Install Python

Python is an extremely flexable language. There are many ways one can install and configure it. However, with Data Analytics, it's hard to beat Anaconda Python as the platform was designed specifically for this type of work. You are more than welcome to use whatever methods suits your work environment best.

The Anaconda Python Install Instruction for Linux are quite good, so there's no need to reiterate their process. However, I will go over the steps I use for an example.

Install Prerequisites

This step is recommended. If you plan on using any of the visual UX tools, ensure you install the Prerequisites.

Begin Installation

After installing the prerequisites, perform the following tasks in the same terminal.

NOTE: Check for the latest version at the bottom on the Install Page. At the time of this writing, it was Anancond3-2020-11. Change the curl link below as needed. Any resent version should suffice.

# change directories to download
cd ~/Downloads

# Ensure you have curl installed
sudo apt update && sudo apt install curl -y

# download the file
curl https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh

# once downloaded, run the installer
bash ~/Downloads/Anaconda3-2020.11-Linux-x86_64.sh

# Install Notes:
#   - Accept the Defaults = yes
#   - Add Anaconda to your path = yes
#   - Close then re-open a terminal

Post Installation

Upon reopening the terminal, you should see (base) in front of your path. If you don not, you can activate it with the following.

# activate base, this will auto-activate base each time you open a terminal
conda config --set auto_activate_base True

# source bashrc file
source ~/.bashrc

# check Python version
python -V

# check path to Python
which python

# the path to Python base should point to
/home/$USER/anaconda3/bin/python

Create Virtual Environment

You can create as many or few virtual environments as you wish. Generally, I create one per project, sometimes more depending on complexity and test requirements.

You can name the virtual environments anything you like. I tend to name it after the project, in this case wsprana.

Additionally, I normally stay one minor version back from the latest Mainstream Python release to help with package compatibility. At the time of this writing it was 3.9.1. Therefore, we'll create a v3.8 virtual environment.

In the terminal perform the following:

# create virtual environment
conda create -n wsprana -y python=3.8

# after the creation, activate the new venv
conda activate wsprana

# update pip, optional, but helps with warnings
python -m pip install --upgrade pip

NOTE: Each time you close and re-open a terminal, you will be set back to the (base) environment (this is by design). You can change this behavior by adding conda activate wsprana to your ~/.bashrc file.

Conclusion

This completes the basic virtual environment installation. You should take a few moments and read through the Conda Documentation to become familiar with basic conda usage.