Python Data Analysis Cookbook : Clean, Scrape, Analyze, and Visualize Data with the Power of Python!
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| Title: | Python Data Analysis Cookbook : Clean, Scrape, Analyze, and Visualize Data with the Power of Python! |
|---|---|
| Description: | 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. |
| Authors: | Ivan Idris |
| Resource Type: | eBook. |
| Subjects: | Python (Computer program language), Database management |
| Categories: | COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / General, COMPUTERS / Languages / Python |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf – Type: ebook-epub Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Python Data Analysis Cookbook : Clean, Scrape, Analyze, and Visualize Data with the Power of Python! – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ivan+Idris%22">Ivan Idris</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Python+%28Computer+program+language%29%22">Python (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Database+management%22">Database management</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Data+Modeling+%26+Design%22">COMPUTERS / Data Science / Data Modeling & Design</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+General%22">COMPUTERS / Data Science / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Languages+%2F+Python%22">COMPUTERS / Languages / Python</searchLink> |
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