Who this book is for
This book is the outcome of a course on Python for scientific computing which is taught at Lund University since 2008. The course expanded over the years, and condensed versions of the material were taught at universities in Cologne, Trondheim, Stavanger, Soran, Lappeenranta and also in computation oriented companies.
Our belief is that Python and its surrounding scientific computing ecosystem — SciPy, NumPY and matplotlib — represent a tremendous progress in scientific computing environment. Python and the aforementioned libraries are free and open source. What’s more, is a modern language featuring all the bells and whistles that this adjective entails: object oriented programming, testing, advanced shell with IPython, etc. When writing this book we had two groups of readers in mind:
- The reader who chooses Python as his or her first programming language will use this book in a teacher-led course. The book guides into the different topics and offers background reading and experimenting. A teacher typically selects and orders the material from this book in such a way, that it fits to the specific learning outcomes of an introductory course.
- The reader who already has some experience in programming, and some taste for scientific computing or mathematics will use this book as a companion when diving into the world of Scipy and Numpy. Programming in Python can be quite different from programming in MATLAB, say. The book wants to point out the "pythonic" way of programming, which makes programming a pleasure.
Our goal is to explain the steps to get started with Python in the context of scientific computing. The book may be read either from the first page to the last, or by picking the bits that seem most interesting. Needless to say, as improving one’s programming skills requires considerable practice, it is highly advisable to experiment and play with the examples and the exercises in the book.
We hope that the readers will enjoy programming with Python, SciPy, NumPY and matplotlib as much as we do.
Python vs Other Languages
When it comes to deciding what language to use for a book on scientific computing many factors come in to play. The learning threshold of the language itself is important for newcomers, here scripting languages usually provide the best options. A wide range of modules for numerical computing is necessary, preferably with a strong developer community. If these core modules are built on a well-tested, optimized foundation of fast libraries like e.g. LAPACK, even better. Finally, if the language is also usable in a wider setting and a wider range of applications, the chance of the reader using the skills learned from this book outside an academic setting is greater. Therefore the choice of Python was a natural one.
In short, Python is
- free and open source
- a scripting language, meaning that it is interpreted
- a modern language (object oriented, exception handling, dynamic typing etc.)
- concise, easy to read and quick to learn
- full of freely available libraries, in particular scientific ones (linear algebra, visualization tools, plotting, image analysis, differential equations solving, symbolic computations, statistics etc.)
- useful in a wider setting: scientific computing, scripting, web sites, text parsing, etc.
- widely used in industrial applications
There are other alternatives to Python. Some of them and the differences to Python are listed here.
Java, C++ : Object oriented, compiled languages. More verbose and low level compared to Python. Few scientific libraries.
C, FORTRAN : Low level compiled languages. Both languages are extensively used in scientific computing, where computational time matters. Nowadays these languages are often combined with Python wrappers.
PHP, Ruby, other interpreted languages. PHP is web oriented. Ruby is as flexible as Python but has few scientific libraries.
MATLAB, Scilab, Octave : MATLAB is a tool for matrix computation that evolved for scientific computing. The scientific library is huge. The language features are not as developed as those of Python. Neither free nor open source. SciLab and Octave are open source tools which are syntactically similar to MATLAB.
Haskell : Haskell is a modern functional language and follows different programming paradigms than Python. There are some common constructions like list comprehension. Haskell is rarely used in scientific computing. See also [12].
