multiple ways, including converting lists, using built-in functions, or generating random numbers. Understanding these methods is essential for effective numerical computing.




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