"I'd rather do math in a general-purpose language than try to do general-purpose programming in a math language." | ||
| -- John D Cook | ||
Python has become one of the most popular programming languages in scientific computing over the last decade. The reasons for its success are numerous, and these will gradually become apparent as you proceed with this book. Unlike many other mathematical languages, such as MATLAB, R and Mathematica, Python is a general-purpose programming language. As such, it provides a suitable framework to build scientific applications and extend them further into any commercial or academic domain. For example, consider a (somewhat) simple application that requires you to write a piece of software and predicts the popularity of a blog post. Usually, these would be the steps that you'd take to do this:
- Generating a corpus of blog posts and their corresponding ratings (assuming that the ratings here are suitably quantifiable).
- Formulating a model that generates ratings based on content and other data associated with the blog post.
- Training a model on the basis of the data you found in step 1. Keep doing this until you are confident of the reliability of the model.
- Deploying the model as a web service.
Normally, as you move through these steps, you will find yourself jumping between different software stacks. Step 1 requires a lot of web scraping. Web scraping is a very common problem, and there are tools in almost every programming language to scrape the Web (if you are already using Python, you would probably choose Beautiful Soup or Scrapy). Steps 2 and 3 involve solving a machine learning problem and require the use of sophisticated mathematical languages or frameworks, such as Weka or MATLAB, which are only a few of the vast variety of tools that provide machine learning functionality. Similarly, step 4 can be implemented in many ways using many different tools. There isn't one right answer. Since this is a problem that has been amply studied and solved (to a reasonable extent) by a lot of scientists and software developers, getting a working solution would not be difficult. However, there are issues, such as stability and scalability, that might severely restrict your choice of programming languages, web frameworks, or machine learning algorithms in each step of the problem. This is where Python wins over most other programming languages. All the preceding steps (and more) can be accomplished with only Python and a few third-party Python libraries. This flexibility and ease of developing software in Python is precisely what makes it a comfortable host for a scientific computing ecosystem. A very interesting interpretation of Python's prowess as a mature application development language can be found in Python Data Analysis, Ivan Idris, Packt Publishing. Precisely, Python is a language that is used for rapid prototyping, and it is also used to build production-quality software because of the vast scientific ecosystem it has acquired over time. The cornerstone of this ecosystem is NumPy.
Numerical Python (NumPy) is a successor to the Numeric package. It was originally written by Travis Oliphant to be the foundation of a scientific computing environment in Python. It branched off from the much wider SciPy module in early 2005 and had its first stable release in mid-2006. Since then, it has enjoyed growing popularity among Pythonists who work in the mathematics, science, and engineering fields. The goal of this book is to make you conversant enough with NumPy so that you're able to use it and can build complex scientific applications with it.
Let's begin by taking a brief tour of the Scientific Python (SciPy) stack.
Note
Note that SciPy can mean a number of things: the Python module named scipy (http://www.scipy.org/scipylib), the entire SciPy stack (http://www.scipy.org/about.html), or any of the three conferences on scientific Python that take place all over the world.
Fernando Perez, the primary author of IPython, said in his keynote at PyCon, Canada 2012:
"Computing in science has evolved not only because software has evolved, but also because we, as scientists, are doing much more than just floating point arithmetic."
This is precisely why the SciPy stack boasts such rich functionality. The evolution of most of the SciPy stack is motivated by teams of scientists and engineers trying to solve scientific and engineering problems in a general-purpose programming language. A one-line explanation of why NumPy matters so much is that it provides the core multidimensional array object that is necessary for most tasks in scientific computing. This is why it is at the root of the SciPy stack. NumPy provides an easy way to interface with legacy Fortran and C/C++ numerical code using time-tested scientific libraries, which we know have been working well for decades. Companies and labs across the world use Python to glue together legacy code that has been around for a long time. In short, this means that NumPy allows us to stand on the shoulders of giants; we do not have to reinvent the wheel. It is a dependency for every other SciPy package. The NumPy ndarray object, which is the subject of the next chapter, is essentially a Pythonic interface to data structures used by libraries written in Fortran, C, and, C++. In fact, the internal memory layouts used by NumPy ndarray objects implement C and Fortran layouts. This will be addressed in detail in upcoming chapters.
The next layer in the stack consists of SciPy, matplotlib, IPython (the interactive shell of Python; we will use it for the examples throughout the book, and details of its installation and usage will be provided in later sections), and SymPy modules. SciPy provides the bulk of the scientific and numerical functionality that a major part of the ecosystem relies on. Matplotlib is the de facto plotting and data visualization library in Python. IPython is an increasingly popular interactive environment for scientific computing in Python. In fact, the project has had such active development and enjoyed such popularity that it is no longer limited to Python and extends its features to other scientific languages, particularly R and Julia. This layer in the stack can be thought of as a bridge between the core array-oriented functionality of NumPy and the domain-specific abstractions provided by the higher layers of the stack. These domain-specific tools are commonly called SciKits-popular ones among them are scikit-image (image processing), scikit-learn (machine learning), statsmodels (statistics), pandas (advanced data analysis), and so on. Listing every scientific package in Python would be nearly impossible since the scientific Python community is very active, and there is always a lot of development happening for a large number of scientific problems. The best way to keep track of projects is to get involved in the community. It is immensely useful to join mailing lists, contribute to code, use the software for your daily computational needs, and report bugs. One of the goals of this book is to get you interested enough to actively involve yourself in the scientific Python community.