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    1. Data och IT
    2. Programmeringsböcker
    3. Webbprogrammering

    Python Data Analysis Cookbook

    Clean, scrape, analyze, and visualize data with the power of Python!

    AvIvan Idris

    Häftad, Engelska, 2016

    730 kr

    Beställningsvara. Skickas inom 5-8 vardagar. Fri frakt över 249 kr.

    Fler format och utgåvor

    E-bok

    535 kr

    Beskrivning

    Over 140 practical recipes to help you make sense of your data with ease and build production-ready data appsKey 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 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.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 concurrencyWho this book is forThis book teaches Python data analysis at an intermediate level with the goal of transforming you from journeyman to master. Basic Python and data analysis skills and affinity are assumed.

    Produktinformation

    • Utgivningsdatum:2016-07-22
    • Mått:191 x 235 x 25 mm
    • Vikt:856 g
    • Format:Häftad
    • Språk:Engelska
    • Antal sidor:462
    • Förlag:Packt Publishing Limited
    • ISBN:9781785282287

    Utforska kategorier

    • Webbprogrammering inom Data och IT
    • Programspråk inom Data och IT

    Mer om författaren

    Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse 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. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

    Innehållsförteckning

    • Table of ContentsLaying the foundations for reproducible data analysisCreating attractive data visualizationsStatistical data analysis and probabilityDealing with data and numerical issuesWeb mining, Databases and Big DataSignal processing and timeseriesSelecting stocks with financial data analysisText mining and social network analysisEnsemble learning and dimension reductionEvaluating classifiers, regressors and clustersAnalyzing imagesParallelism and performanceAppendix A: GlossaryAppendix B: Function ReferenceAppendix C: Online ResourcesAppendix D: Tips and Tricks for Command Line and Miscellaneous Tools