Learning pandas : High-performance data manipulation and analysis in Python, 2/e

Michael Heydt

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Key Features

  • Get comfortable using pandas and Python as an effective data exploration and analysis tool
  • Explore pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process
  • A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas

Book Description

You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.

With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

What you will learn

  • Understand how data analysts and scientists think about of the processes of gathering and understanding data
  • Learn how pandas can be used to support the end-to-end process of data analysis
  • Use pandas Series and DataFrame objects to represent single and multivariate data
  • Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources
  • How to access data from external sources such as files, databases, and web services
  • Represent and manipulate time-series data and the many of the intricacies involved with this type of data
  • How to visualize statistical information
  • How to use pandas to solve several common data representation and analysis problems within finance

About the Author

Michael Heydt is a technologist, entrepreneur, and educator with decades of professional software development and financial and commodities trading experience. He has worked extensively on Wall Street specializing in the development of distributed, actor-based, highperformance, and high-availability trading systems. He is currently founder of Micro Trading Services, a company that focuses on creating cloud and micro service-based software solutions for finance and commodities trading. He holds a master's in science in mathematics and computer science from Drexel University, and an executive master's of technology management from the University of Pennsylvania School of Applied Science and the Wharton School of Business.

Table of Contents

  1. pandas and Data Science and Analysis
  2. Up and running with pandas
  3. Representing univariate data with the Series
  4. Representing tabular and multivariate data with the DataFrame
  5. Manipulation and indexing of DataFrame objects
  6. Indexing Data
  7. Categorical Data
  8. Numeric and Statistical Methods
  9. Grouping and Aggregating Data
  10. Tidying Up Your Data
  11. Combining, Relating and Reshaping Data
  12. Data Aggregation
  13. Time-Series Modelling
  14. Visualization
  15. Applications to Finance