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 Jupiter 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 Jupiter 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 group by 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.
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Language: English, Francais, Italiano, Espanol, Deutsch, chinese
Wes McKinney is a New York?based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. He’s now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.Wes was later the co-founder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software. <div id="
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.
As others have said, this book provides a good manual. If you have a project in mind and some programming background, you can adapt the examples in the book to complete the task. That said, a lot of the book reads more as documentation than instruction, and the documentation is more sparse than the official pandas documentation. Furthermore, some of the examples are rather opaque in understanding the main point, and the use of random number generators for example data manipulation sometimes makes it difficult to understand what a specific block of code is doing.Overall, this book provides a jumping off point in understanding the capabilities of pandas as well as its strengths, but it wasn’t terribly useful in even basic data science workflow and concepts. For that, I highly recommend something like Hadley Wickham’s “R for Data Science,” which is much more approachable and rewarding in its use of example datasets, its more personable writing style, and its outlining of good practices for data science.
This book has been my foundation of using python as a data analyst.This book primarily focuses on the pandas Python library, which is awesome at processing and organizing data (Python pandas is like MS Excel times 100. This is not an exaggeration). It also introduces the reader into numpy (lower level number crunching and arrays), matplotlib (data visualizations), scikitlearn (machine learning), and other useful data science libraries. The book contains other book recommendations for continuing education.Although this would be a challenging book for a brand new Python user, I would still recommend it, especially if you are currently doing a lot of work in MS Excel and/ or exporting data from databases. I had a few false starts learning Python, and my biggest stumbling block was lack of application in what I was learning. This book puts practical tools in the reader’s hands very quickly. I personally don’t have time to make goofy games etc. that other books have used as practice examples. Despite other reviews criticizing the use of random data throughout the book, I found the examples easy to follow and useful. I would also argue that learning how to generate random data is useful in itself (thus the purpose of the numpy random library), and that there are practical examples throughout the book. Chapter 14 devoted to real-world data analysis examples.I am almost finished with my second time through the book, this time working through every example. This book has been well worth the hours spent in it. For context, I previously relied on Excel, SQL, and some AutoHotKey. This book has significantly improved how I work.Thanks, Wes and team.
This is the Python book for the data scientist: already knows Python or at least OOP programming, but wants to be able to utilize the native and NumPy structures for writing machine learning algorithms. Slicing, broadcasting, tuples, pandas data frames — all useful for applying Python’s tools to data science. This is not a beginner book, but it’s exactly what I needed to learn the details for translating equations to code.
First of all, if you’ve never used python before find an intro to python. This book is more for people who are familiar to intermediate python programmers. Meaning this is not for the complete beginner.The book mainly deals with introducing you to Numpy and Pandas libraries used for data analysis, such cleaning, manipulating wrangling, processing and visualisation.Its a great book to have as a reference and learning data analysis techniques. There are plenty of code examples. So worth the purchase.Only negative I wish there were mini projects to learn from.
This book covers all of the basics that you would want to know to get started in programming in Python for data analysis, as the title implies, but it doesn’t really offer compelling real-world examples. The data seem to be made up and the analyses don’t go into enough detail to help you really learn how pandas and numpy work. Overall this is a decent starter book but you will have to bookmark the python and pandas documentation online if you want to have a reference to all of the functionality those tools have, and there are many places online where you can get better examples to learn from. If you haven’t made your mind up about which tool to use for data analysis, I highly recommend checking out dplyr in R, which has an excellent free book online (R for data science, hadley wickham). I find it very easy to learn and it is much easier to set up R and RStudio than it is to set up Python, even though I love Python and Pandas.
This book is generally considered a classic but I am in 2 minds over it. It gives an introduction to the Python language, which is nice for a beginner. It also gives very good introductions to numpy, pandas, matplotlib and seaborn. I had not heard of the latter so the book was of great value there. However, all of these libraries are huge; pandas alone is massive. This leaves the author being stuck in terms of how deep to go. Some areas are well covered and others are just skimmed over forcing you to look deeper on the internet. Adding to the problem is that these libraries are being continually updated. I was left with a growing feeling that books like this are becoming redundant. There is so much free material online now that if someone stole this book from me it would not make a jot of difference.
I have purchased other books for jumping into machine learning using Python but they always somewhat gloss over the basics, and you have to accept a bit of magic around Pandas, Matplotlib etc to follow along. I’m so glad I went back to build a solid foundation with this book, so I’m no longer fumbling around with magic commands or spending a huge proportion of time trawling Stack Overflow.Probably my favourite aspect of this book is that you can just read it- every single concept is demonstrated in code, on the paper, with the full input and outputs. The only time I’ve opened my editor is to play around with concepts I wanted to clarify- the rest has been just a good solid read with everything clearly demonstrated. It’s well structured and builds concepts as you progress but is also an excellent reference book I can see myself dipping back into time and again.I think this is essential foundational material for starting your journey into data analysis and/or machine learning with Python.
The content of the book is good. The print quality is bad. I can see the texts through the pages. Most of the figures are poorly printed, looks like run out of ink. I can understand that this is paperback print, but I would expect better quality for £30 book. It is too much hassle to return this in the current situation.
A good reference book. It did a reasonable job in breaking python data analysis down into consumable chunks, however there are many free resources out there that would do an equally good job. This book rarely focuses on the bigger picture, it rather gives you line of code after line of code without ever going into real world usage of that code/construct. To use an analogy, If I was looking to build a tree house this book gave me hammer and nails without providing a blueprint on how to build that house.Look for a book that takes a project based approach to learning if you are looking to get into python data analysis.
Wes is a great writer and teacher, I feel I am learning more about data analysis with python by tracing out the code in the book (Wes refers to this as strengthening one’s “muscle memory”) in my Juptyer notebooks on my laptop than I had from trying moocs on data analysis.
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