# 5.3. Test input data quality, stability and drift \[CODE PRACTICE]

{% embed url="<https://youtu.be/wZ0op3L9t2k?si=xQ-AoYiHp6zbv2Ml>" %}

**Video 3**. [Test input data quality, stability and drift \[CODE PRACTICE\]](https://youtu.be/wZ0op3L9t2k?si=xQ-AoYiHp6zbv2Ml), by Emeli Dral

In this video, we run test suites for data quality, data stability, and data drift on raw and pre-processed data. We also get the output as a Python dictionary to show how to integrate conditional checks in the prediction pipelines.

**Want to go straight to code?** Here is the [example notebook](https://github.com/evidentlyai/ml_observability_course/blob/main/module5/data_quality_test_practice.ipynb) to follow along.

**Outline:**\
[00:00](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=0s) Introduction\
[01:10](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=70s) Imports and data preparation\
[03:50](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=230s) Test data stability on raw data\
[06:40](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=400s) Run the test suite and explore the results\
[11:07](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=667s) Test data quality on raw data\
[12:34](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=754s) Test data drift on raw data\
[15:47](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=947s) Run tests and interpret data drift on pre-processed data\
[19:28](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=1168s) Whether to run tests on raw or pre-processed data\
[20:07](https://www.youtube.com/watch?v=wZ0op3L9t2k\&t=1207s) Get output as JSON or Python dictionary and create conditions


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