feat: add DataToDataFrame component for converting Data objects

src/backend/base/langflow/components/processing/data_to_dataframe.py

@@ -0,0 +1,68 @@
+from langflow.custom import Component
+from langflow.io import DataInput, Output
+from langflow.schema import Data, DataFrame
+
+
+class DataToDataFrameComponent(Component):
+    display_name = "Data → DataFrame"
+    description = (
+        "Converts one or multiple Data objects into a DataFrame. "
+        "Each Data object corresponds to one row. Fields from `.data` become columns, "
+        "and the `.text` (if present) is placed in a 'text' column."
+    )
+    icon = "table"
+    name = "DataToDataFrame"
+
+    inputs = [
+        DataInput(
+            name="data_list",
+            display_name="Data or Data List",
+            info="One or multiple Data objects to transform into a DataFrame.",
+            is_list=True,
+        ),
+    ]
+
+    outputs = [
+        Output(
+            display_name="DataFrame",
+            name="dataframe",
+            method="build_dataframe",
+            info="A DataFrame built from each Data object's fields plus a 'text' column.",
+        ),
+    ]
+
+    def build_dataframe(self) -> DataFrame:
+        """Builds a DataFrame from Data objects by combining their fields.
+
+        For each Data object:
+          - Merge item.data (dictionary) as columns
+          - If item.text is present, add 'text' column
+
+        Returns a DataFrame with one row per Data object.
+        """
+        data_input = self.data_list
+
+        # If user passed a single Data, it might come in as a single object rather than a list
+        if not isinstance(data_input, list):
+            data_input = [data_input]
+
+        rows = []
+        for item in data_input:
+            if not isinstance(item, Data):
+                msg = f"Expected Data objects, got {type(item)} instead."
+                raise TypeError(msg)
+
+            # Start with a copy of item.data or an empty dict
+            row_dict = dict(item.data) if item.data else {}
+
+            # If the Data object has text, store it under 'text' col
+            text_val = item.get_text()
+            if text_val:
+                row_dict["text"] = text_val
+
+            rows.append(row_dict)
+
+        # Build a DataFrame from these row dictionaries
+        df_result = DataFrame(rows)
+        self.status = df_result  # store in self.status for logs
+        return df_result

src/backend/tests/unit/components/processing/test_data_to_dataframe.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