# How to Build Data Science Notebooks That Still Work Months Later
Every data scientist has lived this nightmare. A project sits untouched for weeks, and when someone finally needs to rerun the analysis, the notebook throws errors from top to bottom. The numbers in the charts are still there — but the code that produced them is broken beyond repair. Hidden dependencies, renamed columns, deleted variables, and silent mutations accumulate until the notebook becomes a museum exhibit: it looks correct but cannot be executed.
The good news is that the practices that prevent this decay cost almost nothing. This guide walks through six practical habits you can apply to any notebook, using a real Olympic athletes dataset and fewer than 100 lines of code.
—
## The Dataset
The data comes from a table called `olympics_athletes_events`, structured as one row per athlete per event. It contains 352 rows covering 336 athletes across 15 Olympic Games and 167 different events. Eleven athletes appear more than once, and one athlete appears six times. The `medal` column is populated for roughly 120 rows; a blank entry means the athlete participated but did not medal.
| id | name | sex | age | height | team | noc | year | sport | medal |
|—|—|—|—|—|—|—|—|—|—|
| 3520 | Guillermo J. Amparan | M | — | — | Mexico | MEX | 1924 | Athletics | — |
| 35394 | Henry John Finchett | M | — | — | Great Britain | GBR | 1924 | Gymnastics | — |
| … | … | … | … | … | … | … | … | … | … |
| 999998 | John Testman | M | 30.0 | 180.0 | Canada | CAN | 2004 | Athletics | Bronze |
A few things immediately stand out: the table mixes detailed records with incomplete entries (many missing age and height values), and some IDs sit far outside the expected range. These quirks become the perfect testing ground for the habits ahead.
—
## Practice 1: Centralize Every Assumption in One Place
Place all file paths, random seeds, thresholds, and expected value ranges inside the very first cell. Nothing else belongs there.
“`python
from pathlib import Path
import pandas as pd
DATA_PATH = Path(“olympics_athletes_events.csv”)
RANDOM_SEED = 17
NATURAL_KEY = [“id”, “games”, “event”]
MAX_ALLOWED_ID = 900_000
VALID_SEXES = {“M”, “F”}
VALID_MEDALS = {“Gold”, “Silver”, “Bronze”}
AGE_BOUNDS = (10, 75)
HEIGHT_BOUNDS_CM = (120, 230)
WEIGHT_BOUNDS_KG = (25, 220)
MIN_GROUP_SIZE = 5
“`
Two benefits make this habit worth the effort. First, a colleague who revisits the notebook after months can scan the first cell and understand every assumption without reading through pages of analysis. Second, when a file location changes or a threshold needs adjusting, there is exactly one place to update.
The seed is especially important. Even if you do not think you are sampling, many Pandas operations draw from the global random state — `df.sample()`, train/test splits, and even certain clustering initializations. A notebook that produces slightly different numbers each time it runs quickly loses the trust of everyone who uses it.
—
## Practice 2: Let Each Cell Define Only Functions
The single most important rule for notebook longevity: a cell should either define a function or call one — never both, and never write to a variable created in another cell.
“`python
def load_data(filepath: Path) -> pd.DataFrame:
“””Load the raw CSV with zero transformations.”””
return pd.read_csv(filepath)
def transform(df: pd.DataFrame) -> pd.DataFrame:
“””Remove test records, deduplicate, and normalize missing medals.”””
output = df[df[“id”] < MAX_ALLOWED_ID].copy()
output = output.drop_duplicates(subset=NATURAL_KEY, keep="first")
output["medal"] = output["medal"].fillna("None")
output["sex"] = output["sex"].astype("category")
output["season"] = output["season"].astype("category")
return output.reset_index(drop=True)def enrich(df: pd.DataFrame) -> pd.DataFrame:
“””Add decade, is_medalist, and bmi columns without modifying the input.”””
output = df.copy()
output[“decade”] = (output[“year”] // 10) * 10
output[“is_medalist”] = output[“medal”].ne(“None”)
output[“bmi”] = compute_bmi(output[“weight”], output[“height”])
return output
“`
Notice the `.copy()` call at the top of each function. As long as no function mutates its argument, the order of execution no longer matters. You can run `enrich` five times in a row and get the same result every time, which eliminates the classic failure mode where a cell produces different output on its second execution than on its first.
One subtle detail worth highlighting: the `transform` function replaces blank medals with the string `”None”` instead of leaving them as `NaN`. A blank medal means the athlete competed and did not place — that is a meaningful, factual value, not a missing entry. Treating it as `NaN` would cause it to vanish during aggregations and skew summary statistics.
—
## Practice 3: Validate Before You Trust the Data
Write down what you believe to be true about the dataset, and let the notebook verify those beliefs automatically.
“`python
def check(df: pd.DataFrame) -> list[str]:
“””Return a list of contract violations. Empty means the data passes.”””
issues = []
expected_columns = {“id”, “sex”, “age”, “height”, “weight”, “year”,
“sport”, “event”, “medal”, “games”, “team”}
missing = expected_columns – set(df.columns)
if missing:
issues.append(f”missing columns: {sorted(missing)}”)
return issues
duplicates = df.duplicated(subset=NATURAL_KEY).sum()
if duplicates:
issues.append(f”{duplicates} duplicate rows on {NATURAL_KEY}”)
flagged_ids = df.loc[df[“id”] >= MAX_ALLOWED_ID, “id”]
if len(flagged_ids):
found = sorted(int(i) for i in flagged_ids.unique())
issues.append(f”{len(flagged_ids)} out-of-range ids: {found}”)
invalid_sex = set(df[“sex”].dropna().unique()) – VALID_SEXES
if invalid_sex:
issues.append(f”unexpected sex values: {invalid_sex}”)
invalid_medal = set(df[“medal”].dropna().unique()) – VALID_MEDALS
if invalid_medal:
issues.append(f”unexpected medal values: {invalid_medal}”)
for column, (low, high) in [(“age”, AGE_BOUNDS),
(“height”, HEIGHT_BOUNDS_CM),
(“weight”, WEIGHT_BOUNDS_KG)]:
values = df[column].dropna()
count = ((values < low) | (values > high)).sum()
if count:
issues.append(f”{count} {column} values outside {low}–{high}”)
return issues
“`
Running this on the raw dataset produces a concise report:
“`
loaded 352 rows, 10 columns
validation: [‘3 duplicate rows on [id, games, event]’, ‘2 out-of-range ids: [999998, 999999]’]
after transformation: 347 rows
“`
Both findings deserve attention. The duplicate check targets the natural key `[“id”, “games”, “event”]` rather than just `id` alone. Checking on `id` alone would flag 16 rows, every single one a false alarm — because an athlete who enters multiple events at the same Games legitimately appears once per event. Choosing the wrong grouping key would have silently deleted 16 valid records.
The out-of-range IDs belong to planted test records: two entries for “John Testman” with fabricated IDs of 999998 and 999999. These records are completely invisible to `df.head()`, `df.describe()`, and every null count. Yet they directly affect medal-rate calculations for Athletics, shifting the rate by roughly 13%. A validation block that takes four lines caught a distortion large enough to mislead any conclusion drawn from the data.
—
## Practice 4: Embed Tests Directly in the Notebook
You do not need a separate test framework to validate notebook logic. A small fixture and a cell of assertions do the job perfectly.
“`python
def _sample_data() -> pd.DataFrame:
return pd.DataFrame({
“id”: [101, 101, 102],
“games”: [“1924 Summer”, “1924 Summer”, “1924 Summer”],
“event”: [“Fencing”, “Fencing”, “Fencing”],
“sex”: [“M”, “M”, “F”],
“age”: [22.0, 22.0, None],
“height”: [175.0, 175.0, 162.0],
“weight”: [72.0, 72.0, 54.0],
“year”: [1924, 1924, 1924],
“season”: [“Summer”, “Summer”, “Summer”],
“sport”: [“Fencing”, “Fencing”, “Fencing”],
“medal”: [“Gold”, “Gold”, None],
“team”: [“France”, “France”, “France”],
})
def run_all_checks() -> None:
fixture = _sample_data()
assert len(transform(fixture)) == 2, “transform() should drop duplicate”
assert transform(fixture)[“medal”].tolist() == [“Gold”, “None”],
“blank medal should become ‘None'”
original = fixture.copy()
enrich(fixture)
pd.testing.assert_frame_equal(fixture, original) # must not mutate input
result = enrich(transform(fixture))
assert result[“is_medalist”].tolist() == [True, False]
assert round(result.loc[0, “bmi”], 1) == 23.5
assert result[“decade”].unique().tolist() == [1920]
assert len(check(fixture)) == 1 # one duplicate found
assert “missing columns” in check(pd.DataFrame({“id”: [1]}))[0]
tampered = pd.concat([fixture, fixture.iloc[[2]].assign(id=999999)],
ignore_index=True)
assert any(“out-of-range” in msg for msg in check(tampered))
assert 999999 not in transform(tampered)[“id”].values
print(“All checks passed.”)
run_all_checks()
“`
Three rows of synthetic data, ten assertions, and sub-second execution. The `assert_frame_equal` line alone pays for itself: it will fail the moment someone accidentally writes to the input parameter instead of a local copy inside `enrich`. A failed assertion at 9 in the morning saves a wrong chart at 4 in the afternoon.
—
## Practice 5: Make Documentation Self-Testing
Comments become outdated the moment they are written. Docstring examples do not, because a tool like `doctest` actually runs them.
“`python
def compute_bmi(weight_kg: float, height_cm: float) -> float:
“””Calculate body mass index in kg per square meter.
>>> round(compute_bmi(72.0, 175.0), 1)
23.5
>>> round(compute_bmi(54.0, 162.0), 1)
20.6
“””
return weight_kg / (height_cm / 100) ** 2
“`
Since doctests do not automatically execute inside a notebook environment, invoke them explicitly:
“`python
import doctest
doctest.run_docstring_examples(compute_bmi, globals(), name=”compute_bmi”, verbose=True)
“`
Now the documented behavior and the actual behavior are locked together. Change the formula to accidentally use inches instead of centimeters, and the second example will immediately fail on the next run.
—
## Practice 6: Ensure the Notebook Can Run as a Standalone Script
The final cell ties everything together. It also includes a `main()` function guarded by a name check, which makes the entire notebook exportable to a clean Python script.
“`python
def medal_rate(df: pd.DataFrame, threshold: int = MIN_GROUP_SIZE) -> pd.DataFrame:
“””Compute medal win rate by sport for sports with enough participants.”””
summary = (df.groupby(“sport”, observed=True)
.agg(count=(“id”, “size”),
medals_won=(“is_medalist”, “sum”),
avg_age=(“age”, “mean”))
.query(“count >= @threshold”))
summary[“rate”] = (summary[“medals_won”] / summary[“count”]).round(3)
summary[“avg_age”] = summary[“avg_age”].round(1)
return summary.sort_values(“rate”, ascending=False)
def main() -> pd.DataFrame:
raw = load_data(DATA_PATH)
print(f”loaded {len(raw)} rows, {raw.shape[1]} columns”)
problems = check(raw)
print(“validation:”, problems or “passed”)
df = enrich(transform(raw))
print(f”after processing: {len(df)} rows”)
print(“spot check:”)
print(df.sample(3, random_state=RANDOM_SEED)[[“name”, “year”, “sport”, “medal”, “bmi”]])
report = medal_rate(df)
print(f”medal rate by sport ({len(report)} sports):”)
print(report)
return report
if __name__ == “__main__”:
run_all_checks()
doctest.run_docstring_examples(compute_bmi, globals(), name=”compute_bmi”, verbose=True)
main()
“`
That `if __name__ == “__main__”` line is the part most people skip — and the one that matters most. When you convert the notebook to a script using standard tools, every top-level statement fires cleanly on import. Without it, running the notebook as a module causes every cell to execute immediately, which breaks any downstream pipeline.
The spot check also serves an important diagnostic purpose. Printing three seeded rows alongside the aggregated results means you see real data values up close. In this dataset, for instance, a blank BMI in the sample immediately reveals that most older records lack height measurements — 226 of the 352 rows have no height data at all.
—
## Frequently Asked Questions
**Why does cell execution order matter so much in notebooks?**
Notebooks maintain state across cells. If Cell B depends on a variable created in Cell A, and you modify Cell A without rerunning it first, Cell B still references the old value. This silent inconsistency is the number one cause of “works on my machine” notebook failures.
**What happens if I don’t use `.copy()` inside my functions?**
Without it, any operation on the input DataFrame modifies the original object in memory. Later cells that reference that object will see changed values, making it impossible to reproduce intermediate results. Functions that always copy their inputs are idempotent and safe to rerun in any order.
**Is it worth writing tests for a notebook that will only be run once?**
Yes. Notebooks that are “only run once” have a way of being revisited when stakeholders ask questions months later. A small set of assertions — even just three or four — catches the most common failure modes: missing columns, type mismatches, and silent data mutations.
**Can these practices be applied to other languages or tools beyond Python?**
The underlying principles are universal. The equivalent of centralizing configuration means keeping all connection strings, parameters, and constants in one location. The equivalent of function-per-cell means avoiding inline mutations in SQL cells, R chunks, or Scala blocks. The core idea — make the notebook reproducible without relying on memory — applies everywhere.
**What if the validation cell finds problems I do not know how to fix?**
That is exactly the point. A validation cell should surface issues early and loudly. If you discover unexpected values, missing columns, or duplicates you cannot explain, pause and investigate before proceeding. It is far better to fix the data pipeline now than to build analysis on a flawed foundation.
—
## Conclusion
The six habits summarized here are not about writing perfect code. They are about building notebooks that remain trustworthy over time.
1. **Centralize configuration** — one cell holds every assumption, threshold, and seed.
2. **Define only functions in cells** — every function copies its inputs and avoids side effects.
3. **Validate automatically** — state what you believe about the data and let the notebook check it.
4. **Embed assertions** — a small fixture and a handful of checks run in every session.
5. **Use executable documentation** — docstring examples prove that comments match behavior.
6. **Support script export** — a guarded `main()` function lets the notebook run cleanly as a standalone file.
The total investment is roughly thirty minutes for the first notebook and about five minutes for each subsequent one. The payoff is a notebook that you — or anyone on your team — can open months later, restart the kernel, run every cell, and get the same results.
A dataset that looks clean on the surface often carries surprises. Duplicate entries planted by test fixtures, out-of-range IDs, and silently dropped records all lurk beneath `df.head()`. The difference between a notebook that survives and one that dies comes down to a few small patterns written at the start.
Restart the kernel. Run all cells. If every assertion passes and every print statement produces the expected output, the notebook is alive — and it will still be alive next month.
Thank you for reading
