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Python programming for data analysis

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Python programming for data analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Python programming for data analysisby Jose Unpingco.
Author:	Unpingco, Jose.
Published:	Cham :Springer International Publishing :2021.
Description:	xii, 263 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Python (Computer program language)
Online resource:	https://doi.org/10.1007/978-3-030-68952-0
ISBN:	9783030689520$q(electronic bk.)

Python programming for data analysis
Unpingco, Jose.

Python programming for data analysis[electronic resource] /by Jose Unpingco. - Cham :Springer International Publishing :2021. - xii, 263 p. :ill., digital ;24 cm.

Introduction -- Basic Language -- Basic Data Structures -- Basic Programming -- File Input/Output -- Dealing with Errors -- Power Python Features to Master -- Advanced Language Features -- Using modules -- Object oriented programming -- Debugging from Python -- Using Numpy - Numerical Arrays in Python -- Data Visualization Using Python -- Bokeh for Web-based Visualization -- Getting Started with Pandas -- Some Useful Python-Fu -- Conclusion.

This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.

ISBN: 9783030689520$q(electronic bk.)

Standard No.: 10.1007/978-3-030-68952-0doiSubjects--Topical Terms:

215247
Python (Computer program language)

LC Class. No.: QA76.73.P98 / U575 2021

Dewey Class. No.: 005.133

Python programming for data analysis
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505 0 $a Introduction -- Basic Language -- Basic Data Structures -- Basic Programming -- File Input/Output -- Dealing with Errors -- Power Python Features to Master -- Advanced Language Features -- Using modules -- Object oriented programming -- Debugging from Python -- Using Numpy - Numerical Arrays in Python -- Data Visualization Using Python -- Bokeh for Web-based Visualization -- Getting Started with Pandas -- Some Useful Python-Fu -- Conclusion.
520 $a This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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