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pymort

https://github.com/actuarialopensource/pymort

Science Score: 26.0%

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    Low similarity (9.8%) to scientific vocabulary

Keywords

pandas python
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: actuarialopensource
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 75.4 MB
Statistics
  • Stars: 13
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 1
Topics
pandas python
Created over 4 years ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

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.

```py from pymort import MortXML

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 computer

xmlfrompath = MortXML.from_path("t3282.xml")

you can load from raw xml text

xmlstr = Path("t3282.xml").readtext() xmlfromstr = 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.

```py from pymort import MortXML

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 table

xml.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.

```py select = MortXML.fromid(3265).Tables[0].Values.unstack().values ultimate = MortXML.fromid(3265).Tables[1].Values.unstack().values

select.shape # (78, 25) ages from 18 to 95, duration from 1 to 25 ultimate.shape # (103,) is age 18 to 120

Be careful when indexing into these, ultimate[0] is the rate at age 18!

```

Owner

  • Name: Actuarial Open Source Community
  • Login: actuarialopensource
  • Kind: organization
  • Email: support@actuarialopensource.org
  • Location: United States of America

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Dependencies

.github/workflows/pytest.yml actions
  • abatilo/actions-poetry v2 composite
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
  • codecov/codecov-action v3 composite
poetry.lock pypi
  • appnope 0.1.4
  • asttokens 2.4.1
  • atomicwrites 1.4.1
  • attrs 23.2.0
  • cffi 1.16.0
  • colorama 0.4.6
  • comm 0.2.2
  • coverage 7.4.4
  • debugpy 1.8.1
  • decorator 5.1.1
  • exceptiongroup 1.2.0
  • executing 2.0.1
  • importlib-metadata 7.1.0
  • ipykernel 6.29.3
  • ipython 8.18.1
  • jedi 0.19.1
  • jupyter-client 8.6.1
  • jupyter-core 5.7.2
  • matplotlib-inline 0.1.6
  • more-itertools 10.2.0
  • nest-asyncio 1.6.0
  • numpy 1.26.4
  • packaging 24.0
  • pandas 2.2.1
  • parso 0.8.3
  • pexpect 4.9.0
  • platformdirs 4.2.0
  • pluggy 0.13.1
  • prompt-toolkit 3.0.43
  • psutil 5.9.8
  • ptyprocess 0.7.0
  • pure-eval 0.2.2
  • py 1.11.0
  • pycparser 2.21
  • pygments 2.17.2
  • pytest 5.4.3
  • pytest-cov 4.1.0
  • python-dateutil 2.9.0.post0
  • pytz 2024.1
  • pywin32 306
  • pyzmq 25.1.2
  • six 1.16.0
  • stack-data 0.6.3
  • tomli 2.0.1
  • tornado 6.4
  • traitlets 5.14.2
  • typing-extensions 4.10.0
  • tzdata 2024.1
  • wcwidth 0.2.13
  • zipp 3.18.1
pyproject.toml pypi
  • ipykernel ^6.4.1 develop
  • pytest ^5.2 develop
  • pytest-cov ^4.0.0 develop
  • pandas >=1.3.4
  • python >=3.9,<4.0