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