Python for Data Analysis  (English, Paperback, McKinney Wes)

Python for Data Analysis, published by O’Reilly, is concerned with the minute details of manipulating, processing, cleaning, and crunching data in Python. It is also a practical and modern introduction to scientific computing in Python, designed for data-intensive applications. The book discusses the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. It is packed with practical cases studies and is ideal for analysts new to Python and for Python programmers new to scientific computing. Other salient features of the book include: explanation of the use of the IPython interactive shell as your primary development environment, discussion basic and advanced NumPy (Numerical Python) features, explanation of data analysis tools in the pandas library, explanation of the use high-performance tools to load, clean, transform, merge, and reshape data, explanations to how to create scatter plots and static or interactive visualizations with matplotlib, how to apply the pandas groupby facility to slice, dice, and summarize data sets, and how to measure data by points in time, whether it’s specific instances, fixed periods, or intervals. The title will also help you learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

About Wes McKinney

Wes McKinney is the foremost author of pandas, the popular open source Python library for data analysis. He graduated from MIT. He is an active talker and contributor in the Python and open source communities. McKinney served as a quantitative analyst at AQR Capital Management before establishing an enterprise data analysis company, Lambda Foundry, in 2012.

About O’Reilly

O’Reilly is a renowned media house which has been publishing technology books, tech conferences, IT courses and news, online services, magazines, and research journals since 1978. Some of the books published by O’Reilly are Lean UX Workshop, Software Architecture Fundamentals Part 2: Taking a Deeper Dive, Bioinformatics Data Skills, Head First JavaScript Programming, and Python Networking Programming Cookbook.