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

I'm half way through the book and really enjoying it. Excellent book to start using pandas and other Python libraries for data analysis. Very well written and easy to follow. Great buy. Arrived in good condition.

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.

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.

I have the second and third edition, and they’re both good for reference. I do wish there was more explanation but a good starting point and tips and notes.

If you want to learn python for data analysis and data science this book let's you do it the way I prefer - hands on! You learn it because you do it. Well written.

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.

Instructional, as expected. 5/5 stars.

No doubt the author has knowledge about the subject. He's a respectable professional in the area and the creator of the pandas package. But once more in life, we see that knowing a subject doesn't give you the tools for teaching it. If you, like me, have learned from other resources about python, numpy, pandas, and the rest of the items touched on in the book, then, you occasionally will add some little tip that perhaps you didn't pay attention to before.Also, the way the author explains the different uses of the packages is not very clear. I found myself reading the book and jumping all the time to Google or other sources to clarify some things or for simpler examples that enlightened me to return back to the examples in the book. I would like to rate the author with five stars and the book with less than that. But, it's not possible so I rated it with three stars.