Key Features
- Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types
- Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning
- Authored by Ivan Idris, expert in python programming and proud author of eight highly reviewed books
Book Description
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.
What you will learn
- Set up reproducible data analysis
- Clean and transform data
- Apply advanced statistical analysis
- Create attractive data visualizations
- Web scrape and work with databases, Hadoop, and Spark
- Analyze images and time series data
- Mine text and analyze social networks
- Use machine learning and evaluate the results
- Take advantage of parallelism and concurrency
About the Author
Ivan Idris was born in Bulgaria from 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, datawarehouse developer, and QA analyst.
His main 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.
Ivan Idris was born in Bulgaria from 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, datawarehouse developer, and QA analyst. His main 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.
