Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning.

Python Data Analysis Cookbook is a follow-up to Python Data Analysis and goes even further and deeper. This time we focus on reproducibility and creating production-ready systems. We start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes, then dive into statistical data analysis using distribution algorithms and correlations. We’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining.

We dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. We will achieve parallelism to improve system performance by using multiple threads and speeding up your code.

By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.