Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Off the bat, get this book!This text uses Numpy and Pandas for data analysis in a far more extensive way than many texts on the market. The text starts from a very basic principle: transforming lists into Pandas Series, moving on to other iterables and transforming them into both Series and DataFrames. The syntax in this book is straightforward. Nonetheless, this book's pages are less rigid, and care must be taken when flipping pages.Strengths:Simplicity of syntax: This book's Python codes are very simple and easily understood by anyone who has used basic Python for a while. For people who may be new to the language, the first two chapters gradually introduce basic Python language constructs that form the bedrock for the chapters that follow. While this text does not teach Python overall, it does a decent job of giving you the tools needed to analyse data from scratch.Organisation: Unlike other books that require readers to start from chapter 1 to the end, and make chapters dependent on previous chapters' codes, this book allows readers to jump seamlessly between chapters. I have moved pretty quickly through the text by jumping to topics that interest me or give me the kicks I need for a project I am working on. Its GitHub site also supports readers to customise codes for their own use.Datasets: The success of any analysis study is the practice on those tools one has acquired. This text provides numerous datasets that one could use for practice. Moreover, a reader can comfortably simulate their own data to learn. I understand simulated data may not be like real-world data, but they test your skills for future work. Should you need more practice with this text, the UCI data repository comes in handy.More packages:Although this book is heavily bent on Pandas and Numpy, it does an excellent job integrating other packages. For instance, statsmodels, scipy and other packages are used along with Pandas and Numpy, offering simple ways to orchestrate models and use them for prediction.Weaknesses:1. Weak pages: A major flaw of this book is its weak pages. Although the binding is perfect, the pages themselves are too fragile. A few days ago, I was careless withmy coffee and I had a spill on the book. That little carelessness has deformed my otherwise lovely and daily motivational Pandas and Numpy book.2. Lack of end-of-chapter exercises: This book would have had no competition had it had end-of-chapter exercises. For some of us who love to nail concepts to the very bone, practice makes perfect. The lack thereof makes readers seek practice elsewhere.I have always dreaded Numpy, and even worse, Pandas. This book has removed my dread and made me comfortable with these packages in the last month. Despite its weak pages and lack of exercises, this book offers simplified syntax, a huge list of datasets, better organisation and more packages that work in tandem with Pandas and Numpy.

In the ever-evolving world of data analysis, "Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" shines as a beacon for beginners and experienced data enthusiasts alike. It is a testament to the book's caliber that it manages to comprehensively cover Python's powerful data manipulation tools in such an approachable manner.The book's primary strength lies in its thorough exploration of data wrangling. For the uninitiated, data wrangling is the art of maneuvering raw data into a more digestible form for analysis, and this book nails it. By delving deep into the intricacies of libraries like pandas and NumPyWhat's particularly commendable is the balance between theory and practice. While it's brimming with technical details and explanations, it never feels overwhelming. Instead, readers are constantly engaged with practical examples, ensuring that learning is both meaningful and applicable.Whether you're a beginner stepping into the world of data or a seasoned pro, this book offers invaluable insights and skills that will enhance your data-wrangling journey.

Took this course at the university of Louisville and nightly enjoyed it! My professor was wonderful. He’s a fun guy! Made it very interesting and interactive.I knew it from taking it with Stanford online last year.Still a good class !

It took about two months to read the book, and I changed using iPython to Jupyter Notebook for the examples. A lot of the info take in!But unfortunately there are some errors in the book. I'm not talking about the updated pandas since the book was written in 2022. You can see the errors on this book website in the errata page, and think there are more errors not on the this page.I notice that he placed in .ipynb in github for all the chapters in this book and there are some correction in these chapters that are not in the book. I notice that github also said that it save all the cells and the results with the .ipynb and I place my some of my chapters in .ipynb and they showed both. But not his - only shows the cells and not the results. It's look like he remove the results and then placed .ipynb on Github, or never do the results and placed the .ipynb on github without checking them.It's a good book, but I'm not sure would be another book by the author however.

still using it today

If you need to learn 'data wrangling' and Python, then this book is right. I was amazed at how extensive the coverage of information in this book. I am still learning Python and refer to this book. Plenty of examples and exercises. I am a beginner and well able to use it. It can also be used by people with higher skills or refreshing for advanced users.

As with all O’reilly books I’ve purchased, great quality and a great reference. Really good to go back to the fundamentals.

... in that this book is well written, insightful, and with good code snippets. Maybe the author spends too much time on the Python language itself, but it is time spent on parts of python relevant to data analysis. But those figures - why bother? Multi-colored graphs printed as grayscale are just confusing. O'Reilly - Please start printing your books in color again or don't bother with actual printed books. If you need to raise your prices do so, your readers will understand.

I bought it 2nd time so I knew the content.

This book is awesome. I will recommend it to anyone seeking to expand their understanding about data wrangling. The author did a good job.

Muy bueno

Good on numpy and pandas, which is what I needed. Then there’s a lot of data manipulation examples. An outstanding book in the marketplace for this subject area.

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