Data analysis with Python

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Content Introduction

【 Celebrity recommendation 】

“The scientific computing and data analysis community has been waiting for this book for years: lots of concrete practical recommendations, and lots of integrated application methods. This book will certainly become the definitive guide to technical computing in Python for years to come.”

— Fernando Perez, a research scientist at the University of California, Berkeley, and co-founder of IPython

【 Brief Introduction 】

Looking for a complete course in controlling, manipulating, organizing, and analyzing structured data in Python? Learn how to efficiently solve a wide variety of data analysis problems using Python libraries including NumPy, Pandas, Matplotlib, and IPython.

Because Author Wes McKinney is the lead author of pandas, this book can also serve as a practical guide to scientific computing for data-intensive applications using Python. This book is suitable for analysts new to Python and Python programmers new to scientific computing.

• Use IPython, an interactive Shell, as your primary development environment.

• Learn basic and advanced knowledge of NumPy (Numerical Python).

• Start with the data analysis tools in pandas.

• Load, clean up, transform, merge, and reshape data using high-performance tools.

• Use Matplotlib to create scatter diagrams and static or interactive visualizations.

• Use pandas’ Groupby function to slice, slice, and summarize datasets.

• Handling a variety of time series data.

• Use detailed case studies to solve problems in Web analytics, social sciences, finance, and economics.

About the author · · · ·

Dr. Wes McKinney is a senior data analyst with extensive experience in Python libraries such as NumPy, PANDAS, Matplotlib, and IPython. He has written a number of classic articles on Python data analysis, which have been widely republished by the technical community. He is one of the recognized authorities in Python and the open source technical community. He has developed a well-known open source Python library for data analysis — Pandas, which is well received by users. Prior to founding Lambda Foundry, a firm focused on enterprise data analysis, He was a quantitative analyst at AQR Capital Management.