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Learning pandas - Python Data Discovery and Analysis Made Easy
Key FeaturesEmploy the use of pandas for data analysis closely to focus more on analysis and less on programmingGet programmers comfortable in performing data exploration and analysis on Python using pandasStep-by-step demonstration of using Python and pandas with interactive and incremental examples to facilitate learningBook DescriptionThis learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.
This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.
With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
What You Will LearnInstall pandas on Windows, Mac, and Linux using the Anaconda Python distributionLearn how pandas builds on NumPy to implement flexible indexed dataAdopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructsIndex, slice, and transform data to derive meaning from informationLoad data from files, databases, and web servicesManipulate dates, times, and time series dataGroup, aggregate, and summarize dataVisualize techniques for pandas and statistical dataAbout the AuthorMichael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.
Table of ContentA Tour of pandasInstalling pandasNumpy for pandasThe pandas Series ObjectThe pandas Dataframe ObjectAccessing DataTidying up Your DataCombining and Reshaping DataGrouping and Aggregating DataTime-series DataVisualizationApplications to Finance
This book is your ideal guide to learning about pandas, all the way from installing it to creating one- and two-dimensional indexed data structures, indexing and slicing-and-dicing that data to derive results, loading data from local and Internet-based resources, and finally creating effective visualizations to form quick insights. You start with an overview of pandas and NumPy and then dive into the details of pandas, covering pandas' Series and DataFrame objects, before ending with a quick review of using pandas for several problems in finance.
With the knowledge you gain from this book, you will be able to quickly begin your journey into the exciting world of data science and analysis.
What You Will LearnInstall pandas on Windows, Mac, and Linux using the Anaconda Python distributionLearn how pandas builds on NumPy to implement flexible indexed dataAdopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructsIndex, slice, and transform data to derive meaning from informationLoad data from files, databases, and web servicesManipulate dates, times, and time series dataGroup, aggregate, and summarize dataVisualize techniques for pandas and statistical dataAbout the AuthorMichael Heydt is an independent consultant, educator, and trainer with nearly 30 years of professional software development experience, during which time, he focused on Agile software design and implementation using advanced technologies in multiple verticals, including media, finance, energy, and healthcare. Since 2005, he has specialized in building energy and financial trading systems for major investment banks on Wall Street and for several global energy-trading companies, utilizing .NET, C#, WPF, TPL, DataFlow, Python, R, Mono, iOS, and Android. His current interests include creating seamless applications using desktop, mobile, and wearable technologies, which utilize high-concurrency, high-availability, and real-time data analytics; augmented and virtual reality; cloud services; messaging; computer vision; natural user interfaces; and software-defined networks. He is the author of numerous technology articles, papers, and books. He is a frequent speaker at .NET user groups and various mobile and cloud conferences, and he regularly delivers webinars and conducts training courses on emerging and advanced technologies.
Table of ContentA Tour of pandasInstalling pandasNumpy for pandasThe pandas Series ObjectThe pandas Dataframe ObjectAccessing DataTidying up Your DataCombining and Reshaping DataGrouping and Aggregating DataTime-series DataVisualizationApplications to Finance
506 pages, Kindle Edition
First published March 24, 2015
20 people are currently reading
39 people want to read
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Displaying 1 - 5 of 5 reviews
April 2, 2022
Dobra książka w tym temacie. Można sobie ugruntować wiedzę na temat pandas. Fajne jest to, że czytanie nie wymaga w danym momencie pracy z kompem, więc można ją czytać wszędzie na telefonie, bo przykłady są przejrzyście podane. Jest to oczywiście związane również ze specyfiką tej biblioteki...ale dzięki temu odbiór książki jest właśnie lepszy.
May 28, 2018
A practical and hands-on book for learning Pandas. Good resource to get started with this important and popular Python package.
March 3, 2017
Not a bad introduction--could be more practical.
March 28, 2016
Although the book has many detailed and step-by-step examples and hence could potentially serve as a good reference book, some frequent typos make it somewhat confusing to understand certain part of the book. In addition, some syntax has already been outdated (for example pandas.io.data for DataReader).
March 10, 2016
Best choice indeed for pandas starters
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