Jupyter-lab is the standard tool for modern reproducible data science in Python. In this short class you will learn how to setup JupyterLab, execute code; control the Jupyter Kernel; write Markdown, and structure Jupyter notebooks.
This is a general introduction to Jupyter and is useful for any data science course using Linux, Mac or Windows.
Requirements
- It is recommended that at - a minimum - down and install a Anaconda 3 distribution https://www.anaconda.com/
- The default base conda environment is sufficient to run the notebooks in this class.
- For those wishing to use the same version of Jupyter and Python, the class is also provided with a conda virtual environment:
hds_code. To install the dependences run the following in a terminal:
conda env create -f binder/environment.yml
conda activate hds_code
- Jupyter lab theme
- Directory and file navigation
- Creating a new Jupyter notebook (.ipynb)
- Presentation mode and single document mode
- Adding line numbers and a ruler to code cells
- Using the built in bash terminal
- Code cells versus markdown cells
- Advice about using keyboard shortcuts
- Running individual cells versus running multiple cells
- Execution order matters
- Resetting a kernel
- Interrupting a kernel
- Setting a cell to markdown mode
- Titles and subtitles
- Horizontal lines
- Formatting text as code in markdown
- Bold text and italic text
- Bullet points and numbers lists
- Including LaTeX mathematical expressions.
- Including images
- Including hypertext links.