Hands On Numpy For Numerical Analysis

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Hands On Numpy For Numerical Analysis

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Author : Rituraj Dixit
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-03-18

Hands On Numpy For Numerical Analysis written by Rituraj Dixit and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.

TAGLINE Unlock the Power of NumPy to Accelerate Data Analysis and Computing. KEY FEATURES ● Master NumPy concepts with hands-on examples and real-world use cases. ● Learn efficient numerical data analysis and performance optimization. ● Explore advanced NumPy functions for data science and ML workflows. DESCRIPTION NumPy is the backbone of numerical computing in Python, powering everything from scientific research to machine learning and AI applications. Mastering NumPy is essential for anyone working with data, enabling faster computations, efficient data structures, and seamless integration with advanced analytical tools. Hands-on NumPy for Numerical Analysis is a comprehensive guide that takes you from the fundamentals of NumPy to its advanced applications. Through hands-on examples and real-world scenarios, this book equips data scientists, analysts, and machine learning engineers with the practical skills needed to manipulate large datasets and optimize performance. Key topics include array operations, linear algebra, signal processing, and machine learning implementations, all covered with detailed explanations and step-by-step guidance. Whether you're building your foundation in numerical computing or looking to enhance your data analysis workflows, this book will give you a competitive edge. Don't get left behind—harness the full power of NumPy to supercharge your data science and machine learning projects today! WHAT WILL YOU LEARN ● Master NumPy array operations for high-performance numerical computing. ● Optimize data analysis workflows with efficient NumPy techniques. ● Perform advanced linear algebra and matrix operations using NumPy. ● Conduct statistical and exploratory data analysis with NumPy tools. ● Build end-to-end data processing pipelines with NumPy. ● Leverage NumPy for predictive modeling and machine learning tasks. WHO IS THIS BOOK FOR? This book is tailored for data scientists, analysts, engineers, and researchers looking to master NumPy for efficient numerical computing. A basic understanding of Python is recommended, but no prior expertise in numerical analysis is required. TABLE OF CONTENTS 1. Getting Started with NumPy 2. Understanding NumPy Array 3. Data Type (dtype) in NumPy Array 4. Indexing and Slicing in NumPy Array 5. NumPy Array Operations 6. NumPy Array I/O 7. Linear Algebra with NumPy 8. Advanced Numerical Computing 9. Exploratory Data Analysis 10. Performance Optimization 11. Implementing a Machine Learning Algorithm Index

Hands On Numpy For Numerical Analysis Unlock Numpy With Google Colab For High Performance Numerical Computing And Optimizing Numerical Data Analysis

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Author : Rituraj Dixit
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-03-18

Hands On Numpy For Numerical Analysis Unlock Numpy With Google Colab For High Performance Numerical Computing And Optimizing Numerical Data Analysis written by Rituraj Dixit and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-18 with Computers categories.

Unlock the Power of NumPy to Accelerate Data Analysis and Computing. Key Features● Master NumPy concepts with hands-on examples and real-world use cases.● Learn efficient numerical data analysis and performance optimization.● Explore advanced NumPy functions for data science and ML workflows. Book DescriptionNumPy is the backbone of numerical computing in Python, powering everything from scientific research to machine learning and AI applications. Mastering NumPy is essential for anyone working with data, enabling faster computations, efficient data structures, and seamless integration with advanced analytical tools. Hands-on NumPy for Numerical Analysis is a comprehensive guide that takes you from the fundamentals of NumPy to its advanced applications. Through hands-on examples and real-world scenarios, this book equips data scientists, analysts, and machine learning engineers with the practical skills needed to manipulate large datasets and optimize performance. Key topics include array operations, linear algebra, signal processing, and machine learning implementations, all covered with detailed explanations and step-by-step guidance. Whether you're building your foundation in numerical computing or looking to enhance your data analysis workflows, this book will give you a competitive edge. Don't get left behind—harness the full power of NumPy to supercharge your data science and machine learning projects today! What you will learn● Master NumPy array operations for high-performance numerical computing.● Optimize data analysis workflows with efficient NumPy techniques.● Perform advanced linear algebra and matrix operations using NumPy.● Conduct statistical and exploratory data analysis with NumPy tools.● Build end-to-end data processing pipelines with NumPy.● Leverage NumPy for predictive modeling and machine learning tasks.

Guide To Numpy

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Author : Travis Oliphant
language : en
Publisher: CreateSpace
Release Date : 2015-09-15

Guide To Numpy written by Travis Oliphant and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-15 with categories.

This is the second edition of Travis Oliphant's A Guide to NumPy originally published electronically in 2006. It is designed to be a reference that can be used by practitioners who are familiar with Python but want to learn more about NumPy and related tools. In this updated edition, new perspectives are shared as well as descriptions of new distributed processing tools in the ecosystem, and how Numba can be used to compile code using NumPy arrays. Travis Oliphant is the co-founder and CEO of Continuum Analytics. Continuum Analytics develops Anaconda, the leading modern open source analytics platform powered by Python. Travis, who is a passionate advocate of open source technology, has a Ph.D. from Mayo Clinic and B.S. and M.S. degrees in Mathematics and Electrical Engineering from Brigham Young University. Since 1997, he has worked extensively with Python for computational and data science. He was the primary creator of the NumPy package and founding contributor to the SciPy package. He was also a co-founder and past board member of NumFOCUS, a non-profit for reproducible and accessible science that supports the PyData stack. He also served on the board of the Python Software Foundation.

Learning Numpy Array

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Author : Ivan Idris
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-06-13

Learning Numpy Array written by Ivan Idris and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-13 with Computers categories.

A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy. This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.

Python For Data Analysis

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Author : Wes McKinney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-09-25

Python For Data Analysis written by Wes McKinney and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-25 with Computers categories.

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

Numerical Python

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Author : Robert Johansson
language : en
Publisher: Springer Nature
Release Date : 2024-09-27

Numerical Python written by Robert Johansson and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-27 with Computers categories.

Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers 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. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.

Python Data Science Handbook

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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21

Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Hands On Data Analysis With Numpy And Pandas

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Author : Curtis Miller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-06-29

Hands On Data Analysis With Numpy And Pandas written by Curtis Miller and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-29 with Computers categories.

Get to grips with the most popular Python packages that make data analysis possible Key Features Explore the tools you need to become a data analyst Discover practical examples to help you grasp data processing concepts Walk through hierarchical indexing and grouping for data analysis Book Description Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation. What you will learn Understand how to install and manage Anaconda Read, sort, and map data using NumPy and pandas Find out how to create and slice data arrays using NumPy Discover how to subset your DataFrames using pandas Handle missing data in a pandas DataFrame Explore hierarchical indexing and plotting with pandas Who this book is for Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

Python Programming And Numerical Methods

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Author : Qingkai Kong
language : en
Publisher: Academic Press
Release Date : 2020-12-02

Python Programming And Numerical Methods written by Qingkai Kong and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-02 with Technology & Engineering categories.

Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings.

Scipy And Numpy

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Author : Eli Bressert
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012

Scipy And Numpy written by Eli Bressert and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.

"Optimizing and boosting your Python programming"--Cover.