Data Visualization

Kurt Y. Liu

doi:10.1007/978-3-030-92224-5_4

Data Visualization

Kurt Y. Liu

Engineering Systems & Supply Chain Management

Research output: Chapter in Book/Published conference output › Chapter

Abstract

In this chapter, we demonstrate data visualization in Python with Matplotlib and Seaborn. First, we introduce the components of a Figure, and different ways of creating a Figure and Axes. Then, the methods for customizing and formatting a figure are introduced. Next, we illustrate how to plot common charts using Matplotlib including scatter plot, bar chart, histogram, pie chart, and boxplot. The Seaborn methods for creating informative statistical graphics were demonstrated with two examples. Lastly, we introduce geographic mapping with Basemap.

Original language	English
Title of host publication	Supply Chain Analytics: Concepts, Techniques and Applications
Publisher	Palgrave Macmillan
Chapter	4
Pages	83-111
Edition	1st
ISBN (Electronic)	9783030922245
ISBN (Print)	9783030922238
DOIs	https://doi.org/10.1007/978-3-030-92224-5_4
Publication status	Published - 8 Apr 2022

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10.1007/978-3-030-92224-5_4

Cite this

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title = "Data Visualization",

abstract = "In this chapter, we demonstrate data visualization in Python with Matplotlib and Seaborn. First, we introduce the components of a Figure, and different ways of creating a Figure and Axes. Then, the methods for customizing and formatting a figure are introduced. Next, we illustrate how to plot common charts using Matplotlib including scatter plot, bar chart, histogram, pie chart, and boxplot. The Seaborn methods for creating informative statistical graphics were demonstrated with two examples. Lastly, we introduce geographic mapping with Basemap.",

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AB - In this chapter, we demonstrate data visualization in Python with Matplotlib and Seaborn. First, we introduce the components of a Figure, and different ways of creating a Figure and Axes. Then, the methods for customizing and formatting a figure are introduced. Next, we illustrate how to plot common charts using Matplotlib including scatter plot, bar chart, histogram, pie chart, and boxplot. The Seaborn methods for creating informative statistical graphics were demonstrated with two examples. Lastly, we introduce geographic mapping with Basemap.

UR - https://link.springer.com/chapter/10.1007/978-3-030-92224-5_4

U2 - 10.1007/978-3-030-92224-5_4

DO - 10.1007/978-3-030-92224-5_4

M3 - Chapter

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BT - Supply Chain Analytics: Concepts, Techniques and Applications

PB - Palgrave Macmillan

ER -

Data Visualization

Abstract

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