Jupyter-DataScience

Introduction

Jupyter/datascience includes popular packages for data analysis from the Python, Julia and R communities and also packages are included from its ancestor images jupyter/sci-py notebook, jupyter/r-notebook and jupyter/minimal-notebook.

Some of the packages it includes are,

, , , , , , , , , , , , , , , , , , , , , , , and packages

and for interactive visualizations and plots in Python notebooks.

for visualizing machine learning datasets.

The compiler and base environment.

to support Julia code in Jupyter notebooks.

, , and packages.

Working with jupyter-datascience notebook in HeLx

Step-1:

Launch a jupyter-datascience notebook from HeLx by clicking on “Launch Application” button.

Step-2:

This brings us to the jupyter-lab panel where we can select the environment that we want to work on (Python, Julia, R).

Step-3:

Start working on it. Below code shows loading iris dataset (features, labels) from sklearn package to train/test our machine learning model.