Static Visualizations

Static Visualizations#

Static visualizations should be created in a Jupyter Notebook, saved locally (in JupyterHub), and checked into GitHub. For example, you can save charts as image files (such as PNGs) and commit them to the repository.


# matplotlib or seaborn
import matplotlib.pyplot as plot
import seaborn
plt.savefig("../my-visualization.png")

# altair
import altair as alt
import altair_saver
chart.save("../my-visualization.png")

# plotnine
from plotnine import *
chart.save(filename = '../my-visualization.png')

Publishing Reports#

Reports can be shared as HTML webpages or PDFs. Standalone HTML pages tend to be self-contained and can be sent via email or similar.

A Jupyter Notebook can be converted to HTML with:

import papermill as pm
import subprocess

OUTPUT_FILENAME = "sample-report"

pm.execute_notebook(
    # notebook to execute
    '../my-notebook.ipynb',
    # if needed, rename the notebook as something different
    # this will be the filename that is used when converting to HTML or PDF
    f'../{OUTPUT_FILENAME}.ipynb',
)

# shell out, run NB Convert
OUTPUT_FORMAT = 'html'
subprocess.run([
    "jupyter",
    "nbconvert",
    "--to",
    OUTPUT_FORMAT,
    "--no-input",
    "--no-prompt",
    f"../{OUTPUT_FILENAME}.ipynb",
])

Or you can try:

# Execute NB
jupyter nbconvert --to notebook --execute --inplace my_notebook.ipynb
    
# Convert NB to HTML then to PDF
jupyter nbconvert --to html --no-input --no-prompt my_notebook.ipynb

You can also convert a Jupyter Notebook to PDF for distribution in a few different ways. You might wonder why we don’t suggest simply doing File -> Save and Export Notebook As -> PDF. We don’t recommend this method because it leaves all your code cells visible, which usually isn’t desirable.

All the code below are to be pasted into the terminal.

  • The PDF generated has a very academic look, similar to a LaTex document.

# Convert your original notebook
jupyter nbconvert --to pdf my_notebook.ipynb

# Hide all the code cells by adding --no-input
jupyter nbconvert --to pdf --no-input my_notebook.ipynb

  • For a less academic look, you can convert your notebook into html before using weasyprint. This might cause blank pages to appear, typically at the beginning of your PDF. You will need to manually remove them using Adobe.

# Make sure to install `weasyprint`
pip install WeasyPrint

# Execute NB
jupyter nbconvert --to notebook --execute --inplace my_notebook.ipynb
    
# Convert NB to HTML then to PDF
jupyter nbconvert --to html --no-input --no-prompt my_notebook.ipynb

# Convert to PDF
weasyprint my_notebook.html my_notebook.pdf

  • There are assignments that require you to rerun the same notebook for different values and save each of these new notebooks in PDF format. This essentially combines parameterization principles using papermill with the weasyprint steps above. You can reference the code that was used to generate the CSIS scorecards here. This script iterates over this notebook to produce PDF files for each of the nominated projects found here.

Note: Viewer may need access to the private CSIS repository.

Briefly, the script above does the following:

  • Automates the naming of the new PDF files by taking away punctuation that isn’t allowed.

  • Saves the notebook as html files.

  • Converts the html files to PDF.

  • Saves each PDF to the folder (organized by district) to our GCS.

  • Deletes irrelevant files.

  • Here are some tips and tricks when converting notebooks to HTML before PDF conversions.

    • Any formatting should be done in HTML/CSS first.

    • To create page breaks, add the following in a Markdown cell with however many <br> tags you’d like.

      <br>
<br>
<br>
<br>
<br>

    • Follow the writing, rounding, and visualization ideas outlined in Getting Notebooks Ready for the Portfolio section.

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