for many people (myself among them)the python language is easy to fall in love with since its first appearance in python has become one of the most popular dynamicprogramming languagesalong with perlrubyand others python and ruby have become especially popular in recent years for building websites using their numerous web frameworkslike rails (rubyand django (pythonsuch languages are often called scripting languages as they can be used to write quick-and-dirty small programsor scripts don' like the term "scripting languageas it carries connotation that they cannot be used for building mission-critical software among interpreted languages python is distinguished by its large and active scientific computing community adoption of python for scientific computing in both industry applications and academic research has increased significantly since the early for data analysis and interactiveexploratory computing and data visualizationpython will inevitably draw comparisons with the many other domain-specific open source and commercial programming languages and tools in wide usesuch as rmatlabsasstataand others in recent yearspython' improved library support (primarily pandashas made it strong alternative for data manipulation tasks combined with python' strength in general purpose programmingit is an excellent choice as single language for building data-centric applications python as glue part of python' success as scientific computing platform is the ease of integrating cc++and fortran code most modern computing environments share similar set of legacy fortran and libraries for doing linear algebraoptimizationintegrationfast fourier transformsand other such algorithms the same story has held true for many companies and national labs that have used python to glue together yearsworth of legacy software most programs consist of small portions of code where most of the time is spentwith large amounts of "glue codethat doesn' run often in many casesthe execution time of the glue code is insignificanteffort is most fruitfully invested in optimizing the computational bottleneckssometimes by moving the code to lower-level language like in the last few yearsthe cython project (preferred ways of both creating fast compiled extensions for python and also interfacing with and +code solving the "two-languageproblem in many organizationsit is common to researchprototypeand test new ideas using more domain-specific computing language like matlab or then later port those preliminaries www it-ebooks info