Download PDF Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython By Wes McKinney

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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython-Wes McKinney

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Ebook About
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 computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples

Book Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Review :



Wes is the creator of Pandas but he is not an effective writer. This has left a bad taste of pandas in my mind. A lot of examples created in this book are using random numbers and this is a poor way of teaching someone as it's too abstract. Random number generated examples rarely have anything to do with data encountered in real life.This book's problem is the classic curse of knowledge. The author does not know what it's like to get started with pandas and what are the difficulties users will have.
This book falls somewhere between a manual page providing one example per function and a cookbook, tending more toward the former. Examples are dry and most are constructed using random data. There is very little in the way of practical use cases. I bought the book hoping to get some inspiration for using numpy and/or pandas for some types of analyses I find myself doing, but that didn't happen. Probably I've gathered enough overview that I now can put together useful queries that will provide useful hits on Stack Exchange. I wish I had better to say.

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