In Python data analysis, how do I write integration tests?

In Python data analysis, writing integration tests is crucial to ensure that different components of your data processing pipeline work together as expected. Integration tests validate the interactions between various parts of your application, such as data retrieval from a database, data manipulation, and output generation. Below is an example of how to write integration tests using the popular `unittest` library in Python.

import unittest import pandas as pd from my_data_analysis_module import DataProcessor # Example module class TestDataProcessorIntegration(unittest.TestCase): def setUp(self): # Set up a test data frame self.df = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) self.processor = DataProcessor() def test_integration_process_data(self): result = self.processor.process_data(self.df) expected_output = pd.DataFrame({ 'A': [2, 3, 4], # Example transformation 'B': [5, 6, 7] }) pd.testing.assert_frame_equal(result, expected_output) if __name__ == '__main__': unittest.main()

Python Data Analysis Integration Tests unittest Data Processing

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