Python for Data Analysis
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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 examples
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This book is helpful IF you already use python..or are willing to run through a bunch of basic python learning beside learning how to use Python for data analysis. There are some examples, but they are not fully annotated. Wes McKinney has produced a useful package with pandas. I warn readers (especially newbie readers) to be very patient in working out examples and annotating them until you understand what is happening.
Excellent guide to data analysis with pandas. The author gives a good explanation on the underlying pandas rationale and functionality, providing sumptuous examples and deep dives (in appendix) with cross-referencing chapters without deviating too far away.
Professionally I use pandas on a biweekly basis and this book has put so many things in place.
