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

## [[ BOOKS ]] ✬ Python for Data Analysis Author Wes McKinney – Chainnews.us

- ebook
- 550 pages
- Python for Data Analysis
- Wes McKinney
- 22 April 2017 Wes McKinney
- 9781491957639

A better title for this book might be

Pandas and NumPy in ActionAs the creator of the pandas project, a Python data analysis framework, Wes McKinney 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 real world cases draw...For some time now I have been using R and Python for data analysis And I have long ago discovered the Python technical stack of ipython, NumPy, Scipy, and Matplotlib and I thought I knew what I was doing I even dipped my toe into pandas as my data structure for analysis But Python for Data Analysis showed me entire worlds of improvement in my workflow and my ability to work with data in the messy form that is found in the real world.Python, like most interpreted languages, is slow compared to compiled languages But there is a technical stack that started with the NumPy libraries and has grown to include Scipy, Matplotlib graphing , ipython shell and pandas you get high quality and fast algorithm and data structure Fortran and C libraries underneath Python But while these libraries are designed to be used together, documentation tends to be only about one at a time, and very little puts it all together as an integrated whole McKinney s Python for Data Analysis fills that gap.Even though I have been using iPython, NumPy, Scipy and Matplotlib for years, and pandas for about half a year, going through this book makes me feel like I was a rank novice I learned how to efficient...

Good introduction to pandas data analysis library by its main contributor, Wes McKinney Also covers useful Python tools libraries for data analysis such as ipython and numpy Lots of examples.Didn t read the last three chapters on time ser...

Selected notes pickle is only recommended as a short term storage format The problem is that it is hard to guarantee that the format will be stable over time an object pickled today may not unpickle with a later version of a library The map method on a Series accepts a function or dict like object containing a mapping, Long Wide reshaping can be done by pivot long to wide , melt wide to long , stack wide to long , and unstack long to wide Pandas have a category type similar to R s factor type It can be ordered or unordered pivot_table has a margins True False option that can be used to show subtotals DataFrame assign and pipe method enable easier method chaining for i, value in enumerate collection value some_dict.get key, d...

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This book is a well written, verbose introduction to Pandas by the main author of that library Don t expect to learn much besides Pandas matplotlib gets a brief mention, and there is a short Numpy section, but broadcasting is relegated to an appendix.This book is a peer of Python Data Science Handbook by Jake VanderPlas, and they are alike than different They both start with long sections on manipulating data in Numpy and Pandas, on mostly made up examples of random numbers This book is the verbose of the two it does have complete coverage of Pandas functionality albeit less coverage of Numpy , and it also takes longer to read It s only 4 stars because it s not very engaging I prefer a book like this to introduce some real data early and to motivate the learning of techniques by showing how it helps answer questions in the data, like R for Data Science do...

I did copy editing on this book, so my review is of an unfinished but close to finished version That being said McKinney is the principal author on pandas, a Python package for doing data transformation and statistical analysis The book is largely about pandas and NumPy , but also delves into general methodologies for munging data and performing analytical operations on them e.g., normalizing messy data and turning it into graphs and tables he also delves into some semi esoteric information about how Python works at very low levels, and discusses ways to optimize data structures so that you can get maximum performance from your programs This book won t be useful for someone looking for a book that discusses data analysis in a broad sense, nor would it be useful for someone looking for a generalist s book on Python

howeverif you ve already selected Python as your analytical...This book was the perfect set of training wheels for me, especially since my main goal was to operate on economic and financial data By chapter 4 practically the beginning of this book , I was able to sample random stocks, run correlations between stocks and commodities I think th...

Just a verbose documentation After a promising introduction showing several real world usages of data manipulation, the book is nothing than a documentation of pandas and libraries like numpy and matplotlib Moreover, many of functions descri...

This book is a reasonably comprehensive tutorial to pandas the Python library for data wrangling As a tutorial, it works well.But it wasn t quite what I was expecting I was expecting less tutorial and case studies taking meaningful datasets instead of makey ...