# Python Package - ChainLadder
*Source: [casact/chainladder-python: Actuarial reserving in Python](https://github.com/casact/chainladder-python)*
Docs: [Welcome to Chainladder — Reserving in Python](https://chainladder-python.readthedocs.io/en/latest/intro.html)
## chainladder - Property and Casualty Loss Reserving in Python
This package gets inspiration from the popular [R ChainLadder package](https://github.com/mages/ChainLadder).
This package strives to be minimalistic in needing its own API. Think in [pandas](https://pandas.pydata.org/) for data manipulation and [scikit-learn](https://scikit-learn.org/latest/index.html) for model construction.
An actuary already versed in these tools will pick up this package with ease. Save your mental energy for actuarial work.
## Documentation
Please visit the [Documentation](https://chainladder-python.readthedocs.io/en/latest/) page for examples, how-to's, and source code documentation.
## Available Estimators
| Loss Development | Tails Factors | IBNR Models | Adjustments | Workflow |
| --------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
| [Development](https://chainladder-python.readthedocs.io/en/latest/development.html#development) | [TailCurve](https://chainladder-python.readthedocs.io/en/latest/tails.html#tailcurve) | [Chainladder](https://chainladder-python.readthedocs.io/en/latest/methods.html#chainladder) | [BootstrapODPSample](https://chainladder-python.readthedocs.io/en/latest/adjustments.html#bootstrapodpsample) | [VotingChainladder](https://chainladder-python.readthedocs.io/en/latest/workflow.html#votingchainladder) |
| [DevelopmentConstant](https://chainladder-python.readthedocs.io/en/latest/development.html#developmentconstant) | [TailConstant](https://chainladder-python.readthedocs.io/en/latest/tails.html#tailconstant) | [MackChainladder](https://chainladder-python.readthedocs.io/en/latest/methods.html#mackchainladder) | [BerquistSherman](https://chainladder-python.readthedocs.io/en/latest/adjustments.html#berquistsherman) | [Pipeline](https://chainladder-python.readthedocs.io/en/latest/workflow.html#pipeline) |
| [MunichAdjustment](https://chainladder-python.readthedocs.io/en/latest/development.html#munichadjustment) | [TailBondy](https://chainladder-python.readthedocs.io/en/latest/tails.html#tailbondy) | [BornhuettterFerguson](https://chainladder-python.readthedocs.io/en/latest/methods.html#bornhuetterferguson) | [ParallelogramOLF](https://chainladder-python.readthedocs.io/en/latest/adjustments.html#parallelogramolf) | [GridSearch](https://chainladder-python.readthedocs.io/en/latest/workflow.html#gridsearch) |
| [ClarkLDF](https://chainladder-python.readthedocs.io/en/latest/development.html#clarkldf) | [TailClark](https://chainladder-python.readthedocs.io/en/latest/tails.html#tailclark) | [Benktander](https://chainladder-python.readthedocs.io/en/latest/methods.html#benktander) | [Trend](https://chainladder-python.readthedocs.io/en/latest/adjustments.html#trend) | |
| [IncrementalAdditive](https://chainladder-python.readthedocs.io/en/latest/development.html#incrementaladditive) | | [CapeCod](https://chainladder-python.readthedocs.io/en/latest/methods.html#capecod) | | |
| [CaseOutstanding](https://chainladder-python.readthedocs.io/en/latest/development.html#caseoutstanding) | | | | |
| [TweedieGLM](https://chainladder-python.readthedocs.io/en/latest/development.html#tweedieglm) | | | | |
| [DevelopmentML](https://chainladder-python.readthedocs.io/en/latest/development.html#developmentml) | | | | |
| [BarnettZehnwirth](https://chainladder-python.readthedocs.io/en/latest/development.html#barnettzehnwirth) | | | | |
## Getting Started Tutorials
Tutorial notebooks are available for download [here](https://github.com/casact/chainladder-python/tree/latest/docs/tutorials).
- [Working with Triangles](https://chainladder-python.readthedocs.io/en/latest/tutorials/triangle-tutorial.html)
- [Selecting Development Patterns](https://chainladder-python.readthedocs.io/en/latest/tutorials/development-tutorial.html)
- [Extending Development Patterns with Tails](https://chainladder-python.readthedocs.io/en/latest/tutorials/tail-tutorial.html)
- [Applying Deterministic Methods](https://chainladder-python.readthedocs.io/en/latest/tutorials/deterministic-tutorial.html)
- [Applying Stochastic Methods](https://chainladder-python.readthedocs.io/en/latest/tutorials/stochastic-tutorial.html)
- [Large Datasets](https://chainladder-python.readthedocs.io/en/latest/tutorials/large-datasets.html)
## Installation
To install using pip: `pip install chainladder`
To install using conda: `conda install -c conda-forge chainladder`
Alternatively for pre-release functionality, install directly from github: `pip install git+https://github.com/casact/chainladder-python/`
Note: This package requires Python>=3.5 pandas 0.23.0 and later, sparse 0.9 and later, scikit-learn 0.23.0 and later.
***
#### Related
- [[Development]]
- [[R Package - ChainLadder]]
- [[2-Areas/MOCs/Python]]
- [[Actuarial Science]]
- [[CAS - Casualty Actuarial Society]]
*Backlinks:*
```dataview
list from [[Python Package - ChainLadder]] AND -"Changelog"
```