Why Most Data Science Notebooks Die After Day One: How to Build Ones That Survive

A notebook dies the moment "Restart Kernel and Run All" stops working.

Nobody notices for a week. The analysis was finished, the chart went into a deck, the file got pushed. Then someone asks where a number came from; you open the notebook, run it from the top, and cell 12 throws a KeyError on a column you renamed in cell 31 and deleted in cell 44. The output cells still show the old numbers, so the notebook looks fine while being unrunnable.

The habits that prevent this are cheap. We are going to apply all of them to one real dataset and keep the whole thing under 100 lines of Pandas.

The Data

In this article, we are using a table called olympics_athletes_events, used in this interview question.

olympics_athletes_events is one row per athlete per event, which is the detail that matters later. Its 352 rows cover 336 athletes across 15 Games and 167 events, so 11 athletes appear more than once and one appears 6 times. The medal column is filled for 120 rows, and a blank means that athlete did not win a medal in that event.

Here is a sample:

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
21918	Georg Frederik Ahrensborg Clausen	M	28.0		Denmark	DEN	1924	Cycling
110345	Marinus Cornelis Dick Sigmond	M	26.0		Netherlands	NED	1924	Football
…	…	…	…	…	…	…	…	…	…
999998	John Testman	M	30.0	180.0	Canada	CAN	2004	Athletics	Bronze

Let's now explore the habits that keep your notebook alive.

Habit 1: Adding Configurations to the First Cell

Every path, seed, threshold, and magic number goes in the first cell. Nothing else does.

Two important things happen here. Someone reading the notebook six months later can see every assumption in 15 lines without scrolling. And when the file moves or the threshold changes, there is exactly one place to edit.

Habit 2: Writing One Function per Cell

Habit 3: Validating the Data Before You Trust It

Running it on the raw file: