Numerical Python - NumPy Part 1 | Python Programming

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Published on August 9, 2023 12:48:36 PM ● Video Link: https://www.youtube.com/watch?v=AG5q5vNiXi4

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#numericals #python #pythonprogramming
✳️ About the video
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In this video we will Learn About what is NumPy, why use NumPy, why is NumPy faster then lists, Which Language is Numpy written in, Numpy- Ndarray Object

What is NumPy? NumPy, which stands for Numerical Python, is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions to operate on these arrays.

Why Use NumPy?
Efficiency: NumPy operations are implemented in C, making array computations significantly faster than standard Python lists.
Functionality: NumPy provides a wide range of mathematical, logical, shape manipulation, sorting, selecting, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation, and more.

Memory Efficiency: NumPy arrays are more memory efficient than lists.
Interoperability: NumPy can integrate with a wide variety of databases and can interface with other languages like C and Fortran.

Why is NumPy Faster Than Lists?
Fixed Type: Unlike Python lists, NumPy arrays have a fixed type for their elements, which allows them to be stored more compactly and accessed more quickly.

Contiguous Memory: NumPy arrays are stored in contiguous blocks of memory, which is beneficial for cache and memory hierarchy, resulting in faster access.

Optimized Functions: The core functions of NumPy are implemented in C, making use of optimized libraries like BLAS and LAPACK, which are known for their performance in mathematical computations.

Which Language is NumPy Written In? NumPy is primarily written in C, with some parts in Fortran, making its operations faster and more efficient.

NumPy - ndarray Object The central feature of NumPy is the ndarray (n-dimensional array) object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. Each ndarray has attributes like shape (tuple indicating the size of each dimension), dtype (data type of the array), and more. They are more efficient and faster than Python's built-in lists for numerical operations due to the reasons mentioned above.

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