We, NumPy users, live in exciting times. New NumPy-related developments seem to come to our attention every week or maybe even daily. When this book was being written, NumPy Foundation of Open Code for Usable Science was created. The Numba project—NumPy-aware, dynamic Python compiler using LLVM—was announced. Also, Google added support to their Cloud product Google App Engine.
In the future, we can expect improved concurrency support for clusters of GPUs and CPUs. OLAP-like queries will be possible with NumPy arrays.
This is wonderful news, but we have to keep reminding ourselves that NumPy is not alone in the scientific (Python) software ecosystem. There is Scipy, Matplotlib (a very useful Python plotting library), IPython (an interactive shell), and Scikits. Outside of the Python ecosystem, languages such as R, C, and Fortran are pretty popular. We will go into the details of exchanging data with these environments.
Chapter 1, Winding Along with IPhython, covers IPython that is a toolkit, mostly known for its shell. The web-based notebook is a new and exciting feature, which we will cover in detail. Think of Matlab and Mathematica, but in your browser, that is open source and free.
Chapter 2, Advanced Indexing and Array Concepts, describes some of NumPy's more advanced and tricky indexing techniques. NumPy has very efficient arrays that are easy to use due to their powerful indexing mechanism.
Chapter 3, Get to Grips with Commonly Used Functions, makes an attempt to document the most essential functions that every NumPy user should know. NumPy has many functions, too many to even mention in this book.
Chapter 4, Connecting NumPy with the Rest of the World, shows us that the number of programming languages, libraries, and tools that one encounters in the real world is mind-boggling. Some of the software runs on the Cloud, and some of it lives on your local machine or a remote server. Being able to fit and connect NumPy in such an environment is just as important as being able to write standalone NumPy code.
Chapter 5, Audio and Image Processing, shows you a different view of NumPy. So when you think of NumPy after reading this chapter, you'll probably think of sounds or images too.
Chapter 6, Special Arrays and Universal Functions, covers technical topics, such as special arrays and universal functions. It will help us learn how to perform string operations, ignore illegal values, and store heterogeneous data.
Chapter 7, Profiling and Debugging, will demonstrate several convenient profiling and debugging tools necessary to produce a great software application.
Chapter 8, Quality Assurance, will discuss common methods and techniques such as unit testing, mocking, and BDD, including the NumPy testing utilities, as quality assurance deserves a lot of attention.
Chapter 9, Speed Up Code with Cython, shows how Cython works from the NumPy perspective. Cython tries to combine the speed of C and the strengths of Python.
Chapter 10, Fun with Scikits, gives us a quick tour through some of the most useful Scikits projects. Scikits are a yet another part of the fascinating, scientific Python ecosystem.