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

de Wes McKinney

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277473,649 (3.77)2
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment Learn basic and advanced NumPy (Numerical Python) features Get started with data analysis tools in the pandas library Use high-performance tools to load, clean, transform, merge, and reshape data Create scatter plots and static or interactive visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Measure data by points in time, whether it's specific instances, fixed periods, or intervals Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples… (mais)
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Exibindo 4 de 4
This has the flavor of an O'Reilly Nutshell book because it's mostly a tour of pandas features. Most of the examples are unmotivated and use random numbers instead of real data. If you're looking for a pandas tutorial this is probably fine. If you're looking for a pandas tutorial plus a primer on data analysis, this falls short of the bar set in the R world by Wickham's R for Data Science. ( )
  encephalical | Jun 17, 2019 |
A better title for this book might be Pandas and NumPy in Action

As the creator of the pandas project, a Python data analysis framework, [a:Wes McKinney|6007417|Wes McKinney|http://www.goodreads.com/assets/nophoto/nophoto-U-50x66-347709e8e0c4cd87940bf10aebee7a1c.jpg] is well placed to write this book. His experience and vision for the pandas framework is clear, and he is able to explain the main function and inner workings of both pandas and another package, NumPy, very well.

Although the title of the book suggests a broad look at the Python language for data analysis, McKinney almost exclusively focuses on an in-depth exploration of pandas. The book started with a great deal of promise, but as McKinney delved into the detail of NumPy and pandas, the ideas and examples of data analysis are replaced with random number datasets.

The book became a tiresome parade of pandas feature after pandas feature. Each example was stripped of meaning without any real world basis. It would have been great to see more real world cases drawn from McKinney's experience as a day to day user of pandas and Python for data analysis.

This book would be ideal if you're using, or thinking about using NumPy or pandas. If you're looking for a broader introduction to Data Analysis with Python, this might not be the book for you. ( )
  Beniaminus | Nov 1, 2017 |
452 p.
  BmoreMetroCouncil | Feb 9, 2017 |
A great handbook for anyone looking to do break down data sets in Python. This won't teach you what to look for or how to do data analysis, but it will show you all the tools to get it done. ( )
  trilliams | May 30, 2015 |
Exibindo 4 de 4
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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language. Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It's ideal for analysts new to Python and for Python programmers new to scientific computing. Use the IPython interactive shell as your primary development environment Learn basic and advanced NumPy (Numerical Python) features Get started with data analysis tools in the pandas library Use high-performance tools to load, clean, transform, merge, and reshape data Create scatter plots and static or interactive visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Measure data by points in time, whether it's specific instances, fixed periods, or intervals Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples

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