Report generation (notebooks to HTML/PDF) in Data Analysis Python - Time & Space Complexity
When we create reports from notebooks, we want to know how the time to generate them changes as the report size grows.
We ask: How does the time to convert notebooks to HTML or PDF grow with more content?
Analyze the time complexity of the following code snippet.
import nbconvert
import nbformat
# Load notebook file
with open('report.ipynb') as f:
notebook_content = f.read()
# Convert notebook to HTML
html_exporter = nbconvert.HTMLExporter()
(body, resources) = html_exporter.from_notebook_node(nbformat.reads(notebook_content, as_version=4))
# Save HTML output
with open('report.html', 'w') as f:
f.write(body)
This code reads a notebook file, converts it to HTML format, and saves the result.
Identify the loops, recursion, array traversals that repeat.
- Primary operation: Processing each cell in the notebook to convert it to HTML.
- How many times: Once for each cell in the notebook, so the number of cells (n).
As the number of notebook cells grows, the time to convert grows roughly in direct proportion.
| Input Size (n) | Approx. Operations |
|---|---|
| 10 | About 10 cell conversions |
| 100 | About 100 cell conversions |
| 1000 | About 1000 cell conversions |
Pattern observation: Doubling the number of cells roughly doubles the work needed.
Time Complexity: O(n)
This means the time to generate the report grows linearly with the number of notebook cells.
[X] Wrong: "The conversion time stays the same no matter how big the notebook is."
[OK] Correct: Each cell needs to be processed, so more cells mean more work and more time.
Understanding how report generation time grows helps you design efficient data workflows and explain performance in real projects.
"What if the notebook contains very large images or outputs in some cells? How would that affect the time complexity?"