great-expectations-experimental

import great_expectations as gx import pandas as pd import os # Create a sample DataFrame df = pd.DataFrame({ "col1": [1, 2, 3, 4, 5], "col2": ["A", "B", "C", "D", "E"] }) # Initialize an ephemeral DataContext # This creates a temporary context in memory without modifying the filesystem. context = gx.get_context(cloud_mode=False) # Add an In-Memory Data Asset # The name 'my_experimental_dataframe' is used to refer to this data within GX. my_asset = context.add_pandas_dataframe_asset( dataframe=df, name="my_experimental_dataframe" ) # Build a batch request for validation batch_request = my_asset.build_batch_request() # Get a Validator for the specified batch validator = context.get_validator(batch_request=batch_request) # Add an expectation: expect values in 'col1' to be between 1 and 5 (inclusive) validator.expect_column_values_to_be_between(column="col1", min_value=1, max_value=5) # Add another expectation: expect 'col2' to contain distinct values from a set validator.expect_column_distinct_values_to_contain_set(column="col2", value_set=["A", "C", "E"]) # Validate the data against the defined expectations results = validator.validate() print("\nValidation Results:") print(f"Overall Validation Success: {results.success}") # You can inspect individual expectation results for result in results.results: print(f" Expectation: {result.expectation_config.expectation_type}, Success: {result.success}")