In this video we are going to cover another library for Data Quality testing. Previously we have used PyTest to carry out data quality tests. With PyTest we write our own functions to perform testing. The Expectation library has built-in functions to carry out the data quality tests.
With Great Expectations, you can assert what you expect from the data you load and transform, and catch data issues quickly – Expectations are basically unit tests for your data. Great Expectations also creates data documentation and data quality reports from those Expectations.

Link to PyTest video: [ Ссылка ]
Link to Notebook: [ Ссылка ]

Link to Great Expectations Docs: [ Ссылка ]
Link to functions glossary: [ Ссылка ]

#dataquality #Python #greatexpectations

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Topics in this video (click to jump around):
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0:00 Introduction Great Expectations
0:38 Notebook & Data Import
1:08 Convert to Great Expectations DataFrame
1:41 Run your First Data Quality Test
2:39 Primary Key Tests; Column Exists, Unique, Null & Data Type
3:56 Test Values in Set
5:09 Test Values in Range
7:07 Save Tests for re-use
7:33 Re-Use Tests

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