Added Pandas Horizontal Split and Vertical Split nodes.

modules/pandas_wrapper/pandas_horizontal_split.py

@@ -0,0 +1,49 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasHorizontalSplit:
+    """
+    Horizontally splits a Pandas DataFrame into two pandas DataFrames.
+
+    category: Transformation
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe": ("DATAFRAME", {}),
+                "column_integer_position": ("INT", {"default": 0, "min": -1, "max": 2**31})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME", "DATAFRAME")
+    RETURN_NAMES: tuple = ("Left DataFrame", "Right DataFrame")
+
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, column_integer_position: int) -> tuple:
+        """
+        Horizontally splits a Pandas DataFrame into two pandas DataFrames.
+
+        Args:
+            dataframe (pd.DataFrame): The DataFrame to split.
+            column_integer_position (int): The column integer position where the split occurs. 
+                The left DataFrame contains columns [0, column_integer_position-1], 
+                and the right DataFrame contains columns [column_integer_position, end].
+
+        Returns:
+            tuple: A tuple containing the left and right DataFrames.
+        """
+        df_left = dataframe.iloc[:,:column_integer_position]
+        df_right = dataframe.iloc[:,column_integer_position:]
+        
+        return (df_left, df_right)

modules/pandas_wrapper/pandas_vertical_split.py

@@ -0,0 +1,49 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasVerticalSplit:
+    """
+    Vertically splits a Pandas DataFrame into two pandas DataFrames.
+
+    category: Transformation
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe": ("DATAFRAME", {}),
+                "row_integer_position": ("INT", {"default": 0, "min": -1, "max": 2**31})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME", "DATAFRAME")
+    RETURN_NAMES: tuple = ("Top DataFrame", "Bottom DataFrame")
+
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, row_integer_position: int) -> tuple:
+        """
+        Vertically splits a Pandas DataFrame into two pandas DataFrames.
+
+        Args:
+            dataframe (pd.DataFrame): The DataFrame to split.
+            row_integer_position (int): The row integer position where the split occurs. 
+                The top DataFrame contains rows [0, row_integer_position-1], 
+                and the bottom DataFrame contains rows [row_integer_position, end].
+
+        Returns:
+            tuple: A tuple containing the top and bottom DataFrames.
+        """
+        df_top = dataframe.iloc[:row_integer_position]
+        df_bottom = dataframe.iloc[row_integer_position:]
+        
+        return (df_top, df_bottom)