Scientific Computing with Python - Second Edition: High-performance scientific computing with NumPy, SciPy, and pandas Paperback – Import, 23 July 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.

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.

A broad and easy to understand introduction to scientific computing in python.

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.

This book is very good for teaching the basics of Python scientific programming using lots of examples. However, it is poor as a reference if you want to look up a command or how to do something. The Index is minimal and pretty useless. It would have been much more useful to me if it just had a good Index and maybe an Appendix with the basic commands for quick reference.

Highly recommended for anyone who's interested in building their intuition for problem solving in parallel to their Python skills. The early chapters might be a bit too "light" for more experienced practitioners, but would also be a quick read / refresh. I'm looking forward to completing this book, and might be back to add the 5th star.