NUMPY

NumPy is a Python library used for working with arrays.

It also has functions for working in domain of linear algebra, fourier transform, and matrices.

NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.

NumPy stands for Numerical Python NumPy is a Python library used for working with arrays.

It also has functions for working in domain of linear algebra, fourier transform, and matrices.

NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.

NumPy is a Python library used for working with arrays.

It also has functions for working in domain of linear algebra, fourier transform, and matrices.

NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely.

It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these elements efficiently. 1. Arrays: At the core of NumPy is the ndarray (n-dimensional array), a flexible and efficient data structure for representing arrays. These arrays can be one-dimensional, two-dimensional, or multi-dimensional, providing a versatile tool for handling numerical data. NumPy’s arrays are more memory-efficient than Python lists and offer a variety of methods for array manipulation. import numpy as np

# Creating a NumPy array arr = np.array([1, 2, 3, 4, 5])

2. Mathematical Operations: NumPy excels in performing element-wise operations on arrays. This means that operations are applied to each element of the array, allowing for concise and efficient code. # Element-wise addition result = arr + 2 # Result: [3, 4, 5, 6, 7]

# Element-wise multiplication result = arr * 3 # Result: [3, 6, 9, 12, 15]

NumPy also supports broadcasting, a powerful feature that enables operations between arrays of different shapes and sizes

# Broadcasting example matrix = np.array([[1, 2, 3], [4, 5, 6]]) result = matrix + np.array([10, 20, 30]) # Result: # [[11, 22, 33], # [14, 25, 36]]

3. Indexing and Slicing: Accessing and manipulating data within NumPy arrays is intuitive using indexing and slicing operations. Elements can be accessed by their position in the array, and slices can be extracted for more complex manipulations. # Indexing element = arr[2] # Result: 3

# Slicing subset = arr[1:4] # Result: [2, 3, 4]

4. Linear Algebra: NumPy provides a robust set of functions for linear algebra operations. This includes matrix multiplication, eigenvalue decomposition, singular value decomposition, and solving linear equations. These functionalities are crucial for scientific and engineering applications. # Matrix multiplication matrix_a = np.array([[1, 2], [3, 4]]) matrix_b = np.array([[5, 6], [7, 8]]) result = np.dot(matrix_a, matrix_b) # Result: # [[19, 22], # [43, 50]] 5.Random: The numpy.random module offers tools for generating random data. This is essential for various applications, such as simulations, statistical analysis, and machine learning. # Generating random numbers random_numbers = np.random.rand(3, 3) # Result: 3x3 array of random numbers between 0 and 1 6. Statistical Functions: NumPy includes a variety of statistical functions for analyzing data. These functions make it easy to compute measures like mean, median, variance, standard deviation, and more. # Computing mean and standard deviation data = np.array([1, 2, 3, 4, 5]) mean_value = np.mean(data) std_deviation = np.std(data) 7. Integration with Python: NumPy seamlessly integrates with Python and other scientific computing libraries. Its array objects can be used as input for a wide range of functions in libraries like SciPy, Pandas, and scikit-learn, fostering a cohesive ecosystem for scientific computing in Python.

NumPy is a powerful and versatile library that forms the foundation of numerical computing in Python. Its efficient array operations, linear algebra capabilities, and integration with other libraries make it an indispensable tool for scientists, engineers, and data scientists. Whether you’re working with large datasets, implementing algorithms, or conducting simulations, NumPy provides the building blocks for efficient and high-performance computation in the Python programming language.