Python for Data Analysis, 2e: Data Wrangling with Pandas, Numpy, and Ipython

This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While 'data analysis' is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis.

New for the Second Edition
The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and developing rapidly. In this updated and expanded second edition, I have overhauled the chapters to account both for incompatible changes and deprecations as well as new features that have occurred in the last five years.

I’ve also added fresh content to introduce tools that either did not exist in 2012 or had not matured enough to make the first cut. Finally, I have tried to avoid writing about new or cutting-edge open source projects that may not have had a chance to mature. I would like readers of this edition to find that the content is still almost as relevant in 2020 or 2021 as it is in 2017.

The major updates in this second edition include:
All code, including the Python tutorial, updated for Python 3.6 (the first edition used Python 2.7)
Updated Python install instructions for the Anaconda Python Distribution & other Python packages
Updates for the latest versions of the pandas library in 2017
A new chapter on some more advanced pandas tools, and some other usage tips
A brief introduction to using statsmodels and scikit-learn
Reorganized since from the first edition to make the book more accessible to newcomers.
Product description
About the Author
Wes McKinney is the main author of pandas, the popular open sourcePython library for data analysis. Wes is an active speaker andparticipant in the Python and open source communities. He worked as a quantitative analyst at AQR Capital Management and Python consultant before founding DataPad, a data analytics company, in 2013. He graduated from MIT with an S.B. in Mathematics. Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examplesI never regret purchasing this book. It is my Swiss army knife for analysis in Python. I used R extensively in previous role and in university. Thus, despite understanding some techniques like aggregation, pivots, I needed to know how to their Python syntax. This book is sharp, straightforward, in helping you answer questions like "How do I do this (data wrangling technique) in Python?"