Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
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Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third 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, and Jupyter 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 Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
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Publisher : O’Reilly Media
Publication date : September 20, 2022
Edition : 3rd
Language : English
Print length : 579 pages
ISBN-10 : 109810403X
ISBN-13 : 978-1098104030
Item Weight : 1.95 pounds
Dimensions : 7 x 1.5 x 9 inches
Best Sellers Rank: #21,685 in Books (See Top 100 in Books) #2 in Data Mining (Books) #6 in Data Processing #11 in Python Programming
Customer Reviews: 4.6 4.6 out of 5 stars (499) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });
Moses –
Data Analysis simplified using Numpy and Pandas.
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.
Amol Joshi –
Excellent Reference book
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.
BURN –
Great reference!
As with all O’reilly books I’ve purchased, great quality and a great reference. Really good to go back to the fundamentals.
Jeff Bales –
Good, but sometimes intense, book.
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.
Hillary D. –
Python
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 !
Giovanni Girelli –
Great book for data analysis with Python.
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.
Mark L. –
Book with Right Python Details
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.
Richard Demcsak –
Great book.
Festus Azibator –
This book is awesome. I will recommend it to anyone seeking to expand their understanding about data wrangling. The author did a good job.