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Python Data Analysis Cookbook
Key FeaturesAnalyze Big Data sets, create attractive visualizations, and manipulate and process various data typesPacked with rich recipes to help you learn and explore amazing algorithms for statistics and machine learningAuthored by Ivan Idris, expert in python programming and proud author of eight highly reviewed booksBook DescriptionData 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 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 learnSet up reproducible data analysisClean and transform dataApply advanced statistical analysisCreate attractive data visualizationsWeb scrape and work with databases, Hadoop, and SparkAnalyze images and time series dataMine text and analyze social networksUse machine learning and evaluate the resultsTake advantage of parallelism and concurrencyAbout the AuthorIvan 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.
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 learnSet up reproducible data analysisClean and transform dataApply advanced statistical analysisCreate attractive data visualizationsWeb scrape and work with databases, Hadoop, and SparkAnalyze images and time series dataMine text and analyze social networksUse machine learning and evaluate the resultsTake advantage of parallelism and concurrencyAbout the AuthorIvan 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.
- GenresProgrammingNonfiction
403 pages, Paperback
Published July 22, 2016
About the author
Ivan Idris
14 books26 followersIvan Idris is the author of NumPy Beginner's Guide and NumPy Cookbook. He was born in Bulgaria from Indonesian parents. He moved to the Netherlands in the 1990s, where he graduated from high school and got a MSc in Experimental Physics.
His graduation thesis had a strong emphasis on Applied Computer Science. After graduating he worked for several companies as Java Developer, Datawarehouse Developer and QA Analyst.
His main professional interests are Business Intelligence, Big Data and Cloud Computing. Ivan Idris enjoys writing clean testable code and interesting technical articles.
His graduation thesis had a strong emphasis on Applied Computer Science. After graduating he worked for several companies as Java Developer, Datawarehouse Developer and QA Analyst.
His main professional interests are Business Intelligence, Big Data and Cloud Computing. Ivan Idris enjoys writing clean testable code and interesting technical articles.
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