To use respy in the following tutorials, you need the following three components.


The Anaconda distribution is a bundle of compatible Python packages. It also includes conda which is a package manager to install, update, and remove packages. You can also manage environments with conda which are a collection of packages you need for a project.

The installation instructions for multiple platforms can be found here.

Jupyter Lab

Jupyter Lab is an IDE (integrated development environment) for literate programming meaning that the notebook display code and text alongside each other in a pleasant way.

Jupyter Lab can be installed with

$ conda install jupyterlab

Although this tutorial is dedicated to Jupyter notebooks, the same instructions apply to Jupyter Lab which will in the long-run supersede Jupyter notebooks.


The recommended way to install respy is via conda, the standard package manager for scientific Python libraries. With conda available on your path, installing respy is as simple as typing

$ conda config --add channels conda-forge
$ conda install -c opensourceeconomics respy

in a command shell. The whole package repository can be found under

If you want to use different numerical integration methods implemented in respy you also need to additionally install the package chaospy as it is not added automatically as a package dependency.

$ pip install chaospy

As respy relies heavily on pandas, you might also want to install their recommended dependencies to speed up internal calculations done with pd.eval.

conda install -c conda-forge bottleneck numexpr