Added Pandas Feature Split To Pt and Pandas Replace Advanced nodes.

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))

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,)