Reproducible Analysis

A "one-off" spreadsheet analysis becomes a "rerun-this-every- month" report faster than anyone expects. Reproducibility is the discipline of making sure that future-you (or future-them) can re-run your work and get the same result.

The reproducibility ladder

Most teams hover around L1–L2. The higher you go, the easier it is to trust — and reuse — the analysis later.

Why this matters

• Time: running the analysis again takes one command, not one afternoon.
• Auditability: if a stakeholder challenges a number, you can trace exactly how it was produced.
• Onboarding: a new teammate can rerun your work without a one-on-one walkthrough.
• Mistakes: when a bug is found, every dependent number can be regenerated automatically.

The core habits

1. Never modify the raw data

Treat the raw file as immutable. Every cleaning step should be code, not manual edits.

data/
  raw/         ← never edited
  interim/     ← intermediate steps
  processed/   ← final, analysis-ready

If your boss asks for the "original" file, you should always be able to point to data/raw/.

2. Top-of-notebook imports

import pandas as pd
import numpy as np
import plotly.express as px

pd.options.display.max_columns = 50

Future readers (including you) should be able to scan the imports and know what they're getting into.

3. Set random seeds

Any operation involving randomness — train/test splits, samples, model initializations — should set a seed. Otherwise your "reproducible" notebook gives slightly different numbers each run.

4. Restart and run-all, regularly

A notebook that works because you ran the cells out of order is not reproducible. Periodically: kernel → restart → run all. If it doesn't produce the same result from a blank state, something is wrong.

5. Pin your environments

A requirements.txt (or environment.yml) freezes package versions:

pandas==2.2.2
numpy==1.26.4
plotly==5.21.0

A year from now, Pandas may have removed or renamed something your notebook depends on. Pinning protects you.

6. Parameterize, don't hard-code

# Bad
df = df[df["year"] == 2024]

# Better
ANALYSIS_YEAR = 2024
df = df[df["year"] == ANALYSIS_YEAR]

Or, for more elaborate setups, load parameters from a config file or environment variables.

7. Separate steps clearly

A typical analysis notebook reads like a recipe:

Each section should produce a clearly named intermediate variable so you can pick up at any step.

8. Sanity-check the output

assert orders["amount"].min() >= 0, "Negative amounts shouldn't exist"
assert len(orders) > 0, "Empty result — check filters"

assert statements throughout a pipeline catch regressions before they propagate to charts and reports.

Notebook vs script

Many teams use notebooks for exploration and promote the final logic into a script as the analysis stabilizes.

A reproducibility checklist

Before you call an analysis "done", check:

• Raw data is unchanged on disk.
• All steps are in code (no manual Excel edits).
• Notebook runs top-to-bottom from a fresh kernel.
• Random seeds are set.
• Package versions are recorded.
• Key outputs (DataFrames, charts) are exported with date- stamped filenames.
• Inputs and parameters are clearly listed at the top.
• At least one sanity-check assertion exists per step.

Automation as the next step

Reproducible code is the precondition for automation. Once your analysis can be re-run with one command, you can:

• Schedule it weekly with a cron job.
• Run it on a fresh dataset uploaded by a colleague.
• Hand it off to an engineering team to productionize.

Without reproducibility, automation is impossible.

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