Overview of NumPy Framework

NumPy (Numerical Python) is a fundamental package for numerical computing in Python. It provides support for large arrays and matrices, along with mathematical functions to manipulate these data structures efficiently.

Installing NumPy

To start using NumPy, install it using pip:

            pip install numpy

Creating NumPy Arrays

NumPy arrays can be created using the array function:

            import numpy as np
            
            # Creating a 1D array
            arr = np.array([10, 20, 30, 40, 50])
            print(arr)

Output:

            [10 20 30 40 50]

Array Operations

NumPy supports various operations on arrays:

            a = np.array([2, 4, 6])
            b = np.array([1, 3, 5])
            
            print(a + b)  # Addition
            print(a - b)  # Subtraction
            print(a * b)  # Multiplication
            print(a / b)  # Division

Output:

            [3 7 11]
            [1 1 1]
            [ 2 12 30]
            [2. 1.33333333 1.2]

Array Shape and Reshaping

You can check and modify the shape of an array:

            c = np.array([[10, 20, 30], [40, 50, 60]])
            print(c.shape)  # Output: (2, 3)
            
            d = c.reshape(3, 2)
            print(d)

Output:

            (2, 3)
            [[10 20]
             [30 40]
             [50 60]]

Generating Special Arrays

NumPy provides functions for creating specific types of arrays:

            print(np.zeros((3, 3)))  # 3x3 array of zeros
            print(np.ones((2, 2)))   # 2x2 array of ones
            print(np.eye(4))         # 4x4 identity matrix
            print(np.arange(5, 25, 5))  # Array with step size 5

Output:

            [[0. 0. 0.]
             [0. 0. 0.]
             [0. 0. 0.]]
            
            [[1. 1.]
             [1. 1.]]
            
            [[1. 0. 0. 0.]
             [0. 1. 0. 0.]
             [0. 0. 1. 0.]
             [0. 0. 0. 1.]]
            
            [ 5 10 15 20]

Conclusion

NumPy is an essential library for numerical computations in Python. Its array manipulation capabilities make it an efficient tool for scientific computing and data analysis.




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