Added system testing Data Loading and Export Workflow

Author: Sai Sandeep Ramavath

pandas/tests/system/test_system_workflows.py

@@ -0,0 +1,73 @@
+"""
+System-level black-box tests for pandas end-to-end workflows.
+
+These tests validate complete user workflows through public APIs only,
+treating the system as a black box without referencing internal implementation.
+
+Test Categories:
+Data Loading and Export Workflow (Sandeep Ramavath)
+"""
+import os
+import tempfile
+import numpy as np
+import pandas as pd
+import pytest
+
+
+class TestDataIOWorkflow:
+    """
+    System tests by Sandeep Ramavath.
+    Validates end-to-end data import/export workflows through public API.
+    """
+    
+    def test_csv_roundtrip_workflow(self, tmp_path):
+        """
+        Test Case: CSV Data Import-Export Workflow
+        
+        Pre-conditions:
+        - Temporary directory available for file operations
+        - pandas library installed and functional
+        
+        Test Steps:
+        1. Create DataFrame with mixed data types using public API
+        2. Export DataFrame to CSV file
+        3. Import CSV file back into new DataFrame
+        4. Verify data integrity and type preservation
+        
+        Expected Results:
+        - CSV file created successfully
+        - Data round-trips without loss
+        - Numeric, string, and datetime types preserved
+        - All values match original dataset
+        """
+        # Step 1: Create DataFrame with mixed types through public API
+        original_data = pd.DataFrame({
+            'id': [1, 2, 3, 4, 5],
+            'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
+            'score': [95.5, 87.3, 92.1, 88.7, 91.4],
+            'date': pd.date_range('2024-01-01', periods=5),
+            'active': [True, False, True, True, False]
+        })
+        
+        # Step 2: Export to CSV using public API
+        csv_path = tmp_path / "test_data.csv"
+        original_data.to_csv(csv_path, index=False)
+        
+        # Verify file exists
+        assert csv_path.exists(), "CSV file should be created"
+        
+        # Step 3: Import from CSV using public API
+        loaded_data = pd.read_csv(csv_path, parse_dates=['date'])
+        
+        # Step 4: Verify data integrity
+        assert len(loaded_data) == 5, "Should load 5 rows"
+        assert list(loaded_data.columns) == ['id', 'name', 'score', 'date', 'active']
+        assert loaded_data['id'].tolist() == [1, 2, 3, 4, 5]
+        assert loaded_data['name'].tolist() == ['Alice', 'Bob', 'Charlie', 'David', 'Eve']
+        assert loaded_data['score'].tolist() == [95.5, 87.3, 92.1, 88.7, 91.4]
+        assert loaded_data['active'].tolist() == [True, False, True, True, False]
+        
+        # Verify datetime parsing
+        assert pd.api.types.is_datetime64_any_dtype(loaded_data['date'])
+
+