Learning Pandas - Python Data Discovery and Analysis Made Easy

This learner's guide will help you understand how to use the features of pandas for interactive data manipulation and analysis.

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 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

A Tour of pandas
Installing pandas
Numpy for pandas
The pandas Series Object
The pandas Dataframe Object
Accessing Data
Tidying up Your Data
Combining and Reshaping Data
Grouping and Aggregating Data
Time-series Data
Visualization
Applications to Finance