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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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About the Author
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.
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Product details
Paperback: 550 pages
Publisher: O'Reilly Media; 2 edition (October 20, 2017)
Language: English
ISBN-10: 1491957662
ISBN-13: 978-1491957660
Product Dimensions:
7 x 1.3 x 9.1 inches
Shipping Weight: 1.9 pounds (View shipping rates and policies)
Average Customer Review:
4.1 out of 5 stars
62 customer reviews
Amazon Best Sellers Rank:
#6,034 in Books (See Top 100 in Books)
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.
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.
This book gave me my first job. And I am still learning it. It is simple, talks some general idea why functions design like this, and introduces some practical functions. Because in real life real job you always need to look up documentation or to google certain functions, I think the idea why Wes makes functions/variables like this, and what he wants to develop in the future is very important. anyway, I think this book is for data analysis beginner and some intermediate users. I learned Python first so I recommend beginners who want to use Python for Data Analyst/Scientist to learn Python Programming first/simultaneously. At least understand lambda and python expressions, otherwise, you can't feel the full magic.
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 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.
I’m relatively new to python and data analysis tools like Pandas. Started a month ago and this book makes several things clear — wish I had had it a few weeks ago.We’ll written and generally doesn’t get into minutiae. Very useful.
It's really comprehensive, and covers almost everything you might want to know in mainstream Python data analysis. I like that the examples are complete so you don't have to go and play with the code to understand. Yes, its good to play with the code, but I prefer this style. It's not exhaustive on Pandas, but it covers a lot more than that so you best go to a specialised Pandas book if that is what you want.
I'm getting my feet wet with Python and Python for Data Analysis. This is a great book for beginners to advance users who want to explore and learn Data analysis using Python. In addition to using Python for other purposes.
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