Learning Pandas, Second Edition

Michael Heydt

ISBN 10: 1787123138 / ISBN 13: 9781787123137

Editore: Packt Publishing, 2017

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Codice articolo 9781787123137

Dati bibliografici

• Titolo: Learning Pandas, Second Edition
• Editore: Packt Publishing
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 1787123138
• ISBN 13: 9781787123137
• Rilegatura: Taschenbuch
• Edizione: seconda edizione
• Condizione: Neu
• Dimensioni: 235 L x 191 A x 25 P (millimetri) 235 millimetri di larghezza per 191 millimetri di altezza per 25 millimetri di profondità
• Peso: 827 grammi

Descrizione articolo

Riassunto

Key Features

• Get comfortable using pandas and Python as an effective data exploration and analysis tool
• Explore pandas through a framework of data analysis, with an explanation of how pandas is well suited for the various stages in a data analysis process
• A comprehensive guide to pandas with many of clear and practical examples to help you get up and using pandas

Book Description

You will learn how to use pandas to perform data analysis in Python. You will start with an overview of data analysis and iteratively progress from modeling data, to accessing data from remote sources, performing numeric and statistical analysis, through indexing and performing aggregate analysis, and finally to visualizing statistical data and applying pandas to finance.

With the knowledge you gain from this book, you will quickly learn pandas and how it can empower you in the exciting world of data manipulation, analysis and science.

What you will learn

• Understand how data analysts and scientists think about of the processes of gathering and understanding data
• Learn how pandas can be used to support the end-to-end process of data analysis
• Use pandas Series and DataFrame objects to represent single and multivariate data
• Slicing and dicing data with pandas, as well as combining, grouping, and aggregating data from multiple sources
• How to access data from external sources such as files, databases, and web services
• Represent and manipulate time-series data and the many of the intricacies involved with this type of data
• How to visualize statistical information
• How to use pandas to solve several common data