Local install instructions

The course uses Python 3 and some data analysis packages such as Numpy, Pandas, scikit-learn, and matplotlib.

Install Miniconda

This step is only necessary if you don't have conda installed already:

• download the Miniconda installer for your operating system (Windows, MacOSX or Linux) here
• run the installer following the instructions here depending on your operating system.

Create conda environment

# Clone this repo
git clone https://github.com/ArturoAmorQ/PSL-intensive-week
cd PSL-intensive-week
# Create a conda environment with the required packages for this tutorial:
conda env create -f environment.yml

Check your install

To make sure you have all the necessary packages installed, we strongly recommend you to execute the check_env.py script located at the root of this repository:

# Activate your conda environment
conda activate PSL_week
python check_env.py

Make sure that there is no FAIL in the output when running the check_env.py script, i.e. that its output looks similar to this:

Using python in /home/username/miniconda3/envs/PSL_week
3.9.1 | packaged by conda-forge | (default, Jan 10 2021, 02:55:42)
[GCC 9.3.0]

[ OK ] numpy version 1.19.5
[ OK ] scipy version 1.6.0
[ OK ] matplotlib version 3.3.3
[ OK ] sklearn version 0.24.0
[ OK ] pandas version 1.2.0
[ OK ] seaborn version 0.11.1
[ OK ] notebook version 6.2.0
[ OK ] plotly version 4.14.3

Run Jupyter notebooks locally

# Activate your conda environment
conda activate PSL_week
jupyter notebook