Added Pandas Fill NA Scalar Int/Float nodes.

modules/pandas_wrapper/pandas_fillna_scalar_float.py

@@ -0,0 +1,46 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasFillNAScalarFloat:
+    """
+    Pandas Fill NA Scalar Float:
+    Replaces missing values in a Pandas DataFrame with a floating-point number.
+
+    To set the value, use **the CDA Float Create node** to define the floating-point number and connect it to this node's float_scaler input. Alternatively, you can enter the value directly in this node, but it currently supports only one decimal digit.
+
+    category: Data cleansing
+    """
+    
+    @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", {}),
+                "float_scalar": ("FLOAT", {"default": 0.0, "min": -2**31, "max": 2**31})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, float_scalar: float) -> tuple:
+        """
+        Replaces missing values in a Pandas DataFrame with a floating-point number.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+            float_scalar (DataFrame): The scalar value to replace with.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result.
+        """
+        df_out = dataframe.fillna(float_scalar)
+        return (df_out,)

modules/pandas_wrapper/pandas_fillna_scalar_int.py

@@ -0,0 +1,44 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasFillNAScalarInt:
+    """
+    Pandas Fill NA Scalar Int:
+    Replaces missing values in a Pandas DataFrame with an integer.
+
+    category: Data cleansing
+    """
+    
+    @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", {}),
+                "int_scalar": ("INT", {"default": 0, "min": -2**31, "max": 2**31})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, int_scalar: int) -> tuple:
+        """
+        Replaces missing values in a Pandas DataFrame with an integer.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+            int_scalar (DataFrame): The scalar value to replace with.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result.
+        """
+        df_out = dataframe.fillna(int_scalar)
+        return (df_out,)