JupyterLab and Jupyter Notebook can display HTML-embedded images in notebook documents. You can use
the IPython.display.HTML class to structure these images into a basic image gallery.
The notebook below defines a gallery() function that accepts a list of image URLs, local image
| FROM jupyter/datascience-notebook:eb149a8c333a |
IPython's display() function can return a DisplayHandle object. You can use a DisplayHandle to update the output of one cell from any other cell in a Jupyter Notebook.
The display_handle.ipynb notebook below calls display(display_id=True) to get a display handle instance. It then uses the DisplayHandle.display method to show some initial, static Markdown. Later, in a different cell, it calls DisplayHandle.update in a loop to show a range of emoji characters.
IPython shell assignment (the ! operator) evaluates a command using the local shell (e.g., bash) and returns a string list (IPython.utils.text.SList). An SList is a list-like object containing "chunks" of stdout and stderr, properties for accessing those chunks in different forms, and convenience methods for operating on them.
The SList.ipynb notebook below uses SList properties to access the output of a shell command as a list-like of strings, a newline-separated string, a space-separated string, and a list of pathlib.Path objects. The notebook then uses the SList.fields() and SList.grep() methods to extract columns from and search command output.
You can use nbconvert to execute a notebook from the command line (aka headlessly) and store the results in a new notebook file, an HTML file, a PDF, etc. Tools based on nbconvert, like papermill and nbflow, take this capability a step further and let you easily parameterize and chain notebooks.
The command below executes the notebook named run_me.ipynb and outputs a new notebook file named you_ran_me.ipynb with all cell outputs captured. nbconvert executes the run_me.ipynb notebook using the kernel the notebook document declares in its metadata, python3 in this example.
| FROM jupyter/all-spark-notebook:8ccdfc1da8d5 |
Many modern web browsers provide a speech synthesis API for JavaScript. You can write and invoke a function to have your notebook speak when it finishes executing certain cells, whether you're running it in JupyterLab (>=0.34) or classic Jupyter Notebook.
def speak(text):
from IPython.display import Javascript as js, clear_output
# Escape single quotes