data-contracts

name

data-contracts

description

Use when computing, transforming, cleaning, joining, merging, aggregating, reshaping, or modeling ANY result from data — before you trust a number, a table, a model metric, or a chart. Establishes data contracts and invariants up front, validates assumptions before building on them, asserts join cardinality before every merge, and freezes validated results as regression baselines. Use this whenever you load a dataset, write a transform or cleaning step, do a join or group-by, fit a model, or are about to report a figure — even if the user only says "analyze this", "what's the trend", "clean this up", "merge these two files", or "build this metric" without ever mentioning tests or validation — in R, Julia, Python, or Stata. NOT the owner of the cleaning PHASE: building a dataset end-to-end is data-preparation, which calls this skill per step.

Data Contracts

Overview

A number you computed but never validated is a guess wearing a lab coat.

Core principle: Lock in what must be true before you trust what you discovered.

This is the checker. It asserts invariants and reconciles totals; it does not plan or run the work. Its complement is the doer: during the cleaning phase, data-preparation calls this skill to validate each ingest/join/dedup/recode step, and executing-analysis-plans calls it on every later spine step and fanned-out spec. You reach for data-contracts directly the moment you're about to trust a number or do a join — and the doers reach for it on your behalf throughout.

This is the data-analytics counterpart of test-driven development. TDD's literal ritual — "write a failing test asserting the exact output, then implement" — does not transfer to analysis, because in analysis the output is the unknown. You cannot assert mean == 42.3 before you've computed it; computing it is the whole point. But the discipline underneath TDD transfers exactly, and matters more here.

Why analysis breaks naive TDD (and why you still need its spirit)

In software the dangerous bug usually throws — a stack trace, a red test, something loud. In analysis the dangerous bug is silent: the code runs cleanly and hands you a confident, wrong answer.

• A join fans out 1-to-many and your revenue triples.
• One NA / missing / NaN poisons a mean, or gets silently dropped and biases it.
• Units are off by 100× (dollars vs. cents, proportion vs. percent).
• A timezone or date-floor shifts every event into the wrong day.
• A factor level / category you didn't expect quietly creates a new group.
• Train and test overlap and your model metric is a fantasy.

None of these raise an error. So we move the discipline from "assert the answer first" (impossible) to "assert everything around the answer that must hold regardless of the answer." Those are your data contracts and invariants. They are checkable before you know the result and again after — which is exactly the leverage test-first gives you in software.

Two regimes — know which one you're in

1. Exploration (you're hunting for the answer: EDA, plotting, trying models). Do not force test-first here — it would be theater. The rule that does apply: validate the inputs before you trust any output, and check intermediate results at every step. Trust nothing you haven't looked at.

2. Reusable rules (a cleaning step, a metric definition, a transform you'll run again, a feature pipeline). Here you do know the rule, so real test-first applies cleanly. Hand-build a tiny fixture with a known answer, write the check, watch it fail, then implement. A metric definition without a test is a rumor.

Most analysis work flows from regime 1 into regime 2: you explore to find the right transform, then you lock it down as a tested, contracted rule. The mistake is staying in regime 1 forever and shipping exploratory code as if it were production.

The loop (analytics red-green)

CONTRACT  →  CHECK IT BITES  →  COMPUTE  →  RECONCILE  →  FREEZE

1. CONTRACT — Before computing, write down what must be true: row counts, key uniqueness, value ranges, totals that must reconcile, allowed categories, types and units. (See the invariant catalog below.)
2. CHECK IT BITES — Confirm the check actually fails on bad data: feed it one deliberately broken row and watch the assertion trip. (Recalling a past incident motivates writing the check; it is not a substitute for seeing it go red.) A check that cannot fail proves nothing — the analytics form of TDD's "watch the test fail." If you've never seen it red, you don't know it's testing anything.
3. COMPUTE — Do the transform / aggregation / model.
4. RECONCILE — Run the contract against the result. Reconcile totals back to the source ("do my segment revenues sum to the grand total I started with?"). Mismatch = stop; route to wrong-number-debugging to bisect to the bad step — don't patch and proceed. And if the "fix" would drop/winsorize/filter rows or move a number the user has already seen, that's a sample/spec change, not an autonomous fix → analysis-checkpoints.
5. FREEZE — Once a result is validated, snapshot it as a golden / reference output (a small committed CSV/parquet, or stored summary stats). Future re-runs and refactors compare against it, so a silent drift becomes a loud failure instead of a number nobody noticed changed.

Join cardinality — the single highest-yield contract

More silent analytics disasters come from joins than from anything else, because a join is the one operation that can change your row count in either direction without erroring. Before every merge, declare the relationship you expect, then assert it:

• 1:1 — both keys unique; row count must not change. (Two tables keyed on the same entity.)
• 1:m / m:1 — one side unique; rows on the many side preserved, none duplicated by accident.
• m:m — almost never what you actually want. If you didn't intend a cross-product, an m:m is a bug. Treat an unexpected m:m as a stop-the-line event.

Say out loud what you expect, then let the tool enforce it:

	Python (pandas)	R (dplyr)	Julia
Enforce cardinality	df.merge(o, on="id", validate="one_to_one") (or "one_to_many", "many_to_one")	left_join(o, by="id", relationship="one-to-one") (or "many-to-one")	assert key uniqueness before leftjoin: @assert allunique(o.id)
Catch dropped/added rows	assert len(out) == len(left) for a left join that must not fan out	stopifnot(nrow(out) == nrow(left))	@assert nrow(out) == nrow(left)
Catch unmatched keys	check indicator=True value counts	anti_join() to see what failed to match	antijoin(left, right, on=:id)

The row-count assertion around a join is the cheapest, highest-value check in all of data work. Write it every time.

Before a merge in an established project, consult the project's docs/LESSONS.md (and your memory) for prior join failures in this data — a fan-out that bit last month, a vintage mismatch in this crosswalk, a key that wasn't as unique as it looked. The capture half of that loop lives in result-verification; this is the recall half — a logged join bug only stops recurring if you read it back at the moment you're about to repeat it.

The invariant catalog — what to assert

These are the things that hold regardless of the answer. Reach for the ones that fit your step:

• Cardinality / joins — Did rows fan out or vanish? Assert the expected row count after every join (see above).
• Keys & nulls — Join keys unique where they should be? No unexpected nulls in keys? Primary keys actually unique?
• Versioned / vintage keys — when a join key gets re-released over time (geographies like CBSA/county-FIPS, industry or diagnosis codes, taxonomies, any crosswalk), assert both sides use the same vintage, not just the same key. A vintage mismatch doesn't error — it silently mismatches (a low match rate you might not notice) and drops or duplicates rows. Pin the vintage as part of the key contract.
• Ranges & domains — Ages in [0,120], proportions in [0,1], no negative quantities, prices positive.
• Totals reconcile — Parts sum to the known whole. Pre-aggregation total == post-aggregation total. This catches the majority of silent join/filter bugs.
• Categories — The set of category levels matches expectations; no surprise new levels ("N/A", "unknown", mojibake, trailing-space duplicates).
• Types & units — dtype/eltype/class is what you think; dollars not cents; seconds not ms; the percent column really is a percent.
• Missingness — How many NA/missing/NaN? Did an operation silently drop them? Is the missingness rate stable vs. last run?
• Temporal — Date ranges sane, no future timestamps, timezone explicit, no duplicated periods after a resample.
• Determinism / reproducibility — Same input + same seed → same output. If it doesn't, you have hidden state.
• Leakage — For any model: no target leakage, no train/test overlap, no future information in features.

Provenance up front — write the lineage into the contract

A contract is not just about values; it's about where the values came from. Before you build on a column, you should be able to answer: where did this come from, how was it derived, and what was dropped or recoded upstream to make it? Capture that as part of the contract — a short data dictionary alongside the code:

• Source — which raw file / table / extract / API each column originates from.
• Derivation — the exact rule that produced any computed column (and its units).
• Upstream surgery — rows filtered, categories collapsed, values recoded, deduplication applied before this dataset reached you. These are the silent killers; an analyst who doesn't know a 30% sample was already taken will over-count by 3×.

When a number later comes out wrong, this lineage is what lets wrong-number-debugging bisect fast instead of guessing. Document it while you still remember it.

Watch it bite — the "see it fail" rule, adapted

The single most-skipped step, and the one that separates real validation from decoration. After writing a check, prove it can catch the thing it's meant to catch:

• Perturb one row to violate the contract and confirm the assertion fires.
• Or temporarily point the check at a known-bad earlier version of the data.
• Or recall the specific incident that motivated the check and reconstruct it.

If your row-count assertion would pass on a broken join, it isn't protecting you — it's lying to you comfortably. Fix the check until it bites.

Freeze validated results — golden outputs

Once a number is right, make it stay right. Write the validated result (or its summary statistics) to a small, committed reference file. On the next run, diff against it. This converts the worst class of analytics bug — "the number changed three weeks ago and nobody noticed" — into an immediate, obvious failure. It's the regression test of data work.

Language cheat-sheet (R / Julia / Python)

Use the idioms native to each stack rather than bolting on a framework you don't need:

Need	Python	R	Julia
Inline assertion	assert df.shape[0] == n, msg	stopifnot(nrow(df) == n)	@assert nrow(df) == n msg
Data contract / schema	pandera, great_expectations, pydantic	assertr, pointblank, validate	@assert on eltype, Test, custom schema check
Unit test (reusable rule)	pytest with tiny fixtures	testthat	Test stdlib (@test)
Missing handling to watch	NaN vs None, df.isna().sum()	NA, sum(is.na(x))	missing, count(ismissing, x)
Reconcile totals	assert np.isclose(parts.sum(), total)	stopifnot(isTRUE(all.equal(sum(parts), total)))	@assert isapprox(sum(parts), total)

Use floating-point-aware comparison (np.isclose / all.equal / isapprox) for any reconciliation — exact == on floats will betray you.

The table above covers one check at one line. The moment a script has two or more joins, a reconciliation, or a baseline to freeze, don't re-derive helper functions from scratch — copy the canonical prelude for your language from references/contract-helpers.md: assert_join (declared cardinality + row-count bracket + unmatched-key report), reconcile, na_audit, and freeze_baseline/check_baseline, in Python, R, Julia, and Stata (where isid, merge, assert(), and datasignature are built in).

Red flags — STOP and validate

• "The pipeline ran without errors, so the numbers are right." (Clean run ≠ correct result.)
• A join, merge, filter, or group-by with no row-count check before and after.
• Reporting a figure you computed but never reconciled against the source.
• "I'll just eyeball the head() / summary() and move on." (Eyeballing isn't a contract; it doesn't re-run.)
• Building step N on top of step N-1's output without having validated step N-1.
• A check that has never once failed — you don't know it works.
• Re-running an analysis and not comparing against the last known-good output.

Common rationalizations

Excuse	Reality
"The data's clean, it came from the warehouse."	Warehouses fan out, change schemas, and re-key. Trust, then verify with a row count.
"I can see it's right."	You can see a number. You can't see the rows the join dropped.
"The totals are close enough."	"Close" on a reconciliation usually means rows are leaking. Find out why before you round it away.
"Adding checks slows me down."	A wrong number in front of a stakeholder costs far more than the 60 seconds the assertion took.

When to Use → where this hands off

Contracts are not a terminal step. The moment a check trips, they propel you out of "keep building" and into a named next skill — route imperatively, don't just note the relationship:

digraph data_contracts_next {
    "Reconciliation / total / join-cardinality assertion FAILS?" [shape=diamond];
    "invoke wrong-number-debugging — bisect to the bad step" [shape=box style=filled fillcolor=lightgreen];
    "The 'fix' drops/filters/winsorizes rows or redefines a metric?" [shape=diamond];
    "invoke analysis-checkpoints — STOP, it's a design decision" [shape=box style=filled fillcolor=lightgreen];
    "Contract holds — keep computing the next step" [shape=box];
    "Reconciliation / total / join-cardinality assertion FAILS?" -> "invoke wrong-number-debugging — bisect to the bad step" [label="yes"];
    "Reconciliation / total / join-cardinality assertion FAILS?" -> "The 'fix' drops/filters/winsorizes rows or redefines a metric?" [label="no, but a 'fix' is tempting"];
    "The 'fix' drops/filters/winsorizes rows or redefines a metric?" -> "invoke analysis-checkpoints — STOP, it's a design decision" [label="yes"];
    "The 'fix' drops/filters/winsorizes rows or redefines a metric?" -> "Contract holds — keep computing the next step" [label="no"];
}

The Process

1. Write the contract and watch it bite — keys, ranges, totals, join cardinality; feed it one broken row and confirm the assertion fires before you trust it.
2. Compute, then reconcile against the source. A clean run is not a correct result.
3. If a reconciliation / total / cardinality assertion FAILS → STOP and invoke wrong-number-debugging — bisect the pipeline to the exact bad step; do not patch and proceed.
4. If the "fix" would drop/filter/winsorize rows, change a join's grain, or move a number the user has already seen → STOP and invoke analysis-checkpoints — that's a sample/spec redesign, not an autonomous bug fix; don't smuggle it in.
5. Otherwise freeze the validated result as a golden baseline and continue to the next step — every future re-run diffs against it.

The bottom line

Reported result  →  contract written, check seen to bite, totals reconciled, baseline frozen
Otherwise        →  not validated, just hopeful

You are not slowing down. You are refusing to be confidently wrong.