SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #55: Charting
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CWPK #55: Charting
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-10-19T16:44:02Z
content: It's Time for Some Pretty Figures and Charts It is time in our Cooking with Python and KBpedia series to investigate some charting options for presenting data or output. One of the remarkable things about Python is the wealth of add-on packages that one may employ, and visualization is no exception. What we will first do in this installment is to investigate some of the leading charting options in Python sufficient for us to make an initial selection. We want nice looking output that is easily configured and fed with selected data. We also want multiple visualization types perhaps to work from the same framework, so that we need not make single choices, but multiple ones for multiple circumstances as our visualization needs unfold. We will next tailor up some datasets for charting. We'd like to see a distribution histogram of our typlogies. We'd like to see the distribution of major components in the system, notably classes, properties, and mappings. We'd like to see a distribution of our notable links (mappings) to external sources. And, we'd like to see the interactive effect of our disjointedness assignments between typologies. The first desires can be met with bar and pie charts. the last with some kind of interaction matrix. (We investigate the actual knowledge graph in the next CWPK installment.) We also want to learn how to take the data as it comes to us to process into a form suitable for visualization. Naturally, since we are generating many of these datasets ourselves, we could alter the initial generating routines in order to more closely match the needs for visualization inputs. However, for now, we will take our existing outputs as is, since that is also a good use case for wrangling wild data. Review of Visualization Options For quite a period, my investigation of Python visualization options had been focused on individual packages. I liked the charting output of options like Seaborn and Bokeh, and knew that Matplotlib and Plotly had close ties with J…
encoded: It’s Time for Some Pretty Figures and Charts It is time in our Cooking with Python and KBpedia series to investigate some charting options for presenting data or output. One of the remarkable things about Python is the wealth of add-on packages that one may employ, and visualization is no exception. What we will first do in this installment is to investigate some of the leading charting options in Python sufficient for us to make an initial selection. We want nice looking output that is easily configured and fed with selected data. We also want multiple visualization types perhaps to work from the same framework, so that we need not make single choices, but multiple ones for multiple circumstances as our visualization needs unfold. We will next tailor up some datasets for charting. We’d like to see a distribution histogram of our typlogies. We’d like to see the distribution of major components in the system, notably classes, properties, and mappings. We’d like to see a distribution of our notable links (mappings) to external sources. And, we’d like to see the interactive effect of our disjointedness assignments between typologies. The first desires can be met with bar and pie charts. the last with some kind of interaction matrix. (We investigate the actual knowledge graph in the next CWPK installment.) We also want to learn how to take the data as it comes to us to process into a form suitable for visualization. Naturally, since we are generating many of these datasets ourselves, we could alter the initial generating routines in order to more closely match the needs for visualization inputs. However, for now, we will take our existing outputs as is, since that is also a good use case for wrangling wild data. Review of Visualization Options For quite a period, my investigation of Python visualization options had been focused on individual packages. I liked the charting output of options like Seaborn and Bokeh, and knew that Matplotlib and Plotly had close ties with J…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: Python
links to: wrangling
links to: Seaborn
links to: Bokeh
links to: Matplotlib
links to: Plotly
links to: JavaScript
links to: visualization toolkits
links to: D3.js
links to: PyViz
links to: tools.html
links to: PyData community
links to: Python visualization landscape
links to: Holoviews
links to: NetworkX
links to: NumPy
links to: installation
links to: CWPK #54
links to: Protégé
links to: pandas
links to: cb1-1
links to: cb1-2
links to: cb1-3
links to: cb1-4
links to: cb1-5
links to: cb1-6
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links to: cb2-1
links to: cb3-1
links to: cb3-2
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links to: cb3-7
links to: cb3-8
links to: cb3-9
links to: cb3-10
links to: cb3-11
links to: cb3-12
links to: cb3-13

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

First diagram is dynamite

Gallery by chart types

Gallery by application

KBpedia

code listing covering the series

direct download

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