collate-data-diff

import pandas as pd import sqlalchemy as sa from data_diff import diff_tables, TableSegment, connect_to_uri # Create two in-memory SQLite databases for demonstration uri_1 = "sqlite:///:memory:" engine_1 = sa.create_engine(uri_1) conn_1 = engine_1.connect() uri_2 = "sqlite:///:memory:" engine_2 = sa.create_engine(uri_2) conn_2 = engine_2.connect() # Create dummy data in pandas DataFrames df1 = pd.DataFrame({ 'id': [1, 2, 3, 4], 'name': ['Alice', 'Bob', 'Charlie', 'David'], 'value': [10, 20, 30, 40] }) df2 = pd.DataFrame({ 'id': [1, 2, 3, 5], # id 4 removed, id 5 added 'name': ['Alice', 'Bob Changed', 'Charlie', 'Eve'], # Bob's name changed 'value': [10, 25, 30, 50] # Bob's value changed }) # Populate tables in the in-memory databases df1.to_sql('table_a', conn_1, index=False, if_exists='replace') df2.to_sql('table_b', conn_2, index=False, if_exists='replace') # Connect to the in-memory databases using data_diff's `connect_to_uri` db1 = connect_to_uri(uri_1) db2 = connect_to_uri(uri_2) # Define TableSegment objects, specifying the key_columns for efficient diffing table1 = TableSegment(db1, 'table_a', key_columns='id') table2 = TableSegment(db2, 'table_b', key_columns='id') # Perform the diff and print results print("Differences found:") diff_results = list(diff_tables(table1, table2)) for d in diff_results: print(d) # Expected Output: # (- , 4, 'David', 40) # Row removed from table_a # (+ , 5, 'Eve', 50) # Row added to table_b # (- , 2, 'Bob', 20) # Original row for ID 2 # (+ , 2, 'Bob Changed', 25) # Modified row for ID 2