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. Hands-On Data Analysis with PandasSecond Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization 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 Hands-On Data Analysis with PandasSecond Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization Although it is a tick book, the explanations are very concise and the chapters are dynamic. Hands-On Data Analysis with PandasSecond 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. Hands-On Data Analysis with PandasSecond Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization 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. Hands-On Data Analysis with PandasSecond Edition: A Python data science handbook for data collection, wrangling, analysis, and visualization
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