diff --git a/__init__.py b/__init__.py
index 3e295b0..0e3ceb0 100644
--- a/__init__.py
+++ b/__init__.py
@@ -73,6 +73,7 @@ from .modules.pandas_wrapper.pandas_eq_scalar_float import PandasEqScalarFloat
 from .modules.pandas_wrapper.pandas_eq_scalar_int import PandasEqScalarInt
 from .modules.pandas_wrapper.pandas_exp import PandasExp
 from .modules.pandas_wrapper.pandas_feature_split_to_numpy import PandasFeatureSplitToNumpy
+from .modules.pandas_wrapper.pandas_feature_split_to_pt import PandasFeatureSplitToPt
 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_ge import PandasGe
@@ -138,6 +139,7 @@ from .modules.pandas_wrapper.pandas_pow_series import PandasPowSeries
 from .modules.pandas_wrapper.pandas_rename import PandasRename
 from .modules.pandas_wrapper.pandas_rename_advanced import PandasRenameAdvanced
 from .modules.pandas_wrapper.pandas_replace import PandasReplace
+from .modules.pandas_wrapper.pandas_replace_advanced import PandasReplaceAdvanced
 from .modules.pandas_wrapper.pandas_save_csv import PandasSaveCSV
 from .modules.pandas_wrapper.pandas_save_json import PandasSaveJSON
 from .modules.pandas_wrapper.pandas_select_column_as_series import PandasSelectColumnAsSeries
@@ -249,6 +251,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasEqScalarInt": PandasEqScalarInt,
     "PandasExp": PandasExp,
     "PandasFeatureSplitToNumpy": PandasFeatureSplitToNumpy,
+    "PandasFeatureSplitToPt": PandasFeatureSplitToPt,
     "PandasFillNAScalarFloat": PandasFillNAScalarFloat,
     "PandasFillNAScalarInt": PandasFillNAScalarInt,
     "PandasGe": PandasGe,
@@ -314,6 +317,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasRename": PandasRename,
     "PandasRenameAdvanced": PandasRenameAdvanced,
     "PandasReplace": PandasReplace,
+    "PandasReplaceAdvanced": PandasReplaceAdvanced,
     "PandasSaveCSV": PandasSaveCSV,
     "PandasSaveJSON": PandasSaveJSON,
     "PandasSelectColumnAsSeries": PandasSelectColumnAsSeries,
@@ -436,6 +440,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasEqScalarInt": "Pandas Eq Scalar Int",
     "PandasExp": "Pandas Exp",
     "PandasFeatureSplitToNumpy": "Pandas Feature Split To Numpy",
+    "PandasFeatureSplitToPt": "Pandas Feature Split To Pt",
     "PandasFillNAScalarFloat": "Pandas Fill NA Scalar Float",
     "PandasFillNAScalarInt": "Pandas Fill NA Scalar Int",
     "PandasGe": "Pandas Ge",
@@ -501,6 +506,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasRename": "Pandas Rename",
     "PandasRenameAdvanced": "Pandas Rename Advanced",
     "PandasReplace": "Pandas Replace",
+    "PandasReplaceAdvanced": "Pandas Replace Advanced",
     "PandasSaveCSV": "Pandas Save CSV",
     "PandasSaveJSON": "Pandas Save JSON",
     "PandasSelectColumnAsSeries": "Pandas Select Column As Series",
diff --git a/docs/reference/node_reference.md b/docs/reference/node_reference.md
index 3a1e5ae..f0d0d08 100644
--- a/docs/reference/node_reference.md
+++ b/docs/reference/node_reference.md
@@ -47,6 +47,7 @@
 | [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. |
+| [Pandas Replace Advanced](pandas_replace_advanced.md) | Replaces strings in a DataFrame cell using a mapping specified in a dictionary. |
 ## Data subset selection
 | Node | Description |
 | --- | --- |
@@ -214,6 +215,7 @@
 | [Pandas At Set Int](pandas_at_set_int.md) | Sets a Python int data in a cell in a pandas DataFrame. |
 | [Pandas At Set String](pandas_at_set_string.md) | Sets a Python string data in a cell in a pandas DataFrame. |
 | [Pandas Feature Split To Numpy](pandas_feature_split_to_numpy.md) | Splits a Pandas DataFrame into feature Numpy ndarray and label Numpy ndarray. |
+| [Pandas Feature Split To Pt](pandas_feature_split_to_pt.md) | Splits a Pandas DataFrame into feature PyTorch tensor and label PyTorch tensor. |
 | [Pandas Horizontal Concat](pandas_horizontal_concat.md) | Horizontally concatenates two pandas DataFrames. |
 | [Pandas Horizontal Split](pandas_horizontal_split.md) | Horizontally splits a Pandas DataFrame into two pandas DataFrames. |
 | [Pandas Iat Set Datetime](pandas_iat_set_datetime.md) | Sets a Python datetime.datetime data in a cell in a pandas DataFrame. |
diff --git a/docs/reference/pandas_feature_split_to_pt.md b/docs/reference/pandas_feature_split_to_pt.md
new file mode 100644
index 0000000..c57ca81
--- /dev/null
+++ b/docs/reference/pandas_feature_split_to_pt.md
@@ -0,0 +1,23 @@
+# Pandas Feature Split To Pt
+Splits a Pandas DataFrame into feature PyTorch tensor and label PyTorch tensor.
+If output_1d_label is set, label will be output as a 1d array instead of a 2d array.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| label_integer_position | Int |
+| output_1d_label | Boolean |
+| feature_data_type |  |
+| label_data_type |  |
+
+## Output
+| Data type |
+|---|
+| Ndarray |
+| Ndarray |
+
+<HR>
+Category: Transformation
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
diff --git a/docs/reference/pandas_replace_advanced.md b/docs/reference/pandas_replace_advanced.md
new file mode 100644
index 0000000..6a66188
--- /dev/null
+++ b/docs/reference/pandas_replace_advanced.md
@@ -0,0 +1,22 @@
+# Pandas Replace Advanced
+Replaces strings in a DataFrame cell using a mapping specified in a dictionary.
+For example, if you want to replace "cat" with "dog" and replace "mountain" with "ocean", specify the mapping in Python dict format:
+```
+{"cat":"dog", "mountain":"ocean"}
+```
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| replacement_dict | String |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Data cleansing
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
diff --git a/modules/pandas_wrapper/pandas_feature_split_to_pt.py b/modules/pandas_wrapper/pandas_feature_split_to_pt.py
new file mode 100644
index 0000000..d24c4c9
--- /dev/null
+++ b/modules/pandas_wrapper/pandas_feature_split_to_pt.py
@@ -0,0 +1,78 @@
+from typing import Any, Dict
+import pandas as pd
+import numpy as np
+import torch
+
+class PandasFeatureSplitToPt:
+    """
+    Splits a Pandas DataFrame into feature PyTorch tensor and label PyTorch tensor.
+    If output_1d_label is set, label will be output as a 1d array instead of a 2d array.
+
+    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", {}),
+                "label_integer_position": ("INT", {"default": -1, "min": -10, "max": 1024}),
+                "output_1d_label": ("BOOLEAN", {"default": True}),
+                "feature_data_type": (("float32","float16", "bloat16", "float64", "int8", "uint8", "int16", "int32", "int64"),),
+                "label_data_type": (("float32","float16", "bloat16", "float64", "int8", "uint8", "int16", "int32", "int64"),)
+            }
+        }
+
+    RETURN_TYPES: tuple = ("TENSOR", "TENSOR")
+    RETURN_NAMES: tuple = ("feature", "label")
+
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame,
+        label_integer_position: int,
+        output_1d_label: bool,
+        feature_data_type: str,
+        label_data_type: str) -> tuple:
+        """
+        Splits a Pandas DataFrame into feature Numpy ndarray and label Numpy ndarray.
+
+        Args:
+            dataframe (pd.DataFrame): The DataFrame to split.
+            label_integer_position (int): The column integer position of the label.
+            output_1d_label (bool): If output_1d_label is set, label will be output as a 1d array instead of a 2d array.
+            feature_data_type (str): Feature data type.
+            label_data_type (str): Label data type.
+        Returns:
+            tuple: A tuple containing the feature and label arrays.
+        """
+        feature_dtype = getattr(torch, feature_data_type)
+        label_dtype = getattr(torch, label_data_type)
+        
+        num_columns = dataframe.shape[1]
+
+        if label_integer_position < 0:
+            label_integer_position += num_columns
+
+        # Validate label_integer_position
+        if label_integer_position >= num_columns:
+            raise ValueError(f"label_integer_position {label_integer_position} is out of range for dataframe with {num_columns} columns.")
+
+        # Extract features
+        np_left = dataframe.iloc[:, :label_integer_position].to_numpy()
+        np_right = dataframe.iloc[:, label_integer_position + 1:].to_numpy() if label_integer_position < num_columns - 1 else np.empty((len(dataframe), 0))
+        np_features = np.hstack([np_left, np_right]) if np_right.shape[1] > 0 else np_left
+
+        # Extract label
+        np_label = dataframe.iloc[:, label_integer_position].to_numpy()
+        if output_1d_label:
+            np_label = np_label.ravel()
+
+        return (torch.tensor(np_features,dtype=feature_dtype),
+                torch.tensor(np_label, dtype=label_dtype))
diff --git a/modules/pandas_wrapper/pandas_replace_advanced.py b/modules/pandas_wrapper/pandas_replace_advanced.py
new file mode 100644
index 0000000..60d7856
--- /dev/null
+++ b/modules/pandas_wrapper/pandas_replace_advanced.py
@@ -0,0 +1,61 @@
+from typing import Any, Dict
+import pandas as pd
+import ast
+
+class PandasReplaceAdvanced:
+    """
+    Replaces strings in a DataFrame cell using a mapping specified in a dictionary.
+    For example, if you want to replace "cat" with "dog" and replace "mountain" with "ocean", specify the mapping in Python dict format:
+    ```
+    {"cat":"dog", "mountain":"ocean"}
+    ```
+    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", {}),
+                "replacement_dict": ("STRING", {"default": "{}", "multiline":True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, replacement_dict: str) -> tuple:
+        """
+        Replaces part or all of a string in each DataFrame cell with a specified string using a wildcard for matching.
+
+        Args:
+            dataframe (DataFrame): The target DataFrame.
+            replacement_dict (str): A dict containing the mapping.
+
+        Returns:
+            tuple: A tuple containing the DataFrame.
+        """
+        d = ast.literal_eval(replacement_dict)
+        if isinstance(d, dict) is False:
+            raise ValueError('You need to specify the mapping in Python dict format such as {"cat":"dog", "mountain":"ocean"}')
+        # check values
+        for k, v in d.items():
+            if (isinstance(k, str) is False and 
+                isinstance(k, float) is False and
+                isinstance(k, int) is False):
+                raise ValueError("You need to specify a string or a number for source values.")
+
+            if (isinstance(v, str) is False and 
+                isinstance(v, float) is False and
+                isinstance(v, int) is False):
+                raise ValueError("You need to specify a string or a number for target values.")
+
+        df2 = dataframe.replace(to_replace=d)
+        return (df2,)
diff --git a/pyproject.toml b/pyproject.toml
index 77dc9da..375db79 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI – Integrates Pandas, Seaborn and Matplotlib into ComfyUI."
-version = "3.0.4"
+version = "3.0.5"
 license = {file = "LICENSE"}
 
 [project.urls]
diff --git a/tests/pandas_wrapper/pandas_feture_split_to_pt_test.py b/tests/pandas_wrapper/pandas_feture_split_to_pt_test.py
new file mode 100644
index 0000000..d1de503
--- /dev/null
+++ b/tests/pandas_wrapper/pandas_feture_split_to_pt_test.py
@@ -0,0 +1,50 @@
+import os
+import sys
+import unittest
+import numpy as np
+import pandas as pd
+import torch
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+
+from pandas_wrapper.pandas_feature_split_to_pt import PandasFeatureSplitToPt
+
+class TestPandasFeatureSplitToPt(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.node = PandasFeatureSplitToPt()
+
+        # Sample DataFrame
+        self.df = pd.DataFrame({
+            'A': [1, 2, 3],
+            'B': [4, 5, 6],
+            'C': [7, 8, 9]
+        })
+
+    def test_feature_split(self):
+        """Test splitting with different label positions."""
+        test_cases = [
+            (0, True, [[4, 7], [5, 8], [6, 9]], [1, 2, 3]),
+            (1, True, [[1, 7], [2, 8], [3, 9]], [4, 5, 6]),
+            (2, True, [[1, 4], [2, 5], [3, 6]], [7, 8, 9]),
+            (-1, True, [[1, 4], [2, 5], [3, 6]], [7, 8, 9]),
+        ]
+        
+        data_types = ["float32", "float16", "bfloat16", "float64", "int8", "uint8", "int16", "int32", "int64"]
+        for feature_dtype in data_types:
+            for label_dtype in data_types:
+                for label_pos, output_1d, expected_features, expected_labels in test_cases:
+                    with self.subTest(label_pos=label_pos, output_1d=output_1d, feature_dtype=feature_dtype, label_dtype=label_dtype):
+                        features, labels = self.node.f(self.df, label_pos, output_1d, feature_dtype, label_dtype)
+                        torch.testing.assert_close(features, torch.tensor(expected_features, dtype=getattr(torch, feature_dtype)))
+                        torch.testing.assert_close(labels, torch.tensor(expected_labels, dtype=getattr(torch, label_dtype)))
+
+    def test_invalid_label_position(self):
+        """Test invalid label positions."""
+        with self.assertRaises(ValueError):
+            self.node.f(self.df, 3, True, "float32", "int64")
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/pandas_wrapper/pandas_replace_advanced_test.py b/tests/pandas_wrapper/pandas_replace_advanced_test.py
new file mode 100644
index 0000000..34346d1
--- /dev/null
+++ b/tests/pandas_wrapper/pandas_replace_advanced_test.py
@@ -0,0 +1,60 @@
+import os
+import sys
+import unittest
+import pandas as pd
+import ast
+from typing import Any, Dict
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+
+from pandas_wrapper.pandas_replace_advanced import PandasReplaceAdvanced
+
+
+class TestPandasReplaceAdvanced(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.node = PandasReplaceAdvanced()
+        cls.df = pd.DataFrame(
+            {
+                "col1": ["cat", "dog", "fish"],
+                "col2": ["mountain", "ocean", "forest"],
+            }
+        )
+
+    def test_valid_replacement(self):
+        test_cases = [
+            ('{"cat": 1, "dog": 2.34}', {"col1": [1, 2.34, "fish"], "col2": ["mountain", "ocean", "forest"]}),
+            ('{"mountain": "hill", "forest": "jungle"}', {"col1": ["cat", "dog", "fish"], "col2": ["hill", "ocean", "jungle"]}),
+        ]
+        for replacements, expected_data in test_cases:
+            with self.subTest(replacements=replacements):
+                result_df = self.node.f(self.df, replacements)[0]
+                expected_df = pd.DataFrame(expected_data)
+                pd.testing.assert_frame_equal(result_df, expected_df)
+
+    def test_invalid_replacement_value_error(self):
+        invalid_cases = [
+            '["cat", "lion"]',  # List is not allowed
+            '{"cat": ["lion", "tiger"]}',  # List is not allowed
+            '{"dog": {"wolf": "coyote"}}'  # Nested dict is not allowed
+        ]
+        for invalid_pairs in invalid_cases:
+            with self.subTest(invalid_pairs=invalid_pairs):
+                with self.assertRaises(ValueError):
+                    self.node.f(self.df, invalid_pairs)
+
+    def test_invalid_replacement_syntax_error(self):
+        invalid_cases = [
+            '{"mountain": ()"hill", "ocean")}',  # Syntax error
+        ]
+        for invalid_pairs in invalid_cases:
+            with self.subTest(invalid_pairs=invalid_pairs):
+                with self.assertRaises(SyntaxError):
+                    self.node.f(self.df, invalid_pairs)
+
+
+
+if __name__ == "__main__":
+    unittest.main()