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an open-source Python package for IAM scenario analysis and visualization
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pyam: a Python toolkit for Integrated Assessment Modeling

Documentation: Questions? Start a discussion on our listserv

Overview and scope

The pyam package provides a range of diagnostic tools and functions for analyzing and working with IAMC-format timeseries data.


  • Summary of models, scenarios, variables, and regions included in a snapshot.
  • Display of timeseries data as pandas.DataFrame with IAMC-specific filtering options.
  • Simple visualization and plotting functions.
  • Diagnostic checks for non-reported variables or timeseries data to identify outliers and potential reporting issues.
  • Categorization of scenarios according to timeseries data or meta-identifiers for further analysis.

The package can be used with timeseries data that follows the data template convention of the Integrated Assessment Modeling Consortium (IAMC). An illustrative example is shown below; see for more information.

model scenario region variable unit 2005 2010 2015
MESSAGE V.4 AMPERE3-Base World Primary Energy EJ/y 454.5 479.6 ...
... ... ... ... ... ... ... ...


A comprehensive tutorial for the basic functions is included in the first tutorial using a partial snapshot of the IPCC AR5 scenario database.


The documentation pages can be built locally. See the instruction in doc/README.


This package was developed and is currently maintained by Matthew Gidden (@gidden) and Daniel Huppmann (@danielhuppmann).


Copyright 2017-2018 IIASA Energy Program

The pyam package is licensed under the Apache License, Version 2.0 (the "License"); see LICENSE and NOTICE for details.


For basic instructions see our website.

To install from source after cloning this repository, simply run

pip install -e .


To setup a development environment,

# pyam can be replaced with any other name
# you don't have to specify your python version if you don't want
conda create --name pyam pip python=X.Y.Z
conda activate pyam  # may be source activate pyam or just activate pyam
pip install -e .[tests,docs,deploy]
# install other required packages (e.g. on a Unix like system)
conda install -c conda-forge $(cat requirements.txt)
# by hand also works e.g.
conda install -c conda-forge cartopy geopandas
# if you want to write notebooks
pip install notebook nbconvert jupyter_contrib_nbextensions

To check everything has installed correctly,

pytest tests

All the tests should pass.

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