[[ Download pdf ]] Python for Data Analysis: Data Wrangling with Pandas, Numpy, and IpythonAuthor Wes McKinney – Schematicwiringdiagram.co

Good introduction to Python Pandas and other libraries for data analysis However, the book goes directly from the introduction into pretty complicated examples As a reader new to R, Pandas, and statistical languages, it was hard work to learn the data structures and semantics After working through several web based tutorials, I had a better intuitive sense for how to solve problems with the framework presented by the author As documentation for Pandas alone, this book is useful. 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 andcase studies taking meaningful datasets instead of makey upy ones and using pandas and other tools to pose and answer questions For me, this would have made the book a muchpractical resource. 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 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 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 ggplots Python 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 requires 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 quantitative computing using Python is done.Disclaimer I received a free electronic copy of this book from the O Reilly Blogger Program 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 series, financial data analysis and advanced numpy.Ipython notebooks are available here, forked from the official repository of the book 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 series, financial data analysis and advanced numpy.Ipython notebooks are available here, forked from the official repository of the book Just averbose documentation After a promising introduction showing several real world usages of data manipulation, the book is nothingthan a documentation of pandas and libraries like numpy and matplotlib Moreover, many of functions described there are already deprecated, so just be aware of that Perhaps the best way of reading this book is just scanning it quickly for a general overview of pandas functionalities, so it can be used as a point of reference when needed. 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 arealike 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 i 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 arealike 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 theverbose of the two it does havecomplete 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 does.I 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 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 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 pivottable 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 somedict.get key, defaultvalue combinations iterable, k Generates a sequence of all possible k tuples of elements in the iterable permutations iterable, k Generates a sequence of all possible k tuples of elements in the iterable, respecting order 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 infor 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 however if you ve already selected Python as your analytical tool and it sounds like it sless the de facto analytical tool in many circles then this could be just the book for you It s hell of a book it took me a lot of time to get through, but it was worth it.Two key points 1 it s not time consuming because it s hard to comprehend or something quite the opposite, but it s very practical examples, examples examples, so it barely makes any sense to read it while not being in front of the keyboard the check the stuff out 2 people very differently understand terms like data analysis , artificial intelligence , machine learning data science this book is ab It s hell of a book it took me a lot of time to get through, but it was worth it.Two key points 1 it s not time consuming because it s hard to comprehend or something quite the opposite, but it s very practical examples, examples examples, so it barely makes any sense to read it while not being in front of the keyboard the check the stuff out 2 people very differently understand terms like data analysis , artificial intelligence , machine learning data science this book is about rather straightforward operations on data reading, sanitizing, filtering, grouping, pivoting, etc no advanced statistics, just the mundane but totally necessary stuff I d call it super flexible equivalent of SQL, but in Python and on any data sets The book is based mainly on NumPy pandas There are several other libraries mentions with some examples , but the only ones you can learn for real are the 2 I ve listed above.If you want to learnabout working with data using NumPy pandas, look no further this book is for you 5 5 stars Get 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 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 processWritten 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 GitHubUse 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