Testing and Upgrading New Packages

It is helpful to test package additions and upgrades for yourself before they are installed for all users. You can make sure the change behaves as you think it should, and does not break anything else. Once tested, request that the change by installed for all users by by creating a new issue in github,contacting cirriculum support staff, or creating a new pull request. Ultimately, thoroughly testing changes locally and submitting a pull request will result in the software being rolled out to everyone much faster.

Install a python package in your notebook

When testing a notebook with new version of the package, add the following line to a cell at the beginning of your notebook.

!pip install --upgrade packagename==version

You can then execute this cell every time you run the notebook. This will ensure you have the version you think you have when running your code.

To avoid complicated errors, make sure you always specify a version. You can find the latest version by searching on pypi.org.

Find current version of a python package

To find the current version of a particular installed package, you can run the following in a notebook.

!pip list | grep <name-of-package>

This should show you the particular package you are interested in and its current version.

Install/Update a R package in your RStudio

When the required version of package is missing in the R Studio, Try the following command to check whether the default installation repo contains the package (and the version) required.

install.packages("packagename")

This should install the particular package you are interested in and its latest version. You can find the latest version of a R package by searching on CRAN.

Find current version of a R package ===============================

To find the current version of a particular installed package, you can run the following in RStudio.

packageVersion("<name-of-package>")

This should show you the particular package you are interested in and its current version.

Tips for Upgrading Package

  • Conda can take an extremely long time to resolve version dependency conflicts, if they are resolvable at all. When upgrading Python versions or a core package that is used by many other packages, such as requests, clean out or upgrade old packages to minimize the number of dependency conflicts.