E-Books →Pandas Cookbook Practical recipes for scientific computing, time series, and exploratory data analysis using Python

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Key FeaturesThis book targets features in pandas 2.x and beyondPractical, easy to implement recipes for quick solutions to common problems in data using pandasMaster the fundamentals of pandas to quickly begin exploring any datasetBook Description
Unlock the full power of pandas 2.x with this hands-on cookbook, designed for Python developers, data analysts, and data scientists who need fast, efficient solutions for real-world data challenges. This book provides practical, ready-to-use recipes to streamline your workflow. With step-by-step guidance, you'll master data wrangling, visualization, performance optimization, and scalable data analysis using pandas' most powerful features.
From importing and merging large datasets to advanced time series analysis and SQL-like operations, this cookbook equips you with the tools to analyze, manipulate, and visualize data like a pro. Learn how to boost efficiency, optimize memory usage, and seamlessly integrate pandas with NumPy, PyArrow, and databases. This book will help you transform raw data into actionable insights with ease.
What you will learnThe pandas type system and how to best navigate itImport/export DataFrames to/from common data formatsData exploration in pandas through dozens of practice problemsGrouping, aggregation, transformation, reshaping, and filtering dataMerge data from different sources through pandas SQL-like operationsLeverage the robust pandas time series functionality in advanced analysesScale pandas operations to get the most out of your systemThe large ecosystem that pandas can coordinate with and supplementWho this book is for
This book is for Python developers, data scientists, engineers, and analysts. pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic pandas
Table of Contentspandas FoundationsSelection and AssignmentData TypesThe pandas I/O SystemAlgorithms and How to Apply ThemVisualizationReshaping DataFramesGroup ByTemporal Data Types and AlgorithmsGeneral Usage and Performance TipsThe pandas Ecosystem