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 focuses on reproducibility and creating production-ready systems. You will 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. You’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.
In this book, you will 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. You 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.
Ivan Idris was born in Bulgaria to Indonesian parents. He moved to the Netherlands and graduated in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a software developer, data warehouse developer, and QA analyst.
His professional interests are business intelligence, big data, and cloud computing. He enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginner’s Guide, NumPy Cookbook, Learning NumPy, and Python Data Analysis, all by Packt Publishing.
Chapter 1: Laying the Foundation for Reproducible Data Analysis
Chapter 2: Creating Attractive Data Visualizations
Chapter 3: Statistical Data Analysis and Probability
Chapter 4: Dealing with Data and Numerical Issues
Chapter 5: Web Mining, Databases, and Big Data
Chapter 6: Signal Processing and Timeseries
Chapter 7: Selecting Stocks with Financial Data Analysis
Chapter 8: Text Mining and Social Network Analysis
Chapter 9: Ensemble Learning and Dimensionality Reduction
Chapter 10: Evaluating Classifiers, Regressors, and Clusters
Chapter 11: Analyzing Images
Chapter 12: Parallelism and Performance
Appendix A: Glossary
Appendix B: Function Reference
Appendix C: Online Resources
Appendix D: Tips and Tricks for Command-Line and Miscellaneous Tools
