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    Numpy Simply In Depth
    Numpy Simply In Depth
    Numpy Simply In Depth
    Ebook209 pages1 hour

    Numpy Simply In Depth

    By Ajit Singh

    5/5

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    About this ebook

    This book covers Python mathematical library NumPy in detail. NumPy (short for Numerical Python) provides an efficient interface to store and operate on dense data buffers. In some ways, NumPy arrays are like Python's built-in list type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size. NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.
    LanguageEnglish
    PublisherLulu.com
    Release dateApr 7, 2020
    ISBN9781716007316
    Numpy Simply In Depth
    Author

    Ajit Singh

    Ajit Singh is equally interested in fiction and non-fiction and has written many books in English, Hindi, and Urdu. He has performed in Haryana, published his prose and verse in India and Pakistan, and participated in an international online poetry symposium organized by Bazm-e-Urdu, Qatar.He lives in a village, teaches science, and comes from a farming family. His father served as a major in the Parachute Regiment of the Indian Army.Ajit plays cricket, football, volleyball, basketball, badminton, and chess. He loves harmonium and flute, sings folk songs, and also enjoys gardening in his spare time. His nickname is "Badal," which means "cloud" in English.

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    Book preview

    Numpy Simply In Depth - Ajit Singh

    Copyrighted Material

    TinyOS An Embedded Operating System

    Copyright © 2020-21 by Ajit Singh, All Rights Reserved.

    No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means — electronic, mechanical, photocopying, recording or otherwise — without prior written permission from the author, except for the inclusion of brief quotations in a review.

    For information about this title or to order other books and/or electronic media, contact the publisher:

    Ajit Singh

    e: ajit_singh24@yahoo.com

    w: https://www.ajitvoice.wordpress.com

    Ravi Kumar Singh

    e: ravsing060@gmail.com

    Preface

    This book covers Python mathematical library NumPy in detail. NumPy (short for Numerical Python) provides an efficient interface to store and operate on dense data buffers. In some ways, NumPy arrays are like Python's built-in list type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size. NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.

    You will learn all the essential things needed to become a confident NumPy user. NumPy started originally as part of SciPy and then was singled out as a fundamental library, which other open source Python APIs build on. As such, it is a crucial part of the common Python stack used for numerical and data analysis.

    Anyone with basic (and upward) knowledge of Python is the targeted audience for this book. Although the tools in  NumPy are relatively advanced, using them is simple and should keep even a novice Python programmer happy.

    Features;

    Work with vectors and matrices using NumPy

    Plot and visualize data with Matplotlib

    Perform data analysis tasks with Pandas and SciPy

    Review statistical modeling and machine learning with statsmodels and scikit-learn

    Optimize Python code using Numba and Cython

    After reading this book, you will be familiar with many computing techniques including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning.

    Chapter 1

    NumPy - Introduction

    NumPy is a Python package. It stands for 'Numerical Python'. It is a library consisting of multidimensional array objects and a collection of routines for processing of array.

    Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package Numarray was also developed, having some additional functionalities. In 2005, Travis Oliphant created NumPy package by incorporating the features of Numarray into Numeric package. There are many contributors to this open source project.

    NumPy (short for Numerical Python) provides an efficient interface to store and operate on dense data buffers. In some ways, NumPy arrays are like Python's built-in list type, but NumPy arrays provide much more efficient storage and data operations as the arrays grow larger in size. NumPy arrays form the core of nearly the entire ecosystem of data science tools in Python, so time spent learning to use NumPy effectively will be valuable no matter what aspect of data science interests you.

    Numpy is all about vectorization. If you are familiar with Python, this is the main difficulty you'll face because you'll need to change your way of thinking and your new friends (among others) are named vectors, arrays, views or ufuncs.

    NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

    At the core of the NumPy package, is the ndarray  object. This encapsulates n-dimensional arrays of homogeneous data types, with many operations being performed in compiled code for performance. There are several important differences between NumPy arrays and the standard Python sequences:

    NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original.

    The elements in a

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