This book starts as a tutorial on NumPy, SciPy, matplotlib, and pandas. These are open source Python packages useful for numerical work, data wrangling, and visualization. Combined, they can compete with MATLAB, Mathematica, and R. The second half of the book teaches more advanced topics such as signal processing, databases, text analysis, machine learning, interoperability, and performance tuning.

Chapter 1, Getting Started with Python Libraries, guides us to achieve a successful installation of the numerical Python software and set it up step by step. Also, we will create a small application.

Chapter 2, NumPy Arrays, introduces us to NumPy fundamentals and arrays. By the end of this chapter, we will have basic understanding of NumPy arrays and the associated functions.

Chapter 3, Statistics and Linear Algebra, gives a quick overview of linear algebra and statistical functions.

Chapter 4, pandas Primer, provides a tutorial on basic pandas functionality where we learn about pandas data structures and operations.

Chapter 5, Retrieving, Processing, and Storing Data, explains how to acquire data in various formats and how to clean raw data and store it.

Chapter 6, Data Visualization, teaches how to plot data with matplotlib.

Chapter 7, Signal Processing and Time Series, contains time series and signal processing examples using sunspot cycles data. The examples mostly use NumPy/SciPy, along with statsmodels in at least one example.

Chapter 8, Working with Databases, provides information about various databases (relational and NoSQL) and related APIs.

Chapter 9, Analyzing Textual Data and Social Media, analyzes texts for sentiment analysis and topics extraction. A small example is also given of network analysis.

Chapter 10, Predictive Analytics and Machine Learning, explains artificial intelligence with weather prediction as a running example and mostly uses scikit-learn. However, some machine learning algorithms are not covered by scikit-learn, so for those, we use other APIs.

Chapter 11, Environments Outside the Python Ecosystem and Cloud Computing, gives various examples on how to integrate existing code not written in Python. Also, setup in the Cloud will be demonstrated.

Chapter 12, Performance Tuning, Profiling, and Concurrency, gives hints on improving performance with profiling and Cythoning as key techniques. For multicore, distributed systems, we discuss the relevant frameworks too.

Appendix A, Key Concepts, serves as a glossary containing short descriptions of key concepts found throughout the book.

Appendix B, Useful Functions, gives an overview of functions used in the book.

Appendix C, Online Resources, lists links to documentation, forums, articles, and other important information.

The code examples in this book should work on most modern operating systems. For all chapters, Python 2 and pip is required. To install Python, go to https://wiki.python.org/moin/BeginnersGuide/Download. To install pip, go to http://pip.readthedocs.org/en/latest/installing.html. Instructions to install software are given throughout the chapters. Most of the time, we need to run the following command with admin privileges:

    $ pip install <some software>

The following is a list of software used for the examples and versions used for testing purposes:

Of course, it's not necessary for you to have the same version of the software. Usually, the latest version available should work.

To uninstall Python packages installed with pip, use the following command:

   $ pip uninstall <some software>

This book is for people with basic knowledge of Python and Mathematics who want to learn how to use Python software to analyze data. We try to keep things simple, but it's not possible to cover all the topics in great detail. It may be useful for you to refresh your knowledge of Mathematics via Khan Academy, Coursera, or Wikipedia.

I would recommend the following books by Packt Publishing for further reading:

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, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Notice that numpysum() does not need a for loop."

A block of code is set as follows:

def pythonsum(n):
   a = range(n)
   b = range(n)
   c = []

   for i in range(len(a)):
       a[i] = i ** 2
       b[i] = i ** 3
       c.append(a[i] + b[i])

   return c

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

$ yum install python-numpy

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 Next button."

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To send us general feedback, simply send an e-mail to <[email protected]>, and mention the book title via the subject of your message.

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