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BL
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| Record Number
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855867
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| Main Entry
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Petrou, Theodore
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| Title & Author
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Pandas cookbook : : recipes for scientific computing, time series analysis and data visualization using Python /\ Theodore Petrou.
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| Publication Statement
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Birmingham, UK :: Packt Publishing,, 2017.
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| Page. NO
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1 online resource (xv, 510 pages) :: illustrations
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| ISBN
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1784393347
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: 9781784393342
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1784393878
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9781784393878
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| Contents
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Pandas foundations -- Dissecting the anatomy of a DataFrame -- Accessing the main DataFrame components -- Understanding data types -- Selecting a single column of data as a series -- Calling series methods -- Working with operators on a series -- Chaining series methods together -- Making the index meaningful -- Renaming row and column names -- Creating and deleting columns -- Essential Chapter 2: DataFrame operations -- Selecting multiple DataFrame columns -- Selecting columns with methods -- Ordering column names sensibly -- Operating on the entire DataFrame -- Chaining DataFrame methds together -- Working with operators on a DataFrame -- Comparing missing values -- Transposing the direction of a DataFrame operation -- Determining college campus diversity -- Chapter 3: Beginning data analysis -- Developing a data analysis routine.
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| Abstract
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Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This 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.
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| Subject
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Data mining.
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Electronic data processing.
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Information visualization.
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Machine learning.
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Management-- Data processing.
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| Subject
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Python (Computer program language)
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COMPUTERS-- Data Processing.
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COMPUTERS-- Mathematical Statistical Software.
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COMPUTERS-- Programming Languages-- Python.
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| Subject
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Data mining.
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| Subject
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Electronic data processing.
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| Subject
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Information visualization.
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| Subject
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Machine learning.
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| Subject
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Management-- Data processing.
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| Subject
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Python (Computer program language)
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| Dewey Classification
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005.133
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| LC Classification
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HD30.25
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QA76.73.P98.P487 2017eb
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