The full Anaconda bundle contains a huge selection of data science packages (694, to be exact) ready to run.

Recent builds use later Python versions, so you have to use Anaconda v2020.02 or Miniconda v4.9.2-p圓7. Google Colab uses Python 3.7, so we need an Anaconda distribution compiled for that version. However, conda is not preinstalled in the Colab environments! The good news is that you can install it manually for each notebook. While most popular projects offer *.deb packages and pip wheels (both methods officially supported by Google Colab), some are only distributed through conda (for example, OpenMM). Now if the install fails, the script prints the message and continues on, but the Python version for the environment (Python 2.7.16) resets back to the default Miniconda version (Python 3.7.3). Most of the data scientists rely on the Anaconda distribution or, at least, its package manager to install the libraries they need: conda. Last week, if the install failed, the script would simply print a message and continue on. When it has loaded we need to create a new directory for the name of the project: 3. Go to the root directory, right click and choose Open with VS Code: 2. Python has become the most popular language in StackOverflow and one could argue that its success is recently due to data science in general and machine learning in particular. Getting VS code to recognise the new virtual environment. !conda install -yq python = 3.7 your_extra_packages

Environnement conda python version