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pymort

pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main

Installation

Install pymort with pip install pymort.

MortXML

If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.

frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)

This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.

autocompletions

Also, mortality rate tables are Pandas DataFrames.

rate table as a dataframe

Accessing mortality rates

For a select and ultimate table we can retrieve rates as follows.

frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values

Usage with tensor libraries

We can get the data from Pandas to NumPy.

select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!

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Resources

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Code of conduct

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17 stars

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
Report repository
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Used by
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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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pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
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Used by
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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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pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
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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" + '

Skip to content
BranchesTags
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codecov
pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
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pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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codecov
pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
About
No description or website provided.
Topics
pandaspython
Resources
Readme
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
17 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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pymort
pymort is a way to retrieve the mortality tables hosted at https://mort.soa.org/. It only hosts the data, an example of a package implementing Lee-Carter models is here - https://github.com/jkoestner/morai/tree/main
Installation
Install pymort with pip install pymort.
MortXML
If you want the full details of any SOA table, you can use the lower level load API. You just need to enter the table ID.
frompymortimportMortXML# load the 2017 Loaded CSO Composite Gender-Blended 20% Male ALB table (tableId = 3282)xml=MortXML.from_id(3282)
# you can load from a file path on your computerxml_from_path=MortXML.from_path("t3282.xml")
# you can load from raw xml textxml_str=Path("t3282.xml").read_text()
xml_from_str=MortXML(xml_str)
This MortXML class is a wrapper around the underlying XML. The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
autocompletions
Also, mortality rate tables are Pandas DataFrames.
rate table as a dataframe
Accessing mortality rates
For a select and ultimate table we can retrieve rates as follows.
frompymortimportMortXML# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ xml=MortXML.from_id(3265)
# This is the select table as a MultiIndex (age/duration) DataFrame.xml.Tables[0].Values# This is the minimum value of the issue age axis on the select tablexml.Tables[0].MetaData.AxisDefs[0].MinScaleValue# This is the ultimate table as a DataFrame with index attained age.xml.Tables[1].Values
Usage with tensor libraries
We can get the data from Pandas to NumPy.
select=MortXML.from_id(3265).Tables[0].Values.unstack().valuesultimate=MortXML.from_id(3265).Tables[1].Values.unstack().valuesselect.shape# (78, 25) ages from 18 to 95, duration from 1 to 25ultimate.shape# (103,) is age 18 to 120# Be careful when indexing into these, ultimate[0] is the rate at age 18!
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