3.749,00 EGP
Description
Price: [price_with_discount]
(as of [price_update_date] – Details)
[ad_1]
[ad_2]
Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python
Buri Tec is a specialized technology website that offers comprehensive insights into the world of technology.
Easy data cooking with Pandas
This book is a very useful tool for all those developers, engineers or data scientists who are starting out in data management with Python. It is a book that you should not leave on the shelf, but on your work table. It takes you through all the options that Pandas offers for data management: data selection, file access, visualization, transformations, date management, time series, etc. And all of this with a clear, direct and practical approach.Totally recommended.
One of my best buy this year.
Great book. The only book you need if you want to learn how to use pandas.
Great Resource for Beginners
I have been working with some financial clients who use a lot of python and Pandas. I havne’t had much experience with it, so this book was a great guide to getting started.I highly recommend if youre not familiar with Pandas to pick this book up.
Essential Reading for Pandas
This book is great and exactly what I needed to get over a technological hump. Highly recommended
Very comprehensive
This book is ideal for anyone who work with data regularly.
Concise Examples
Pandas Cookbook Third Edition is a step by step instructional resource for the novice data scientist and developer seeking to perform single computing, in-line memoryAnalysis of multidimensional structured data using the pandas library in Python. Authors, William Ayd and Matthew Harrison joined forces to develop a masterful resource of 11 chapters which snowball from simple pandas series, dataframes and indexing of small arrays or matrices to advanced statistical algorithms and complex groupings of data. Ayd and Harrison cut right to the chase of providing simple easy to follow examples to follow along for the novice with easy to understand code no longer than 10 lines; driving home each concept or available panda tool for accomplishing the function at hand. The authors do not reveal all things concerning pandas origins or background in the beginning of the book but rather jumps into the examples, slowly revealing more concerning pandas with the progression of each chapter.Chapter 1 through Chapter 5 start with the introduction of panda series, dataframes, indexes, masking and slicing of array values. Series are just a single set or array of numbers, dates or string values which can be indexed, searched or manipulated. DataFrames build upon the series concept and contain a multiple dimensional array of values in a matrix format that can be assigned to a variable and referenced in memory for analysis, manipulating and plotting. Ayd and Harrison introduce pandas data types, selections and assignments, I/O functionality of pandas and eventually algorithms in chapter 5. If the reader is following along using the pandas library in Python, it helps to follow the recipes in order to understand the more advanced concepts in chapter 5 and beyond. I was pleased to see that pandas can read and write to and from multiple formats like CSV, Excel, SQL, Apache, JSON, HTML to name a few.Chapter 6 through 9 present advanced pandas functionality such as plotting data to visualizations, Reshaping dataframes, Grouping dataframes, and temporal data types and algorithms. I was glad to see the clarity of the built in pandas plot libraries in producing standard visuals such as lines, bar, pie, scatter and density graphs produced from series and dataframe plots. These plots are produced from aggregated and unaggregated data. Greater visualizations require installing the PyQt5 library which the backend method Matplotlib uses. Python library, scipy can be installed when desiring to smooth out data with more advance graphing with a Kernel Density Estimate plot.Adding specialized arguments to the python plot commands can produce the series, labels, titles and graphic colors for visualizations. The Group By and Concatenation features introduced allow for complex data manipulation of dataframes.Chapter 10 and 11 uncover common mistakes that developers make when applying pandas code into a production environment and drive home the use of built in methods and functionality that will aid in performance for example, using a vectorized function to calculate a sum rather than a loop which can utilize more performance resources. Chapter 11 gives a simplified list of the diverse Python libraries and tools like NumPy, DuckDB, Apache Arrow, XGBoost and a host of others to accommodate more advanced algorithms. Each chapter ends with a discussion link and QR code for developers to learn more and converse more in depth with fellow pandas developers on Discord. I found this material clear and concise in addition to being easy to follow.
It is an essential resource for anyone looking to deepen their knowledge of the pandas library
With a structured and hands-on approach, this book effectively bridges the gap between pandasâ extensive official documentation and real-world data manipulation challenges.One of the bookâs standout features is its emphasis on practical, example-driven learning. Rather than just explaining pandas functions in isolation, it guides readers through common data analysis workflows, demonstrating how to combine multiple pandas operations to achieve meaningful results. This approach makes it feel like youâre watching an expert at work, breaking down complex problems into digestible steps.A particular highlight is the bookâs focus on idiomatic pandas codingâhelping readers write efficient, readable, and scalable code. The inclusion of performance tips and an exploration of the pandas ecosystem adds even more depth, making it a valuable reference beyond just the basics.That said, this book is best suited for readers who already have some familiarity with Python and are comfortable working with structured data. While it covers foundational topics, it moves quickly into intermediate and advanced techniques, making it a great choice for those looking to refine their pandas skills rather than absolute beginners.
Great resource
Pandas cookbook is am excellent resource for those looking to become fluent in Python with pandas. It’s a very thorough text and provides ample examples. You’ll leave fully understanding how to manipulate datasets for analytical purposes.
Dieses Buch erklärt sehr ausführlich und anschaulich alle relevanten Tipps und Hinweise, die bei der Benutzung der Pandas Bibliothek entscheidend sind.Nach dem Lesen dieses Buchs ist man gerüstet für den täglichen Einsatz der Pandas Bibliothek im Beruf und Alltag.
Being a python developer, data wrangling using pandas is always a fun and this book offers to view all the hidden gems of pandas 2.x and enlightening readers with all the best ways to use the feat available. And and is an expert bringing all with his precise writing.