P2-10.3 Organizing Notebooks as Rerunnable Records¶

Section ID: P2-10.3 Version: v2026.09.15

Saved Documents and Execution State¶

Jupyter Notebook files are JSON-based documents with the .ipynb extension. The nbformat documentation describes lists of cells and metadata, with cells that can contain inputs and outputs. Jupyter's architecture documentation likewise describes notebooks as documents storing code, output, and Markdown notes together.

Distinguish code, saved output, and running state.

flowchart TB
  A["Notebook file<br/>.ipynb document"]
  B["Markdown cell<br/>questions and explanations"]
  C["Code cell<br/>Python code"]
  D["Saved output<br/>values, tables, charts, errors"]
  E["Kernel memory<br/>variables and imports"]
  A --> B
  A --> C
  A --> D
  C -. "execute" .-> E
  E -. "produce output" .-> D

Code and some outputs can remain in the notebook file. Variables and imports live in the running kernel's memory, which a kernel restart clears. Disk files and installed packages are separate. Deleting a remote virtual machine can also remove files and installations stored only on that machine.

Saving a notebook is not enough: check whether the saved document can run again later.

Clearing Outputs and Restarting the Kernel¶

Clearing the display and resetting memory are different operations. This table assumes the notebook has been saved and the same file system remains available.

Restarting a kernel does not create a fresh installation or file system. Deleting a Colab remote VM and obtaining another resets more: files and packages that existed only on the old VM can disappear too.

From Preparation to Interpretation¶

A useful learning notebook should read and execute from top to bottom.

flowchart TB
  A["Purpose"]
  B["Settings and imports"]
  C["Prepare data"]
  D["Calculate"]
  E["Inspect output"]
  F["Write interpretation"]
  G["Restart kernel and run all"]
  H["No errors; output matches interpretation?"]
  I["Save"]
  A --> B --> C --> D --> E --> F --> G --> H
  H -->|Yes| I
  H -->|No: revise| B

First write what you want to investigate. Then import packages, prepare data, and run calculations. Interpret the results after inspecting them.

When this order breaks down, reopening the notebook can leave you unsure why a calculation was performed, which data it used, or what the result means.

Purpose and Data Scope¶

Put the purpose before the code at the start of the notebook.

For example: “This notebook calculates the mean and variance of a small score dataset to compare its center and spread.”

Notebooks grow quickly as cells are added. Without a purpose, experiments scatter and the meaning of results becomes unclear.

Briefly record these items in the purpose cell:

If you add an experiment with a different purpose, give it a distinct title and input conditions.

Package Preparation¶

Place installation instructions and imports before calculations. In a Python notebook without NumPy, use %pip install numpy. %pip is an IPython command targeting the current kernel environment, not ordinary Python script syntax.

This setup cell imports NumPy and prints its version. Record the version actually used, since it depends on the environment.

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import numpy as np
print(np.__version__)

Input Data¶

Files require a source and preparation instructions as well as a path. Saving the string data/scores.csv does not save the file itself.

These are five students' scores out of 100. Assignment displays no output and prepares scores for the later calculation cell.

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scores = [82, 75, 45, 90, 61]

Output and Interpretation¶

The scores total 353 across five students, giving a mean of 70.6. This cell prints a label alongside the value.

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print("mean score:", sum(scores) / len(scores))

The output is mean score: 70.6. Below it, write “The five students' mean is 70.6 and the lowest score is 45.” Update both output and interpretation if the data changes.

Configuration Cell Order¶

When different cells assign the same name, the last executed assignment takes effect. The first cell sets a learning rate of 0.1.

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learning_rate = 0.1

The second changes it to 0.01.

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learning_rate = 0.01

The check cell prints the current value.

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print(learning_rate)

Running first cell → second cell → check gives 0.01. Running second → first → check gives 0.1. Keeping final settings in one cell reduces confusion between document order and execution order.

Check important notebooks as follows:

1. Remove unnecessary temporary cells while retaining required preparation.
2. Restart the Python kernel.
3. Run cells from first to last.
4. Check for errors and agreement between outputs and explanations.
5. Save the verified code, outputs, and interpretation.

Starting State for Randomness¶

AI and statistics exercises use randomness for sampling and initialization. This code creates a generator with seed 42 and selects three of five values without replacement. The review environment produced [50 10 40].

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import numpy as np

rng = np.random.default_rng(seed=42)
sample = rng.choice([10, 20, 30, 40, 50], size=3, replace=False)
print(sample)

A seed can be understood as a starting value for recreating the same random sequence. Fixing it is not mandatory in every exercise, but recording it helps when you want to see the same result again.

A seed does not solve all reproducibility problems. Package versions, environment, hardware, and parallel execution can affect results. Repeating only the sampling step advances the same generator's state and may produce different values. Rerun from generator creation to return to the same starting state.

Preparation for Sharing¶

The Colab FAQ explains that text, code, output, and comments can be shared with a notebook, while its virtual machine, runtime files, and installed libraries are not shared.

Before sharing a Colab notebook, check:

When to Move Code into Scripts¶

Code started in notebooks often grows. Eventually a .py script may become a better fit.

Consider separation when you see these signs:

The organization can follow this pattern:

flowchart TB
  A["Notebook exploration"]
  B["Extract repeated calculations into functions"]
  C["Module file<br/>store shared functions"]
  D["Import in notebooks<br/>compare and interpret"]
  E["Import in scripts<br/>run defined tasks"]
  A --> B --> C
  C --> D
  C --> E

Keep common calculations in a module, with inputs and result interpretation in the notebook.

Case: A Deleted Preparation Cell¶

Suppose the five-score mean displays correctly, then the cell creating scores is deleted. Rerunning only the mean cell in the same kernel still produces 70.6 because the variable remains.

After restarting, that mean cell raises NameError because scores is absent. Restore preparation and run from top to bottom. Saved output alone cannot establish that the current code can run again.

A Runnable Record¶

This notebook contains purpose, inputs and threshold, calculation, output, and interpretation in order. It uses only standard Python features, with no external files or package installation. Download it and open it in Jupyter, or upload it as a notebook in Colab.

Score and selection-threshold notebook

The default scores are [82, 75, 45, 90, 61] and the threshold is 60. Running all cells produces:

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count: 5
mean: 70.6
threshold: 60
selected: [82, 75, 90, 61]

Change the input-cell threshold to 80 and rerun through calculation and output: the mean stays 70.6 and selection becomes [82, 90]. Changing the final score to 100 makes the mean 78.4. An empty score list deliberately raises ValueError in the calculation cell. Output left from a previous run after an error is not the current run's result.

Update the interpretation for the changed conditions, restart the kernel, and run all cells. Save when execution succeeds and output agrees with interpretation. Saved output is a comparison record, not a replacement for a new run.

Checklist¶

• Can you explain a useful notebook as a rerunnable record?
• Can you explain why hidden state is a problem?
• Can you explain why setup and data cells belong near the beginning?
• Can you explain why restarting and running from top to bottom matters?
• Does the notebook start with purpose and scope?
• Are required imports and installation steps near the beginning?
• Is the input data source explained?
• Can all cells run in order without errors?
• Is interpretation recorded below outputs?
• Are seeds or possible variation explained when randomness is involved?
• Have files, packages, and permissions been checked for Colab sharing?
• Should repeated code move into functions or .py files?
• Can you explain why reproducibility needs the same preparation flow, not just the same file?

Sources and References¶

• Project Jupyter, Architecture, Jupyter Documentation, checked on 2026-09-15. Used to confirm that notebook documents store code, output, and markdown notes together.
• Project Jupyter, The Jupyter Notebook Format, nbformat documentation, checked on 2026-09-15. Used to confirm that .ipynb files are JSON-based documents containing a list of cells, metadata, cell inputs, and outputs.
• Google, Google Colab FAQ, Google Colab, checked on 2026-09-15. Used as the basis for the caution that shared Colab notebook contents and runtime state are separate.
• NumPy Developers, Random Generator, checked on 2026-09-15. Reference for generators, seeds, state, and version compatibility limits.