AZW3

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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

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