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MLOps Academy · Lesson

Unit Test Your Data Pipeline

Assert shapes, types, and ranges with pytest.

Unit Test Your Data Pipeline is a free MLOps Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Test the Data, Not Just the Model

Most ML bugs hide in the data pipeline, not the model. A few cheap unit tests on your data catch problems long before training. 🧪

What pytest Gives You

You write tests as plain functions and let pytest discover and run them. Any assert that fails turns into a clear, readable failure report.

Anatomy of a Data Test

A data test loads a sample, calls your transform, then asserts an expectation. The key tool is the assert statement deciding pass or fail.

def test_no_nulls(df):
    assert df["age"].notna().all()

Assert the Shape

Pipelines silently drop or add columns. Lock the shape so a 10-column frame never sneaks through as 9 columns unnoticed.

def test_shape(df):
    assert df.shape[1] == 10

Assert the Column Types

A number read as text breaks training quietly. Pin each column dtype so a type drift fails the test instead of the model.

def test_dtype(df):
    assert df["price"].dtype == "float64"

Assert Sensible Ranges

Guard against impossible values. A range check stops a negative age or a 300% probability from ever reaching your model.

def test_range(df):
    assert df["age"].between(0, 120).all()

Catch the Nulls

Missing values are the most common data bug. Assert that critical columns have no nulls, or that nulls stay under a known threshold.

Use Small Fixtures

Tests should run in milliseconds. A pytest fixture builds a tiny hand-made frame so tests stay fast and never touch real data.

import pytest
@pytest.fixture
def df():
    return load_sample()

Test Each Transform Alone

Test one step at a time so failures point to the exact culprit. This is unit testing: small, isolated, and fast to debug.

Run the Suite

One command runs every test and prints a green or red summary. Run pytest locally and again in CI on every push.

# in your terminal
pytest -q

Test Edge Cases on Purpose

Feed an empty frame, one row, or all-null input. Good edge case tests prove your pipeline fails loudly, not silently.

Quick Check

Which check best protects against a column being read as the wrong type?

Recap: Tests Catch Data Bugs

You now unit test data with pytest: assert shape, types, ranges, and nulls on tiny fixtures so bad data fails the build, not your users. ✅

Frequently asked questions

Is the “Unit Test Your Data Pipeline” lesson free?

Yes — the full text of “Unit Test Your Data Pipeline” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.

What will I learn in “Unit Test Your Data Pipeline”?

Assert shapes, types, and ranges with pytest. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start MLOps Academy?

No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Unit Test Your Data Pipeline” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this MLOps Academy lesson?

Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Unit Test Your Data Pipeline
  2. Behavioral Tests for Models
  3. Set Quality Gates and Thresholds
  4. Validate Data with Great Expectations
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