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Description
Scientists, engineers, and quantitative data analysts face many challenges nowadays. Data scientists want to be able to do numerical analysis of large datasets with minimal programming effort. They want to write readable, efficient, and fast code, that is as close as possible to the mathematical language package they are used to. A number of accepted solutions are available in the scientific computing world.The C, C++, and Fortran programming languages have their benefits, but they are not interactive and are considered too complex by many. The common commercial alternatives are, among others, Matlab, Maple, and Mathematica. These products provide powerful scripting languages, however, they are still more limited than any general purpose programming language. There are other open source tools similar to Matlab such as R, GNU Octave, and Scilab. Obviously, they also lack the power of a language such as Python.
Python is a popular general purpose programming language widely used by in the scientific community. You can access legacy C, Fortran, or R code easily from Python. It is objectoriented and considered more high-level than C or Fortran. Python allows you to write readable and clean code with minimal fuss. However, it lacks a Matlab equivalent out of the box. That's where NumPy comes in. This book is about NumPy and related Python libraries such as SciPy and Matplotlib.
Content:-
Preface
Chapter 1: NumPy Quick Start
Chapter 2: Beginning with NumPy Fundamentals
Chapter 3: Get into Terms with Commonly Used Functions
Chapter 4: Convenience Functions for Your Convenience
Chapter 5: Working with Matrices and ufuncs
Chapter 6: Move Further with NumPy Modules
Chapter 7: Peeking Into Special Routines
Chapter 8: Assure Quality with Testing
Chapter 9: Plotting with Matplotlib
Chapter 10: When NumPy is Not Enough: SciPy and Beyond
Pop Quiz Answers
Index
Author Details
Ivan Idris has a degree in Experimental Physics and several certifications (SCJP, SCWCD and other). His graduation thesis had a strong emphasis on Applied Computer Science. After graduating, Ivan worked for several companies as Java developer, Datawarehouse developer, and Test Analyst.
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