Mastering Python Scientific Computing

**A complete guide for Python programmers to master scientific computing using

Python APIs and tools**

About This Book The basics of scientific computing to advanced concepts

involving parallel and large scale computation are all covered. Most of the

Python APIs and tools used in scientific computing are discussed in detail The

concepts are discussed with suitable example programs Who This Book Is For

If you are a Python programmer and want to get your hands on scientific

computing, this book is for you. The book expects you to have had exposure to

various concepts of Python programming.

What You Will Learn Fundamentals and components of scientific computing

Scientific computing data management Performing numerical computing using

NumPy and SciPy Concepts and programming for symbolic computing using SymPy

Using the plotting library matplotlib for data visualization Data analysis and

visualization using Pandas, matplotlib, and IPython Performing parallel and

high performance computing Real-life case studies and best practices of

scientific computing In Detail

In today's world, along with theoretical and experimental work, scientific

computing has become an important part of scientific disciplines. Numerical

calculations, simulations and computer modeling in this day and age form the

vast majority of both experimental and theoretical papers. In the scientific

method, replication and reproducibility are two important contributing

factors. A complete and concrete scientific result should be reproducible and

replicable. Python is suitable for scientific computing. A large community of

users, plenty of help and documentation, a large collection of scientific

libraries and environments, great performance, and good support makes Python a

great choice for scientific computing.

At present Python is among the top choices for developing scientific workflow

and the book targets existing Python developers to master this domain using

Python. The main things to learn in the book are the concept of scientific

workflow, managing scientific workflow data and performing computation on this

data using Python.

The book discusses NumPy, SciPy, SymPy, matplotlib, Pandas and IPython with

several example programs.

Style and approach

This book follows a hands-on approach to explain the complex concepts related

to scientific computing. It details various APIs using appropriate examples.