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DONUT CHART FOR PYTHON

This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.

Overview ⚡

A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.

As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.

Tools ⚡

  • Jupyter
  • Pandas
  • Plotly

Data ⚡

Data originally published in: Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.

Usage ⚡

To use the module

  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)

The donut() function takes the following arguments:

  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.

⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.

Visualization ⚡

SampleViz

Video Walk Through ⚡

DonutChart Function

License ⚡

This module is licensed under the MIT License. Click the LICENSE for more details.

© 2023 Jazmine N

About

This is a Python module that allows you to create a well-presented donut chart using Plotly.

www.youtube.com/watch?v=1ZBD9mI8siw

Topics

chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization

Resources

Readme
MIT license
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

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Packages

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
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0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
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if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Donut_Chart .ipynb
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LICENSE
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README.md
README.md
donutchart.py
donutchart.py
Repository files navigation
 DONUT CHART FOR PYTHON 
This module provides a simple function to create a well-designed donut chart in Python. The function uses Plotly under the hood to create a chart that provides a quick and easy way to view the proportionality of data features.
Overview ⚡
A donut chart is a type of chart that displays data as a ring-shaped chart with a hole in the middle, similar to a pie chart. Some people find donut charts more visually appealing than pie charts (the "Forbidden Chart") as they can provide a clearer view of the data being displayed. However, it's important to use donut charts carefully and avoid using too many segments, as this can make the chart difficult to read and interpret.
As someone who loves donut charts, it's crucial to create a well-organized, visually appealing donut chart that presents the data in an easily understandable way. For instance, if you want to quickly grasp the proportion of different categories within a feature, you can simply use the module.
Tools ⚡
  • Jupyter
  • Pandas
  • Plotly
Data ⚡
Data originally published in:
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. Click here to view.
Usage ⚡
To use the module
  • Download the Donut.py and save it in the same folder.
  • Import the donutchart() function and provide the necessary arguments.
importpandasaspdfromdonutchartimportdonut# Create a sample datasetdata=pd.Series(['Dog', 'Dog', 'Cat', 'Bunny','Bunny','Bunny', 'Cat', 'Dog', 'Dog', 'Dog', 'Dog', 'Bunny', 'Dog', 'Dog'])
title='Distribution of Dog, Cat and Bunny'# Create the donut chartdonut(data=data, title=title, legend=False)
The donut() function takes the following arguments:
  • data: The data to be charted. This should be in a pandas Series format.
  • title: The title of the chart.
  • hole: The size of the hole in the middle of the chart. This ranges from 0 to 1 and defaults to 0.5.
  • legend: Whether to include a legend. Defaults to True.
⚠️ Note that the donut() function is not suitable for use with data that has more than three categories.
Visualization ⚡
SampleViz
Video Walk Through ⚡
DonutChart Function
License ⚡
This module is licensed under the MIT License. Click the LICENSE for more details.
© 2023 Jazmine N


About
This is a Python module that allows you to create a well-presented donut chart using Plotly.
www.youtube.com/watch?v=1ZBD9mI8siw
Topics
chartchartsdata-visualizationdonut-chartdonutchartdoughnut-chartdoughnut-chart-centerviewpandasplotlyvisualization
Resources
Readme
MIT license
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages