Learning pandas - Softcover

Inhaltsangabe

Key Features

• Employ the use of pandas for data analysis closely to focus more on analysis and less on programming
• Get programmers comfortable in performing data exploration and analysis on Python using pandas
• Step-by-step demonstration of using Python and pandas with interactive and incremental examples to facilitate learning

Book Description

This 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 Learn

• Install pandas on Windows, Mac, and Linux using the Anaconda Python distribution
• Learn how pandas builds on NumPy to implement flexible indexed data
• Adopt pandas' Series and DataFrame objects to represent one- and two-dimensional data constructs
• Index, slice, and transform data to derive meaning from information
• Load data from files, databases, and web services
• Manipulate dates, times, and time series data
• Group, aggregate, and summarize data
• Visualize techniques for pandas and statistical data

About the Author

Michael 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 Content

1. A Tour of pandas
2. Installing pandas
3. Numpy for pandas
4. The pandas Series Object
5. The pandas Dataframe Object
6. Accessing Data
7. Tidying up Your Data
8. Combining and Reshaping Data
9. Grouping and Aggregating Data
10. Time-series Data
11. Visualization
12. Applications to Finance

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