Added Pandas Fill NA Scalar Int/Float nodes.

__init__.py

@@ -33,6 +33,8 @@ from .modules.pandas_wrapper.pandas_div_series import PandasDivSeries
 from .modules.pandas_wrapper.pandas_drop_duplicates import PandasDropDuplicates
 from .modules.pandas_wrapper.pandas_dropna import PandasDropNA
 from .modules.pandas_wrapper.pandas_exp import PandasExp
+from .modules.pandas_wrapper.pandas_fillna_scalar_float import PandasFillNAScalarFloat
+from .modules.pandas_wrapper.pandas_fillna_scalar_int import PandasFillNAScalarInt
 from .modules.pandas_wrapper.pandas_group_by import PandasGroupBy
 from .modules.pandas_wrapper.pandas_head import PandasHead
 from .modules.pandas_wrapper.pandas_horizontal_concat import PandasHorizontalConcat
@@ -133,6 +135,8 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasDropDuplicates": PandasDropDuplicates,
     "PandasDropNA": PandasDropNA,
     "PandasExp": PandasExp,
+    "PandasFillNAScalarFloat": PandasFillNAScalarFloat,
+    "PandasFillNAScalarInt": PandasFillNAScalarInt,
     "PandasGroupBy": PandasGroupBy,
     "PandasHead": PandasHead,
     "PandasHorizontalConcat": PandasHorizontalConcat,
@@ -233,6 +237,8 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasDropDuplicates": "Pandas Drop Duplicates",
     "PandasDropNA": "Pandas Drop NA",
     "PandasExp": "Pandas Exp",
+    "PandasFillNAScalarFloat": "Pandas Fill NA Scalar Float",
+    "PandasFillNAScalarInt": "Pandas Fill NA Scalar Int",
     "PandasGroupBy": "Pandas Group By",
     "PandasHead": "Pandas Head",
     "PandasHorizontalConcat": "Pandas Horizontal Concat",

docs/reference/node_reference.md

@@ -38,6 +38,8 @@
 | --- | --- |
 | [Pandas Drop Duplicates](pandas_drop_duplicates.md) | Drops duplicate rows from a Pandas DataFrame. |
 | [Pandas Drop NA](pandas_dropna.md) | Drops missing values from a pandas DataFrame. |
+| [Pandas Fill NA Scalar Float](pandas_fillna_scalar_float.md) | Replaces missing values in a Pandas DataFrame with a floating-point number. |
+| [Pandas Fill NA Scalar Int](pandas_fillna_scalar_int.md) | Replaces missing values in a Pandas DataFrame with an integer. |
 | [Pandas Is NA](pandas_isna.md) | Checks a pandas DataFrame for missing values. |
 | [Pandas Replace](pandas_replace.md) | Replaces part or all of a string in each DataFrame cell with a specified string using a wildcard for matching.  If you want to remove some characters, then leave the replace_string field blank. |
 ## Data subset selection

docs/reference/pandas_fillna_scalar_float.md

@@ -0,0 +1,20 @@
+# 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.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| float_scalar | Float |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Data cleansing
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/pandas_fillna_scalar_int.md

@@ -0,0 +1,18 @@
+# Pandas Fill NA Scalar Int
+Replaces missing values in a Pandas DataFrame with an integer.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| int_scalar | Int |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Data cleansing
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

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,)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.1.13"
+version = "1.1.14"
 license = {file = "LICENSE"}
 
 [project.urls]