Construct tests for evaluate_dtype()

[ETA444]

Summary of Unit Tests for evaluate_dtype()

The evaluate_dtype() function assesses the data types of specified columns in a DataFrame, which is crucial for ensuring data is appropriately preprocessed for analytical tasks. The test suite covers various error-handling scenarios to ensure robustness and functionality tests to verify the accurate identification of data types.

Detailed Breakdown of Tests

Error-Handling Tests

1. Invalid DataFrame Type:

   • Ensures a TypeError is raised if the input is not a DataFrame.

2. Invalid Column Names Type:

   • Verifies a TypeError for non-list col_names.

3. Non-string Column Names:

   • Checks for TypeError when column names are not strings.

4. Invalid Max Unique Values Ratio Type:

   • Ensures a TypeError for non-float max_unique_values_ratio.

5. Invalid Min Unique Values Type:

   • Checks for TypeError when min_unique_values is not an integer.

6. Invalid String Length Threshold Type:

   • Verifies a TypeError for non-integer string_length_threshold.

7. Invalid Output Type:

   • Ensures a TypeError for non-string output parameter.

8. Empty DataFrame:

   • Checks for a ValueError when the DataFrame is empty.

9. Invalid Max Unique Values Ratio:

   • Verifies handling of out-of-bounds max_unique_values_ratio.

10. Invalid Min Unique Values:

    • Ensures correct error handling for invalid min_unique_values.

11. Invalid String Length Threshold:

    • Checks for errors in string_length_threshold values.

12. Empty Column Names List:

    • Verifies a ValueError for empty col_names.

13. Nonexistent Column:

    • Ensures correct error handling for nonexistent columns.

14. Invalid Output Option:

    • Checks for a ValueError with invalid output options.

Functionality Tests

1. Numerical Data Identification:

   • Confirms correct identification of numerical data.

2. Categorical Identification from Numerical:

   • Verifies numerical data treated as categorical based on unique values.

3. Text Data Identification:

   • Checks for correct identification of text data based on string length.

4. Datetime Data Identification:

   • Ensures correct identification of datetime columns.

5. Multiple Columns Identification:

   • Tests accurate data type identification for multiple columns.

6. Output for Numerical Data:

   • Confirms list output matches expected numerical data types.

7. Output for Categorical Data:

   • Verifies list output for categorical data identification.

8. Output for Datetime Data:

   • Ensures correct list output for datetime columns.

9. Output for Text Data:

   • Checks list output for text data based on string length.

10. Handling Small Categorical Data:

    • Verifies identification of categorical data in small datasets.

11. Handling Small Text Data:

    • Ensures text identification in smaller datasets.

12. Handling Small Numerical Data:

    • Checks numerical data identification in reduced datasets.

13. Handling Small Mixed Data Types:

    • Tests identification across multiple data types in small datasets.

Example Code from the Suite

Here's an example test code snippet for the "Numerical Data Identification":

def test_evaluate_dtype_numerical_identification(sample_dataframe):
    """Test correct identification of numerical data types."""
    result = evaluate_dtype(sample_dataframe, ['Income'], output='dict')
    assert result['Income'] == 'numerical', "Income should be identified as numerical"

This test verifies that the evaluate_dtype() function correctly identifies a column (Income) as numerical based on its contents.

Full Test Suite Access

For a comprehensive view and to explore more about the tests, you can access the full test suite here: Evaluate Dtype Test Suite.