Chapter 1, Tools of the Trade, provides an overview of the tools available for data analysis in Python and details the packages and libraries that will be used in the book with some installation tips. A quick example highlights the common data structure used in the Pandas package.

Chapter 2, Exploring Data, introduces methods for initial exploration of data, including numeric summaries and distributions, and various ways of displaying data, such as histograms, Kernel Density Estimation (KDE) plots, and box plots.

Chapter 3, Learning About Models, covers the concept of models in data analysis and how using the cumulative distribution function and probability density function can help characterize a variable. Furthermore, it shows how to make point estimates and generate random numbers with a given distribution.

Chapter 4, Regression, introduces linear, multiple, and logistic regression with in-depth examples of using SciPy and statsmodels packages to test various hypotheses of relationships between variables.

Chapter 5, Clustering, explains some of the theory behind cluster finding analysis and goes through some more complex examples using the K-means and hierarchical clustering algorithms available in SciPy.

Chapter 6, Bayesian Methods, explains how to construct and test a model using Bayesian analysis in Python using the PyMC package. It covers setting up stochastic and deterministic variables with prior information, constructing the model, running the Markov Chain Monte Carlo (MCMC) sampler, and interpreting the results. In addition, a short bonus section covers how to plot coordinates on maps using both the basemap and cartopy packages, which are important for presenting and analyzing data with geographical coordinate information.

Chapter 7, Supervised and Unsupervised Learning, looks at linear regression, clustering, and classification with two machine learning analysis techniques available in the Scikit-learn package.

Chapter 8, Time Series Analysis, examines various aspects of time series modeling using Pandas and statsmodels. Initially, the important concepts of smoothing, resampling, rolling estimates, and stationarity are covered. Later, autoregressive (AR), moving average (MA), and combined ARIMA models are explained and applied to one of the data sets, including making shorter forecasts using the constructed models.

Appendix, More on Jupyter Notebook and matplotlib Styles, shows some convenient extensions of Jupyter Notebook and some useful keyboard shortcuts to make the Jupyter workflow more efficient. The matplotlib style files are explained and how to customize plots even further to make beautiful figures ready for inclusion in reports. Lastly, various useful online resources are listed and described.

All you need to follow through the examples in this book is a computer running any recent version of Python. While the examples use Python 3, they can easily be adapted to work with Python 2, with only minor changes. The packages used in the examples are NumPy, SciPy, matplotlib, Pandas, statsmodels, PyMC, Scikit-learn. Optionally, the packages basemap and cartopy are used to plot coordinate points on maps. The easiest way to obtain and maintain a Python environment that meets all the requirements of this book is to download a prepackaged Python distribution. In this book, we have checked all the code against Continuum Analytics' Anaconda Python distribution and Ubuntu Xenial Xerus (16.04) running Python 3.

To download the example data and code, an Internet connection is needed.

This book is intended for professionals with a beginner to intermediate level of Python programming knowledge who want to move in the direction of solving more sophisticated problems and gain deeper insights through advanced data analysis. Some experience with the math behind basic statistics is assumed, but quick introductions are given where required. If you want to learn the breadth of statistical analysis techniques in Python and get an overview of the methods and tools available, you will find this book helpful. Each chapter consists of a number of examples using mostly real-world data to highlight various aspects of the topic and teach how to conduct data analysis from start to finish.

In this book, you will find a number of text styles 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: "This code has the effect of selecting matplotlib stylesheet mystyle.mplstyle."

A block of code is set as follows:

gss_data = pd.read_stata('data/GSS2012merged_R5.dta',
                         convert_categoricals=False)
gss_data.head()

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

    python -c 'import numpy'

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Here, you can check the box for add a toolbar button to open the shortcuts dialog/panel."

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