Read Online Python for Data Analysis Data Wrangling with Pandas NumPy and IPython 9781491957660 Computer Science Books

Product details Paperback 550 pages Publisher O'Reilly Media; 2 edition (October 20, 2017) Language English ISBN-10 1491957662

Python for Data Analysis Data Wrangling with Pandas NumPy and IPython 9781491957660 Computer Science Books Reviews

• 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.
• 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 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.
• 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 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.
• 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 was is 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.
• 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.