feat: add DataToDataFrame component for converting Data objects (#6112)

* ✨ (data_to_dataframe.py): add a new component to convert Data objects into a DataFrame for easier data manipulation and analysis.

* [autofix.ci] apply automated fixes

* 📝 (data_to_dataframe.py): improve documentation for the build_dataframe method to explain the process of building a DataFrame from Data objects

* ✨ (test_data_to_dataframe.py): Add unit tests for DataToDataFrameComponent to ensure proper construction of DataFrame from Data objects with various fields and configurations.

* ✨ (test_data_to_dataframe.py): Refactor test_data_to_dataframe.py to use pandas module instead of turtle for DataFrame operations
♻️ (test_data_to_dataframe.py): Refactor test_data_to_dataframe.py to improve readability and consistency in DataFrame testing assertions

* [autofix.ci] apply automated fixes

* 🔧 (test_data_to_dataframe.py): improve variable naming for better readability and consistency in test cases

* [autofix.ci] apply automated fixes

* ✨ (test_data_to_dataframe_component.py): Add unit tests for DataToDataFrameComponent to ensure correct behavior and functionality.

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>

src/backend/tests/unit/components/processing/test_data_to_dataframe_component.py

@@ -0,0 +1,127 @@
+import pytest
+from langflow.components.processing.data_to_dataframe import DataToDataFrameComponent
+from langflow.schema import Data, DataFrame
+
+from tests.base import ComponentTestBaseWithoutClient
+
+
+class TestDataToDataFrameComponent(ComponentTestBaseWithoutClient):
+    @pytest.fixture
+    def component_class(self):
+        """Return the component class to test."""
+        return DataToDataFrameComponent
+
+    @pytest.fixture
+    def default_kwargs(self):
+        """Return the default kwargs for the component."""
+        return {
+            "data_list": [
+                Data(text="Row 1", data={"field1": "value1", "field2": 1}),
+                Data(text="Row 2", data={"field1": "value2", "field2": 2}),
+            ]
+        }
+
+    @pytest.fixture
+    def file_names_mapping(self):
+        """Return the file names mapping for different versions."""
+        # This is a new component, so we return an empty list
+        return []
+
+    def test_basic_setup(self, component_class, default_kwargs):
+        """Test basic component initialization."""
+        component = component_class()
+        component.set_attributes(default_kwargs)
+        assert component.data_list == default_kwargs["data_list"]
+
+    def test_build_dataframe_basic(self, component_class, default_kwargs):
+        """Test basic DataFrame construction."""
+        component = component_class()
+        component.set_attributes(default_kwargs)
+        result_df = component.build_dataframe()
+
+        assert isinstance(result_df, DataFrame)
+        assert len(result_df) == 2
+        assert list(result_df.columns) == ["field1", "field2", "text"]
+        assert result_df["text"].tolist() == ["Row 1", "Row 2"]
+        assert result_df["field1"].tolist() == ["value1", "value2"]
+        assert result_df["field2"].tolist() == [1, 2]
+
+    def test_single_data_input(self, component_class):
+        """Test handling single Data object input."""
+        single_data = Data(text="Single Row", data={"field1": "value"})
+        component = component_class()
+        component.set_attributes({"data_list": single_data})
+
+        result_df = component.build_dataframe()
+
+        assert len(result_df) == 1
+        assert result_df["text"].iloc[0] == "Single Row"
+        assert result_df["field1"].iloc[0] == "value"
+
+    def test_empty_data_list(self, component_class):
+        """Test behavior with empty data list."""
+        component = component_class()
+        component.set_attributes({"data_list": []})
+
+        result_df = component.build_dataframe()
+
+        assert len(result_df) == 0
+
+    def test_data_without_text(self, component_class):
+        """Test handling Data objects without text field."""
+        data_without_text = [Data(data={"field1": "value1"}), Data(data={"field1": "value2"})]
+        component = component_class()
+        component.set_attributes({"data_list": data_without_text})
+
+        result_df = component.build_dataframe()
+
+        assert len(result_df) == 2
+        assert "text" not in result_df.columns
+        assert result_df["field1"].tolist() == ["value1", "value2"]
+
+    def test_data_without_data_dict(self, component_class):
+        """Test handling Data objects without data dictionary."""
+        data_without_dict = [Data(text="Text 1"), Data(text="Text 2")]
+        component = component_class()
+        component.set_attributes({"data_list": data_without_dict})
+
+        result_df = component.build_dataframe()
+
+        assert len(result_df) == 2
+        assert list(result_df.columns) == ["text"]
+        assert result_df["text"].tolist() == ["Text 1", "Text 2"]
+
+    def test_mixed_data_fields(self, component_class):
+        """Test handling Data objects with different fields."""
+        mixed_data = [
+            Data(text="Row 1", data={"field1": "value1", "field2": 1}),
+            Data(text="Row 2", data={"field1": "value2", "field3": "extra"}),
+        ]
+        component = component_class()
+        component.set_attributes({"data_list": mixed_data})
+
+        result_df = component.build_dataframe()
+
+        assert len(result_df) == 2
+        assert set(result_df.columns) == {"field1", "field2", "field3", "text"}
+        assert result_df["field1"].tolist() == ["value1", "value2"]
+        assert result_df["field2"].iloc[1] != result_df["field2"].iloc[1]  # Check for NaN using inequality
+        assert result_df["field3"].iloc[0] != result_df["field3"].iloc[0]  # Check for NaN using inequality
+
+    def test_invalid_input_type(self, component_class):
+        """Test error handling for invalid input types."""
+        invalid_data = [{"not": "a Data object"}]
+        component = component_class()
+        component.set_attributes({"data_list": invalid_data})
+
+        with pytest.raises(TypeError) as exc_info:
+            component.build_dataframe()
+        assert "Expected Data objects" in str(exc_info.value)
+
+    def test_status_update(self, component_class, default_kwargs):
+        """Test that status is properly updated."""
+        component = component_class()
+        component.set_attributes(default_kwargs)
+        result = component.build_dataframe()
+
+        assert component.status is result  # Status should be set to the DataFrame