Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas , Second Edition

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Learn the first steps within Python to highly specialized concepts

Explore examples and code snippets taken from typical programming situations within scientific computing.

Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.

Description

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.

What you will learn

Understand the building blocks of computational mathematics, linear algebra, and related Python objects

Use Matplotlib to create high-quality figures and graphics to draw and visualize results

Apply object-oriented programming (OOP) to scientific computing in Python

Discover how to use pandas to enter the world of data processing

Handle exceptions for writing reliable and usable code

Cover manual and automatic aspects of testing for scientific programming

Get to grips with parallel computing to increase computation speed

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Total $ 163.97

Ashish Patel Oct 21, 2021

Overview:This book is incredible for any individual who want to begin learning python and develop their ability to executing the scientific algorithms with Python programming. This book assists you to composing everything distinctly for instance you would like to transform any mathematical formulation into Programming. This book follows step by step manner with best practices such as functional programming, Object oriented programming, Advance python technique. It also offers computational framework knowledge (SciPy, Pandas, Numpy) which are promptly utilized for data analysis, data science, developers.What I Like :Explain all the basics very clearly and in detail.Every example I have come across while reading this book is working perfectly with mathematical mapping.Described framework with metrics and higher-order tensor examples with step-by-step algebra operations that I like the most which are widely used in data science algorithms.Error handling is very core to any object-oriented programming and functional programming, with various error conditions and custom error handling.Mathematical variable declaration, its types and how to get them with functional programming.SciPy, Pandas, Matplotlib, NumPy, SymPy Usage All the complex examples explained in a simple and clear way that make this book unique to me.Testing code that works for the defined formula.Parallel computing module mpi4py is very new to me but in my research this framework uses distributed memory which increases the speed of programming.What I do not like:This is well written for the beginner and for Industry person. I only want to drive author attention toward the terminology mathematical terms. Terminology is bit difficult to understand for normal user.What I would like to see:I would like to see more advance mathematical modeling and it's conversion on the future edition of this book.I must recommend this book for every beginner, teacher, industry expert, data scientist, data analyst to buy because python related all stuff i enjoyed while reading this book.

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Uwe Jul 24, 2022

Fachlich Interessierte finden eine solide Beschreibung der Programmiersprache Python. Ein Beispiel: wer weiß, dass Pandas und Numpy die Standardabweichung unterschiedlich berechnen (im ersten Fall bezogen auf eine Stichprobe mit n-1 Freiheitsgraden, im zweiten Fall Division nur durch n) und kennt den steuernden Parameter "ddof"? Wie viele andere Bücher auch erläutert es die grundlegenden Bibliotheken Numpy, Pandas, Matplotlib oder auch Sympy. Es behandelt Iterationen und Generatoren, geht auf objektorientierte Programmierung ein oder erläutert die Interaktion mit Funktionen des Betriebssystem. Dank des mathematisch wissenschaftlichen Hintergrunds des Autors ergänzen seine Ausführungen die anderer fortschrittlicher Einführungsbücher.Das Buch beschränkt sich auf eine Einführung in "scientific computing" und erörtern nur wenige "applications". Die Übungsaufgaben sind teilweise sehr phantasievoll gestaltet, leider fehlen die Musterlösungen.Das Buch liegt in der zweiten Auflage vor. Gegenüber der Ausgabe von 2016 erhöhte sich die Seitenzahl bei gleichzeitig geringerem Preis.

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Richard Sep 17, 2021

Scientific Computing with Python is a great book for Python students aimed at an undergraduate level in university.The book covers the foundations of Python in the first few chapters which experienced programmers can easily skip over. But the heart of the content is indeed in 'scientific' programming focusing on NumPy, SciPy, and Pandas. If someone is planning to do research using Python then these three libraries are essential for getting the work done. Pandas is necessary for importing/exporting data and moving around easily in large datasets, NumPy has a lot of arithmetic and algebra operations making calculations easier, and SciPy is great for visualizing data.My favorite thing about the book is that the author uses Jupyter as the IDE which is a very easy and straightforward Python editor. The examples are easy to follow and practice and an experienced programmer can skip around the book to find code they can use in their work.I don't really have any complaints but I found that for a scientific book aimed at researchers and programmers the introductory chapters weren't really necessary but some might find them helpful.I wish I had this book when I started research in Bio Photonics as instead, I had to browse around dozens of websites to find the information that has been neatly arranged in this textbook.Overall, I would recommend this book to undergrad students in Computer Science, Mathematics, and STEM fields that could benefit from research involving Python and the Pandas, NumPy, and SciPy libraries.

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JF Delorme Jul 27, 2023

This is a great book for learning python. Even if the title is "Scientific Computing with Python", 90% of the book is about python programming, with lots of very valuable details about python and concise explanation. I recommend it to anyone.

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AnonySpy Jul 24, 2021

The book "Scientific Computing with Python" Second Edition by Claus Führer, Olivier Verdier, Jan Erik Solem, is perfect for both beginner-level and intermediate-level programmers, students, and python enthusiasts interested in the world of scientific computing and python. I can say that there are no pre-requisites for getting started. The book provides detailed, well-structured laid-out content making it easy for the reader to dive in with no hassle.The book provides a clear and simple introduction to the concepts in python programming like data types, variables, functions, classes, etc. This equips any reader with solid knowledge in programming by making them comfortable with python. The book also consists of necessary mathematical concepts like vectors, arrays, matrices, etc., providing the reader with a robust foundation to dive into the world of scientific computing.It provides the reader with knowledge of programming mathematical structures like arrays, vectors, etc., and doing mathematical operations on these structures in python using libraries like SciPy and NumPy. The reader is also introduced to the world of plotting using the Matplotlib library, which provides them with the knowledge of data visualization, interpretation of numbers, and data similar to the world of graphs in algebra.The authors then introduce the concepts of series and data frames using a library called Pandas, laying stepping stones to structured data, data science, and analysis. This enables the users with the knowledge of data concepts like rows, columns, filtering, aggregation, grouping, indices, etc. It also consists of input-output handling concepts providing the users with reading and writing virtual files in various formats.For any good programmer, testing is a crucial concept for understanding how their code behaves under different environments. The authors introduced the concepts of manual testing, automatic and measurement of execution time. In the end, the authors take a simplistic approach to narrate advanced concepts likes Symbolic Computations using SymPy, Operating System, Shell Commands, Parallel Computing, and other Comprehensive examples providing the readers with an overall hands-on introduction to the Scientific Computing Universe.I personally recommend this book to anyone at any level and get started by creating wonderful things with the power of python. For the students and teachers, this book would be a great resource as a primary textbook or as a reference book for any python course.

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Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas , Second Edition

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Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas , Second Edition

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Description

Who is this book for?

What you will learn

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