Chapter 1, Getting Started with NumPy, will guide you through the steps needed to install NumPy on your system and helps you create a basic NumPy application. We also successfully run a vector addition program.

Chapter 2, NumPy Basics, introduces you to NumPy arrays and fundamentals. In this chapter, we also learn that NumPy arrays can be sliced and indexed in an efficient manner. Here, we will understand the manipulation of shapes of various arrays.

Chapter 3, Basic Data Analysis with NumPy, tells us about learning data analysis with weather data analysis as an example. We will also explore the data from a KNMI weather station.

Chapter 4, Simple Predictive Analytics with NumPy, helps us attempt to predict the weather with simple models, such as Autoregressive Model with Lag 1 and Autoregressive Model with Lag 2.

Chapter 5, Signal Processing Techniques, gives us examples of signal processing and time series analysis. We look at smoothing with window functions and moving averages. We also touch upon the sifting process used by scientists to derive sunspot cycles. And we also get a demonstration of cointegration.

Chapter 6, Profiling, Debugging, and Testing, is about profiling, debugging, and testing, which are essential phases in the development cycle. We also cover unit testing, assert functions, and floating-point precision in depth.

Chapter 7, The Scientific Python Ecosystem, gives an overview of the Python ecosystem in which NumPy takes a central role. We also examine Cython, which is a relatively young programming language based on Python. We also have a look at Clustering, a type of machine learning algorithm.

To try out the code samples in this book, you will need a recent build of NumPy. This means that you will need to have one of the Python versions supported by NumPy as well. Some code samples make use of the Matplotlib for illustration purposes. Matplotlib is not strictly required to follow the examples, but it is recommended that you install it too.

Here is a list of software used to develop and test the code examples:

Needless to say, you don't need to have exactly this software and these versions on your computer. Python and NumPy is the absolute minimum you will need.

This book is for a scientist, engineer, programmer or an analyst looking for a high quality open source mathematical library. Knowledge of Python is assumed. Also, some affinity or at least interest in mathematics and statistics is required.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text are shown as follows: "In this example, we chose numpy-1.8.0-win32-superpack-python2.7.exe."

A block of code is set as follows:

start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "PythonSum elapsed time in microseconds", delta.microseconds

Any command-line input or output is written as follows:

$ python vectorsum.py 4000

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Click on the Continue button."

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