What this book covers
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.
