Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization Using Python
Synopsis "Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization Using Python"
Key FeaturesUse the power of pandas to solve most complex scientific computing problems with easeLeverage fast, robust data structures in pandas to gain useful insights from your dataPractical, easy to implement recipes for quick solutions to common problems in data using pandasBook DescriptionThis book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way.The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter.Many advanced recipes combine several different features across the pandas library to generate results.What you will learnMaster the fundamentals of pandas to quickly begin exploring any datasetIsolate any subset of data by properly selecting and querying the dataSplit data into independent groups before applying aggregations and transformations to each groupRestructure data into tidy form to make data analysis and visualization easierPrepare real-world messy datasets for machine learningCombine and merge data from different sources through pandas SQL-like operationsUtilize pandas unparalleled time series functionalityCreate beautiful and insightful visualizations through pandas direct hooks to Matplotlib and SeabornAbout the AuthorTheodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items. Ted received his masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.Table of ContentsPandas FoundationsEssential DataFrame OperationsBeginning Data AnalysisSelecting Subsets of DataBoolean IndexingIndex AlignmentGrouping for Aggregation, Filtration and TransformationRestructuring Data into Tidy FormJoining multiple pandas objectsTime SeriesVisualization
