Chapter 1, Introducing Data Analysis and Libraries, describes the typical steps involved in a data analysis task. In addition, a couple of existing data analysis software packages are described.

Chapter 2, NumPy Arrays and Vectorized Computation, dives right into the core of the PyData ecosystem by introducing the NumPy package for high-performance computing. The basic data structure is a typed multidimensional array which supports various functions, among them typical linear algebra tasks. The data structure and functions are explained along with examples.

Chapter 3, Data Analysis with Pandas, introduces a prominent and popular data analysis library for Python called Pandas. It is built on NumPy, but makes a lot of real-world tasks simpler. Pandas comes with its own core data structures, which are explained in detail.

Chapter 4, Data Visualizaiton, focuses on another important aspect of data analysis: the understanding of data through graphical representations. The Matplotlib library is introduced in this chapter. It is one of the most popular 2D plotting libraries for Python and it is well integrated with Pandas as well.

Chapter 5, Time Series, shows how to work with time-oriented data in Pandas. Date and time handling can quickly become a difficult, error-prone task when implemented from scratch. We show how Pandas can be of great help there, by looking in detail at some of the functions for date parsing and date sequence generation.

Chapter 6, Interacting with Databases, deals with some typical scenarios. Your data does not live in vacuum, and it might not always be available as CSV files either. MongoDB is a NoSQL database and Redis is a data structure server, although many people think of it as a key value store first. Both storage systems are introduced to help you interact with data from real-world systems.

Chapter 7, Data Analysis Application Examples, applies many of the things covered in the previous chapters to deepen your understanding of typical data analysis workflows. How do you clean, inspect, reshape, merge, or group data – these are the concerns in this chapter. The library of choice in the chapter will be Pandas again.

Chapter 8, Machine Learning Models with scikit-learn, would like to make you familiar with a popular machine learning package for Python. While it supports dozens of models, we only look at four models, two supervised and two unsupervised. Even if this is not mentioned explicitly, this chapter brings together a lot of the existing tools. Pandas is often used for machine learning data preparation and matplotlib is used to create plots to facilitate understanding.

There are not too many requirements to get started. You will need a Python programming environment installed on your system. Under Linux and Mac OS X, Python is usually installed by default. Installation on Windows is supported by an excellent installer provided and maintained by the community.

This book uses a recent Python 2, but many examples will work with Python 3 as well.

The versions of the libraries used in this book are the following: NumPy 1.9.2, Pandas 0.16.2, matplotlib 1.4.3, tables 3.2.2, pymongo 3.0.3, redis 2.10.3, and scikit-learn 0.16.1. As these packages are all hosted on PyPI, the Python package index, they can be easily installed with pip. To install NumPy, you would write:

$ pip install numpy

If you are not using them already, we suggest you take a look at virtual environments for managing isolating Python environment on your computer. For Python 2, there are two packages of interest there: virtualenv and virtualenvwrapper. Since Python 3.3, there is a tool in the standard library called pyvenv (https://docs.python.org/3/library/venv.html), which serves the same purpose.

Most libraries will have an attribute for the version, so if you already have a library installed, you can quickly check its version:

>>> import redis
>>> redis.__version__
'2.10.3'

This works well for most libraries. A few, such as pymongo, use a different attribute (pymongo uses just version, without the underscores).

While all the examples can be run interactively in a Python shell, we recommend using IPython. IPython started as a more versatile Python shell, but has since evolved into a powerful tool for exploration and sharing. We used IPython 4.0.0 with Python 2.7.10. IPython is a great way to work interactively with Python, be it in the terminal or in the browser.

We assume you have been exposed to programming and Python and you want to broaden your horizons and get to know some key libraries in the data analysis field. We think that people with different backgrounds can benefit from this book. If you work in business, finance, in research and development at a lab or university, or if your work contains any data processing or analysis steps and you want know what Python has to offer, then this book can be of help. If you want to get started with basic data processing tasks or time series, then you can find lot of hands-on knowledge in the examples of this book. The strength of this book is its breadth. While we cannot dive very deep into a single package – although we will use Pandas extensively - we hope that we can convey a bigger picture: how the different parts of the Python data ecosystem work and can work together to form one of the most innovative and engaging programming environments.

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: "We can include other contexts through the use of the include directive."

A block of code is set as follows:

>>> import numpy as np
>>> np.random.randn()

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

>>> import pandas as pd

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

$ cat "data analysis" | wc -l

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: "clicking the Next button moves you to the next screen".

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