Added integration testcase

Author: Sai Sandeep Ramavath

pandas/tests/test_integration.py

@@ -0,0 +1,72 @@
+"""
+Integration tests for pandas modules.
+
+These tests verify interactions between multiple modules/components:
+- pandas.core.series (Series construction)
+- pandas.core.frame (DataFrame construction)
+- pandas.core.dtypes (dtype handling)
+
+"""
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Series, DataFrame, Index
+from pandas.core.missing import clean_fill_method
+from pandas._libs import lib
+from pandas.util._validators import (
+    validate_args_and_kwargs,
+    validate_fillna_kwargs,
+    check_dtype_backend,
+    validate_percentile,
+)
+
+
+class TestSandeepIntegration:
+    """Integration tests by Sandeep Ramavath covering Series-DataFrame-dtype interactions."""
+    
+    def test_series_to_dataframe_dtype_preservation(self):
+        """Test Series.to_frame() preserves dtype through internals conversion.
+        
+        This exercises interaction between:
+        - pandas.core.series.Series.to_frame()
+        - pandas.core.internals (manager conversion)
+        - pandas.core.frame.DataFrame
+        - pandas.core.dtypes (dtype preservation)
+        """
+        # Create Series with specific dtype
+        s = Series([1, 2, 3], name="test_col", dtype="int32")
+        
+        # Convert to DataFrame - should preserve dtype through internal conversion
+        df = s.to_frame()
+        
+        assert isinstance(df, DataFrame)
+        assert df.columns[0] == "test_col"
+        assert df["test_col"].dtype == np.dtype("int32")
+        assert len(df) == 3
+        assert (df["test_col"] == s).all()
+
+    def test_dataframe_from_dict_mixed_series_dtypes(self):
+        """Test DataFrame construction from dict with mixed Series dtypes.
+        
+        This exercises interaction between:
+        - pandas.core.frame.DataFrame.__init__
+        - pandas.core.internals.construction.dict_to_mgr
+        - pandas.core.series.Series (multiple instances with different dtypes)
+        - pandas.core.dtypes (type coercion and preservation)
+        """
+        # Create Series with different dtypes
+        s1 = Series([1, 2, 3], dtype="int64")
+        s2 = Series([1.0, 2.0, 3.0], dtype="float32")
+        s3 = Series(["a", "b", "c"], dtype="object")
+        
+        # Build DataFrame from dict of Series
+        df = DataFrame({"col1": s1, "col2": s2, "col3": s3})
+        
+        # Verify each column maintains its original dtype
+        assert df["col1"].dtype == np.dtype("int64")
+        assert df["col2"].dtype == np.dtype("float32")
+        assert df["col3"].dtype == np.dtype("object")
+        assert len(df) == 3
+
+