Install Python Packages

This page describes how to install a Python package in a CoCalc project.

CoCalc already includes hundreds of packages for several Python environments. Check first if the lib you’re looking for is already installed!

Related: Custom Jupyter Kernel, Setup Jupyter Extensions and Install R Packages.

Install requests

If a package may have general use but is not already installed in CoCalc, please open a support request to tell us to install it globally for everyone. Uncomplicated install requests are typically handled within 1 business day for paying customers. Install will happen faster if you include as much as possible of the following information:

  • Which Python environment?

  • Which language version: 2 or 3?

  • A link to the package website

  • Special requirements and dependencies to build & install

  • A short but complete example, such that we can verify that we properly installed the software. This example might be included in internal tests, to make sure future updates do not break that library.

Python “user” installs


Your project must have the “Internet access” upgrade in order to download software from a remote repository (e.g. PyPI or Anaconda) to your project. Installing a Python package will require you to add a license or add upgrades so that your project has internet access.

A way to work around such a blocked internet access is to upload the package files into your project.

You can install additional packages yourself, but only at user-permission level. CoCalc accounts do not have superuser (root) privileges. Software must be installed into user-writeable parts of the filesystem, which are in /home/user (check the value of $HOME).


In a nutshell: a CoCalc project is a Linux user account under the username user. Therefore, installing software and libraries should usually be done in ~/.local (i.e. /home/user/.local), which is the canonical location for user installs. Furthermore, in case the documentation mentions to specify a custom “prefix” path, set this to ~/.local. Executables will install into ~/.local/bin and will work right away, because projects already include that path in their $PATH variable.

Install location and sys.path


In the case of Python 2, $HOME/.local/lib/python2.7/site-packages/ will contain the package you’ve installed. Similarly, this path will contain python3.8 for a Python 3.8 executable.

In case your Python environment can’t find the package, you might have to add your ~/.local/... directory dynamically during runtime like that:

import sys, os
sys.path.insert(0, os.path.expanduser('~/.local/lib/python2.7/site-packages'))

Make sure, the path is correct. I.e. for Python 3 this could be one of python3.7, python3.8


Pip is the “Python package manager”. It installs packages hosted at

If your package can be installed via pip, then run in a CoCalc Terminal file:

  • Python2: pip2 install --user [package-name]

  • Python3: pip3 install --user [package-name]


Regarding Python 2 vs. Python 3:

  • Python 2: use pip2 and python2/ipython2.

  • Python 3: use pip3 and python3/ipython3pip and python should default to these variants.

If you’ve uploaded a zip/wheel file, change the [package-name] to the actual filename.

pip install directly from git repository

Suppose there is a GitHub repository for a python 3 package at (There should be a file at the top-level directory of the repo.) The simplest way to install directly from GitHub via pip is this:

pip3 install --user git+

This approach works with any remote git repository for which you have the necessary access.

If your package is in a folder inside your project (e.g., you uploaded it) which includes a file, you can do either python install --user or pip install --user --upgrade ./

(Some setup instructions alternatively mention python install --home)

If pip requires that any external dependencies be downloaded, then your project must have internet access.


You can avoid the need for --user flags if you work inside a Python virtual environment. See Virtualenv for more information.


A special case is SageMath, which is a fully integrated environment built on top of Python.

To install a python package to be used from Sage, first open a CoCalc Linux Terminal. Then run the command:

sage --pip install <package_name>

After this, you will be able to use the python package from within Sage in any of these settings:

  • Command-line Sage.

  • Sage Worksheets. After installing the package, you will have to restart the Sage worksheet server under project Settings, or restart the project.

  • Jupyter notebook running the Sage kernel. The version of Sage in the Jupyter kernel selected must match the version of Sage used on the command line to install the package. Restart the Jupyter kernel to pick up the newly installed package.

Encapsulated PIP w/ Jupyter Kernel

Here, we explain how to setup your own encapsulated Python environment using pipenv. You can either choose to use the global packages, or – as we do here – tell it to only have explicitly installed ones on board.

We start with an empty directory in our $HOME:

~$ cd
~$ mkdir my-special-env
~$ cd my-special-env

Then we run pipenv install without site packages and specifying the python interpreter to use (Note: by default it might pick up pypy3, which is not a good idea in general). Install pandas below version 1.2 and the juypter kernel:

~/my-special-env$ pipenv install --python /usr/bin/python3 --no-site-packages ipykernel 'pandas<1.2'
[output is abbreviated ...]
Creating a virtualenv for this project...
✔ Successfully created virtual environment!
Installing ipykernel...
✔ Installation Succeeded
Installing pandas<1.2...
✔ Installation Succeeded
✔ Success!
Updated Pipfile.lock (4eda65)!
Installing dependencies from Pipfile.lock (4eda65)...
  🐍   ▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉▉ 0/0 — 00:00:00
To activate this project's virtualenv, run pipenv shell.

Now, we can launch it and give it a try. Let’s check if Pandas is below version 1.5 and other libs like scipy are not available:

~/my-special-env$ pipenv shell
Launching subshell in virtual environment...
 . /home/user/.local/share/virtualenvs/my-special-env-gNmS0l6R/bin/activate
~/my-special-env$  . /home/user/.local/share/virtualenvs/my-special-env-gNmS0l6R/bin/activate
(my-special-env) ~/my-special-env$ python
Python 3.8.5 (default, Jul 28 2020, 12:59:40)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'scipy'
>>> import pandas
>>> pandas.__version__

Finally, we install the Juypter Kernel. We run ipykernel install and give the kernel a unique name. After opening the file in CoCalc’s editor via open <filename.ipynb> , make sure to run “Kernel” → “Refresh kernel list”, to get the new kernel. Then select it and you’re good to code!

(my-special-env) ~/my-special-env$ python3 -m ipykernel install --user --name=my-special-env
Installed kernelspec my-special-env in /home/user/.local/share/jupyter/kernels/my-special-env
(my-special-env) ~/my-special-env$ open my-special-env.ipynb
creating file 'my-special-env.ipynb'

All in all this gives you a precisely defined environment, outfitted with checksums for all dependencies for reproducibility.

Anaconda Environment

Conda is an alternative packaging system by Anaconda. It is mostly used for Python packages, but it can manage and deliver almost any kind of software.

CoCalc provides a global environment, which you can start by running anaconda2020 in a Linux Terminal or a related kernel in a Jupyter Notebooks. To get going with your own setup for your own CoCalc project, you have to create your own environment and your own kernel.

Install some software into my own Anaconda environment

The task below is to create a custom Anaconda overlay environment called myconda and, just for the sake of explanation,

  1. install “Microsoft’s Open R” (which is an enhanced version of R by Microsoft).

  2. Install the plotly library from PyPI

To get it installed in Anaconda as a user, do this:

  1. Open a terminal.

  2. Type anaconda2020

  3. Type conda create -n myconda -c mro r This creates a new local environment called “myconda” (name it as you wish) with the package “r” as its source coming from the channel “mro” (Microsoft’s Open R). Instead of that, you can add any other anaconda package in that spot. The example from the documentation is biopython, see

  4. When installing, it briefly shows you that it ends up in ~/.conda/envs/myconda/.... in your local files. Now that we have it installed, we can get out of this “root” environment via source deactivate or restart the session. In any case, you are back in the the normal Linux terminal environment.

  5. Now run this: source ~/.conda/envs/myconda/bin/activate myconda Note that myconda is the name specified above, and the prompt switches to (myconda) $. Typing which R shows: /projects/xxx-xxx-xxx/.conda/envs/myconda/bin/R and of course, just running R gives:

    R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
    Copyright (C) 2015 The R Foundation for Statistical Computing
    Platform: x86_64-pc-linux-gnu (64-bit)
    Microsoft R Open 3.2.3
    Default CRAN mirror snapshot taken on 2016-01-01
    The enhanced R distribution from Microsoft
  6. In the very same spirit, you can also run pip installations:

    (myconda)~$ pip install plotly
    Downloading/unpacking plotly
    Successfully installed plotly requests six pytz
    (myconda)~$ python -c 'import plotly; print(plotly)'
    <module 'plotly' from '/projects/20e4a191-73ea-4921-80e9-0a5d792fc511/.local/lib/python2.7/site-packages/plotly/__init__.pyc'>

Note that since I’m still in my own “myconda” overlay environment, the --user switch in pip install wasn’t necessary. (Otherwise, it would be necessary.)

Configure a Jupyter kernel for my custom Anaconda environment

With Anaconda’s conda environment and software manager, you can create custom environments with specific versions of Python, R, and their packages. This is similar to capabilities provided by Python’s environment manager, Virtualenv.

Suppose you want to create a custom Anaconda environment with the mdtraj package and be able to use this environment in a Jupyter notebook. Here’s how:

  1. Follow these steps in a .term file in CoCalc. In the last step, the display name of the new kernel is changed so that it does not duplicate the name of kernel installed by CoCalc:

    ~$ mkdir -p ~/.local/share/jupyter/kernels
    ~$ anaconda2020
    (root) ~$ conda create --name mymdtraj mdtraj
    (root) ~$ source activate mymdtraj
    (mymdtraj) ~$ conda install ipykernel
    (mymdtraj) ~$ source deactivate
    ~$ mv ~/.conda/envs/mymdtraj/share/jupyter/kernels/python3 ~/.local/share/jupyter/kernels/mymdtraj
    ~$ open ~/.local/share/jupyter/kernels/mymdtraj/kernel.json
    ## change display_name from "Python 3" to "My mdtraj" and save the file
  2. Open a new Jupyter notebook in CoCalc.

  3. Click on the Kernel button and look for your new kernel, “My mdtraj”, or whatever you used for display_name in kernel.json. If you don’t see your new kernel, scroll to the bottom of the Kernel menu and click Refresh Kernel List, and your new kernel should appear.

  4. Select the new kernel. You will now be running the environment you created with the conda create command.