Python Data Analysis Cookbook
Ivan Idris
Vendu par THE SAINT BOOKSTORE, Southport, Royaume-Uni
Vendeur AbeBooks depuis 14 juin 2006
Neuf(s) - Couverture souple
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Ajouter au panierVendu par THE SAINT BOOKSTORE, Southport, Royaume-Uni
Vendeur AbeBooks depuis 14 juin 2006
Etat : Neuf
Quantité disponible : Plus de 20 disponibles
Ajouter au panierThis item is printed on demand. New copy - Usually dispatched within 5-9 working days 851.
N° de réf. du vendeur C9781785282287
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.
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