feat: introduces DataSet class to improve the experience with lists of Data (#4834)

* Add DataSet class to handle conversion between Data objects and DataFrame

* feat: enhance DataSet class with detailed docstrings and examples for better usability

* feat: add custom constructor property to DataSet for improved DataFrame compatibility

* test: add unit tests for DataSet class methods and functionality

src/backend/base/langflow/schema/data_set.py

@@ -0,0 +1,103 @@
+import pandas as pd
+
+from langflow.schema.data import Data
+
+
+class DataSet(pd.DataFrame):
+    """A pandas DataFrame subclass specialized for handling collections of Data objects.
+
+    This class extends pandas.DataFrame to provide seamless integration between
+    Langflow's Data objects and pandas' powerful data manipulation capabilities.
+
+    Key Features:
+        - Direct initialization from a list of Data objects
+        - Maintains all pandas DataFrame functionality
+        - Conversion back to Data objects when needed
+
+    Notes:
+        - Nested dictionaries within Data objects are preserved in their column representation
+        - All pandas DataFrame operations (groupby, merge, concat, etc.) remain available
+        - Column dtypes are inferred from the Data objects' contents
+
+    Examples:
+        >>> data_objects = [
+        ...     Data(data={"name": "John", "age": 30}),
+        ...     Data(data={"name": "Jane", "age": 25})
+        ... ]
+        >>> dataset = DataSet.from_data_list(data_objects)
+        >>> dataset['age'].mean()
+        27.5
+        >>> original_data = dataset.to_data_list()
+
+    Inheritance:
+        This class inherits all functionality from pandas.DataFrame, meaning any
+        operation that works on a DataFrame will work on a DataSet:
+        - Filtering: dataset[dataset['age'] > 25]
+        - Aggregation: dataset.groupby('category').mean()
+        - Statistical operations: dataset.describe()
+        - etc.
+    """
+
+    @classmethod
+    def from_data_list(cls, data_list: list[Data]) -> "DataSet":
+        """Creates a DataSet from a list of Data objects.
+
+        This method converts a list of Data objects into a DataFrame structure,
+        preserving all data from the original Data objects.
+
+        Args:
+            data_list (list[Data]): A list of Data objects to convert into a DataFrame.
+                Each Data object's internal dictionary becomes a row in the DataFrame.
+
+        Returns:
+            DataSet: A new DataSet instance containing all data from the input list.
+
+        Examples:
+            >>> data_objects = [
+            ...     Data(data={"name": "John", "age": 30}),
+            ...     Data(data={"name": "Jane", "age": 25})
+            ... ]
+            >>> dataset = DataSet.from_data_list(data_objects)
+            >>> print(dataset.columns)
+            Index(['name', 'age'], dtype='object')
+
+        Notes:
+            - Column names are derived from the keys in the Data objects
+            - If Data objects have different keys, the resulting DataFrame will have
+              NaN values for missing data
+            - The original structure of nested data is preserved in the DataFrame
+        """
+        data_dicts = [d.data for d in data_list]
+        return cls(data_dicts)
+
+    def to_data_list(self) -> list[Data]:
+        """Converts the DataSet back to a list of Data objects.
+
+        This method transforms each row of the DataFrame back into a Data object,
+        reconstructing the original data structure.
+
+        Returns:
+            list[Data]: A list of Data objects, where each object corresponds to
+                a row in the DataFrame.
+
+        Examples:
+            >>> dataset = DataSet({'name': ['John'], 'age': [30]})
+            >>> data_objects = dataset.to_data_list()
+            >>> print(data_objects[0].data)
+            {'name': 'John', 'age': 30}
+
+        Notes:
+            - Each row is converted to a dictionary using to_dict()
+            - The resulting Data objects will contain all columns as keys in their
+              internal dictionary
+            - Any modifications made to the DataFrame will be reflected in the
+              resulting Data objects
+        """
+        return [Data(data=row.to_dict()) for _, row in self.iterrows()]
+
+    @property
+    def _constructor(self):
+        def _c(*args, **kwargs):
+            return DataSet(*args, **kwargs).__finalize__(self)
+
+        return _c