Hands-On Data Analysis with Pandas - Second Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization

This is a killer book for the Python and data wrangling professional, making all other books look like elementary school treatments. I've read 7 Pandas books and 32 Python books that have Pandas sections. Stepanie Molin's is by far the strongest, most detailed, easiest to follow, best-exampled book, and easiest to understand of any of the 39 books read. All other books pale in comparison to this must-read book from Molin.The datasets are intuitive. Not a boring texty book. Instead, lots of example code appears on every single page, illustrating the features. The story and example code flow together, not skipping around or showing disjointed points. The chapters follow your workflow, from data ingest and EDA to data cleaning, data wrangling, visualizataion, and finally to applications.Thorough treatments are given to data cleaning, data wrangling, and data enrichment as separate topics, going into deep details on how to reshape and reindex data frames, how to do proper joins on data frames, left, right, inner, and outer, and how to do many other data cleaning and wrangling steps. For exaple, you'll learn how to set a new index, and why you should do that. And when inserting rows from different dataframes, you can leave yourself a new indicator column that shows you which table added the row. Pandas has many features like this that professionals should know, and Stephanie Molin shows the "how to".Of course there's a GitHub link so you can download the example datasets. Honestly, I'm only up through data wrangling - have not even reached the financial analysis, machine learning, and advanced visualization code. I can hardly wait to work all the examples in person. (As you know, reading is good, but building the code is by far the most effective way to learn.)Thanks Stephanie for devoting the time to making this a wonderful detailed and usable guide on how to use Pandas to solve my customer's problems. What a joy to read and use. This is the first and best book you should buy for Pandas.

This easy to follow book is exactly what I needed at this stage of my learning curve. I love how the author takes the reader through accessing real world data that are messy and in some cases missing. Accessing real world data with APIs is a tool I appreciate leaning and then seeing the shortcomings of the real world data has been what most other books are missing. This book will better allow me to translate the lessons to my own needs. Well done!

Solid Book for those with with intermediate python knowledge

Hands on Data Analysis with Pandas is one of the best books I have read recently. What I like the most about it is the structure: combine chapters explaining the different parts of pandas with chapters that have complete practical examples of analysis. This allows you to see detailed examples about the functionality but also see how they fit in the larger picture of a full analysis.Also, it approaches the teaching of pandas with both a data analyst perspective and a software engineer perspective. To be successful in data science today we need to wear both of those hats, so for someone coming from an analysis background without formal software engineering training, the book helps demystify concepts like virtualenv, simulations, source control, etc.It’s not only about learning Pandas but about using pandas in the right away.

I've bought and returned several pandas books so far, but this book checks all the boxes. It's easy to follow along with in the provided Jupyter notebooks, uses examples with real datasets (my favourite was the earthquake data), and taught me some software engineering concepts to write better code as a data scientist. The two chapters on plotting were also fantastic! A must buy for aspiring data scientists!

it breaks it down and explains the why behind it

As an analyst in a cyber security operation center role, I live and breathe data. The more, the better. Pandas is a natural fit for organizing, navigating and analyzing diverse data at scale. However, if you’ve ever tried leveraging Pandas to do this, you quickly realize how difficult it can be. The documentation is ambiguous and due to the diversity of the how others leverage Pandas it’s difficult to find scenarios and code examples that line up with your needs. Enter “Hands-On Data Analysis with Pandas”. Molin does a great job at organizing and presenting all you need to get started leveraging both pandas and Jupyter notebooks. She also clearly and concisely explains the fundamental of machine learning and statistical analysis. Her mastery is in both understanding the discipline and the libraries used to get the work done. I not only reference the book to help with organizing and analyzing my data, I also reference the book to support my visualization and plotting requirements. There aren’t many books out there that are both this comprehensive and good at teaching a very complex subject. If you are in cyber security you need this book.

Just barely OK. If you need to learn Pandas, get Wes McKinnon's book. This one is too superficial to help you get past the starting line.

This is a good pandas reference. I got the kindle edition and it is very convenient when you are on the move and you have to work!

Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition by Stefanie Molin is one of the best books for data science beginners, data analysts, and Python! This is my reference book and for sure I would like to recommend it for anyone interested in these topics.

Livello intermedio, teoria e ottimi esempi, il capitolo sulla classe per l'analisi dati di borsa è veramente ben fatto. Uso proficuo del chaining e delle classi.Uno dei migliori testi su python e le librerie per l'analisi dei dati. Consiglio

I have been able to make some experiences in the field of Data Science and wanted to have a book that approaches the topic in a structured way and also ages well, i.e. places a great emphasis on principles. This book could not meet both expectations. On one hand, the structure is very confusing, a red thread is not recognizable. There is always jumping between topics and at the end of a chapter you don't really know what you were supposed to learn. On the other hand, little emphasis is placed on principles and instead the author loses herself in mundane details. The whole thing is crowned by the fact that already two months after the publication of the book, the code from the associated GitHub repo no longer works and is also obviously not updated.

Although it is a tick book, the explanations are very concise and the chapters are dynamic.