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Python for Data AnalysisGet complete instructions for manipulating processing cleaning and crunching datasets in Python Updated for Python the second edition of this hands on guide is packed with practical case studies that Python for PDFEPUBshow 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 processWritten by Wes McKinney t. 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 worldPython 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 uality 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 gapEven 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 efficiently use the shell as a development tool to the point I have stopped automatically using the ipython notebook or pydev eclipse when starting new projects and I use the shell instead because its introspection and debugging capabilities made it much easier to work I had started using pandas for a data structure because I liked the similarities with R data frames this book showed me where pandas goes well beyond that With matplotlib I could make specific plots this book showed me how to use the pandas interface to make them a natural part of the workflow even if it is not yet at the level of a grammer such as ggplotsPython for Data Analysis does not just teach how to use the Python scientific stack it also teaches a workflow for technical computing And this is beyond what you can get from reading off the web it probably really reuires the opportunity to work alongside someone who knows what they are doing to see the practices that makes them productive As such I would recommend it for anyone who does scientific and technical computing whether in the sciences engineering finance or other areas where uantitative computing using Python is doneDisclaimer I received a free electronic copy of this book from the O'Reilly Blogger Program

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Ical PythonGet 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. 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 • LongWide 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 • pivottable has a marginsTrueFalse option that can be used to show subtotals • DataFrame assign and pipe method enable easier method chaining • for i value in enumeratecollection   • value  somedictgetkey defaultvalue • combinationsiterable k Generates a seuence of all possible k tuples of elements in the iterable permutationsiterable k Generates a seuence of all possible k tuples of elements in the iterable respecting order

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review Python for Data Analysis 103 ß [PDF / Epub] ✅ Python for Data Analysis Author Wes McKinney – Gwairsoft.co.uk Get complete instructions for manipulating processing cleaning and crunching datasets in Python Updated for Python 36 the second edition of this hands on guide is packed with practical case studies th Get complete instructions fHe 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 GitHubUse the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy Numer. 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 appendixThis 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 techniues by showing how it helps answer uestions in the data like R for Data Science doesI find that matplotlib is unusably low level for modern data science and you should skip that section in any of the books and learn either Altair or plotnine a clone of ggplot for your plotting work in Python