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Matplotlib-The Power of Plots

Background

This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.

These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.

The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.

About

This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.

Topics

bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn

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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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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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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); }
})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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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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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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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); }
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var __m = "*";
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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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, 'i');
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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Matplotlib-The Power of Plots
Background
This respository apply a Python Matplotlib to visualize a real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego. Pymaceuticals specializes in anti-cancer pharmaceuticals. In its most recent efforts, it began screening for potential treatments for squamous cell carcinoma (SCC), a commonly occurring form of skin cancer.
These analysis used a complete data from their most recent animal study in two datasets in CSV format. Data set one is Mouse_metadata.csv wich includes 249 mice identified data with SCC tumor growth were treated through a variety of drug regimens, and their Sex, Age_months and Weight (g) identified. The other dataset is Study_results.csv file which includes the results of the study in each columns Mouse I,Timepoint,Tumor Volume (mm3), and Metastatic Sites.
The purpose of this study was to compare the performance of Pymaceuticals' drug of interest, Capomulin, versus the other treatment regimens. The analysis also generated all of the table and figures needed for the technical, and top-level summary report of the study. For this analysis both datasets imported, merged,cleaned and the aggregate data diplayed in to Python Pandas dataframes, visualized in Matplotlib, and other libraries used in order to make a stastical analysis.
About
This respository apply a Python Matplotlib to visualize real-world pharmaceutical data. The data is sourced from Pymaceuticals Inc., a burgeoning pharmaceutical company based out of San Diego.
Topics
bar-chartmatplotlibmatplotlib-pyplotnumpypandaspi-chartplotregressionregression-analysisregression-modelsscatter-plotscipyseabornsklearn
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