⚠️ Your Jupyter Notebook Is Lying to You

Your code works.
But only because you ran cell 17 before cell 5… and forgot about it.

Jupyter notebooks are great for experimentation.
But they can silently break when run out of order.

And worse — they won't warn you.

😬 Why This Hurts

• You debug something that wasn’t actually broken.

• Your model trains with outdated data.

• Your analysis only works on your machine.

Reproducibility dies the moment you say,
"Wait, which cell did I run again?"

✅ Fix It: Keep Your Notebook Honest

Here are 4 simple tricks to keep your notebook logic clean:

1. Restart & Run All — Regularly

Before sharing, exporting, or trusting your notebook:
🧼 Restart the kernel
▶️ Run all cells top-to-bottom

Jupyter: Kernel → Restart & Run All

If something breaks — good. Now you know.

2. Add Cell Execution Numbers

Out-of-order runs are easier to catch when you can see them.

Enable this in Jupyter Lab:

Settings → Advanced Settings Editor → Notebook → Show Execution Timing

This shows which cells ran when.

3. Use Assertions to Catch Bad States

Add quick sanity checks:

assert df is not None, "DataFrame is missing"
assert df.shape[0] > 0, "No rows in data"

Break early, debug faster.

4. Consider a Linear Notebook Pattern

Structure matters. Try this:

• 🔹 Section 1: Imports & config

• 🔹 Section 2: Load data

• 🔹 Section 3: Clean/process

• 🔹 Section 4: Model or analysis

• 🔹 Section 5: Output/save

This makes your notebook read top-down like a script.

💡 Bonus Tip: Convert to .py for Stability

Run this:

jupyter nbconvert --to script your_notebook.ipynb

Now you can debug it as a regular Python file — no cell dependencies to worry about.

📊 Poll

Do you use "Restart & Run All" before pushing or sharing notebooks?
Vote here and see how others handle notebook sanity.