Learning pandas - Second Edition

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
pandas and Data Science and Analysis
Up and running with pandas
Representing univariate data with the Series
Representing tabular and multivariate data with the DataFrame
Manipulation and indexing of DataFrame objects
Indexing Data
Categorical Data
Numeric and Statistical Methods
Grouping and Aggregating Data
Tidying Up Your Data
Combining, Relating and Reshaping Data
Data Aggregation
Time-Series Modelling
Visualization
Applications to Finance