Major refactoring to pass DataFrame object without serializing. Almost all nodes were modified for this. Binary operator nodes were added as well.

modules/cda/cda_json_create.py

@@ -21,7 +21,7 @@ class CDAJSONCreate:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "create"
     CATEGORY: str = "Data Analysis"
 

modules/matplotlib/common.py

@@ -7,7 +7,7 @@ def common_input_types():
     """
     return {
         "required": {
-            "dataframe_json": ("STRING", {"multiscatter": True}),
+            "dataframe": ("STRING", {"multiscatter": True}),
             "x_column_name": ("STRING", {"default": ""}),
             "y_column_name": ("STRING", {"default": ""}),
             "title": ("STRING", {"default": ""}),

modules/matplotlib/mpl_bar.py

@@ -26,7 +26,7 @@ class MPLBar:
     CATEGORY: str = "Data Analysis"
 
     def bar_chart(self,
-                  dataframe_json: str,
+                  dataframe: pd.DataFrame,
                   x_column_name: str,
                   y_column_name: str,
                   title: str,
@@ -37,7 +37,7 @@ class MPLBar:
         Generates a bar chart from a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             x_column_name (str): The column name for the x-axis.
             y_column_name (str): The column name for the y-axis.
             title (str): The title of the plot.
@@ -49,8 +49,8 @@ class MPLBar:
         Returns:
             tuple: A tuple containing the image tensor representation of the plot.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
+
+        df = dataframe
 
         # Create the plot
         fig, ax = plt.subplots()

modules/matplotlib/mpl_line.py

@@ -26,7 +26,7 @@ class MPLLine:
     CATEGORY: str = "Data Analysis"
 
     def line_plot(self,
-                  dataframe_json: str,
+                  dataframe: pd.DataFrame,
                   x_column_name: str,
                   y_column_name: str,
                   title: str,
@@ -37,7 +37,7 @@ class MPLLine:
         Generates a line plot from a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             x_column_name (str): The column name for the x-axis.
             y_column_name (str): The column name for the y-axis.
             title (str): The title of the plot.
@@ -49,8 +49,8 @@ class MPLLine:
         Returns:
             tuple: A tuple containing the image tensor representation of the plot.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
+
+        df = dataframe
 
         # Create the plot
         fig, ax = plt.subplots()

modules/matplotlib/mpl_scatter.py

@@ -26,7 +26,7 @@ class MPLScatter:
     CATEGORY: str = "Data Analysis"
 
     def scatter_plot(self,
-                  dataframe_json: str,
+                  dataframe: pd.DataFrame,
                   x_column_name: str,
                   y_column_name: str,
                   title: str,
@@ -37,7 +37,7 @@ class MPLScatter:
         Generates a scatter plot from a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             x_column_name (str): The column name for the x-axis.
             y_column_name (str): The column name for the y-axis.
             title (str): The title of the plot.
@@ -49,8 +49,8 @@ class MPLScatter:
         Returns:
             tuple: A tuple containing the image tensor representation of the plot.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
+
+        df = dataframe
 
         # Create the plot
         fig, ax = plt.subplots()

modules/pandas_wrapper/pandas_add.py

@@ -0,0 +1,44 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasAdd:
+    """
+    PandasAdd:
+        A class for adding two pandas DataFrames.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_dataframe": ("DATAFRAME", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+        """
+        Add two pandas DataFrames.
+
+        Args:
+            a_dataframe (DataFrame): The left DataFrame.
+            b_dataframe (DataFrame): The right DataFrame.
+            on_column_name (str): The name of the column to join on.
+            join_method (str): The type of join to perform ("inner", "left", "right", "outer").
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of addition.
+        """
+        df_out = a_dataframe.add(b_dataframe)
+        return (df_out,)

modules/pandas_wrapper/pandas_add_series.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+import pandas as pd
+
+class PandasAddSeries:
+    """
+    PandasAdd:
+        A class for adding a Pandas Series to a pandas DataFrames.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_series": ("PDSERIES", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_series: pd.Series) -> tuple:
+        """
+        Add two pandas DataFrames.
+
+        Args:
+            a_dataframe (DataFrame): The left DataFrame.
+            b_series (Series): The right Series.
+            on_column_name (str): The name of the column to join on.
+            join_method (str): The type of join to perform ("inner", "left", "right", "outer").
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of addition.
+        """
+        df_out = a_dataframe.add(b_series)
+        return (df_out,)

modules/pandas_wrapper/pandas_as_float.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasAsFloat:
+    """
+    PandasAsFloat:
+        A class for converting all cells in a pandas DataFrame to float type.
+    """
+    
+    @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", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame) -> tuple:
+        """
+        Converts all cells in the DataFrame to float type.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame with all values as floats.
+        """
+        # Convert all values to float, non-numeric values become NaN
+        df = dataframe.apply(pd.to_numeric, errors='coerce').astype(float)
+        return (df,)

modules/pandas_wrapper/pandas_as_int.py

@@ -0,0 +1,42 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasAsInt:
+    """
+    PandasAsInt:
+        A class for converting all cells in a pandas DataFrame to integer type.
+    """
+    
+    @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", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame) -> tuple:
+        """
+        Converts all cells in the DataFrame to integer type.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame with all values as integers.
+        """
+        # Convert all values to integer, non-numeric values become NaN
+        df = dataframe.apply(pd.to_numeric, errors='coerce').astype('Int64')
+        return (df.to_json(),)

modules/pandas_wrapper/pandas_as_string.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasAsString:
+    """
+    PandasAsString:
+        A class for converting all cells in a pandas DataFrame to string type.
+    """
+    
+    @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", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "convert_to_string"
+    CATEGORY: str = "Data Analysis"
+
+    def convert_to_string(self, dataframe: pd.DataFrame) -> tuple:
+        """
+        Converts all cells in the DataFrame to string type.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame with all values as strings.
+        """
+        # Convert all values to string
+        df = dataframe.astype(str)
+        return (df,)

modules/pandas_wrapper/pandas_columns.py

@@ -1,14 +1,13 @@
 from typing import Any, Dict
-from io import StringIO
 import pandas as pd
-from .utils import index_to_jsons
+
 
 class PandasColumns:
     """
     PandasColumns:
         A class for retrieving the column labels of a pandas DataFrame.
     """
-    
+
     @classmethod
     def INPUT_TYPES(cls) -> Dict[str, Any]:
         """
@@ -19,25 +18,23 @@ class PandasColumns:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDINDEX",)
     FUNCTION: str = "columns"
     CATEGORY: str = "Data Analysis"
 
-    def columns(self, dataframe_json: str) -> tuple:
+    def columns(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns the column labels of a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the serialized pandas.Index containing columns.
+            tuple: A tuple containing a pandas.Index for columns.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        columns = df.columns
-        return (index_to_jsons(columns),)
+        columns = dataframe.columns
+        return (columns,)

modules/pandas_wrapper/pandas_count.py

@@ -19,24 +19,23 @@ class PandasCount:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with count for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.count()),)
+        df = dataframe
+        return (df.count(),)

modules/pandas_wrapper/pandas_create.py

@@ -23,7 +23,7 @@ class PandasCreate:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "create"
     CATEGORY: str = "Data Analysis"
 
@@ -39,8 +39,4 @@ class PandasCreate:
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(StringIO(data))
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_create_with_index.py

@@ -25,7 +25,7 @@ class PandasCreateWithIndex:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "create"
     CATEGORY: str = "Data Analysis"
 
@@ -43,8 +43,4 @@ class PandasCreateWithIndex:
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(StringIO(data), index_col=index_col)
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_div.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasDiv:
+    """
+    PandasDiv:
+        A class for dividing two pandas DataFrames.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_dataframe": ("DATAFRAME", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+        """
+        Divide two pandas DataFrames.
+
+        Args:
+            a_dataframe (DataFrame): The left DataFrame.
+            b_dataframe (DataFrame): The right DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of division.
+        """
+        df_out = a_dataframe.div(b_dataframe)
+        return (df_out,)

modules/pandas_wrapper/pandas_div_series.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import jsons_to_series
+
+class PandasDivSeries:
+    """
+    PandasDivSeries:
+        A class for dividing a Pandas DataFrame by a Pandas Series.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_series": ("PDSERIES", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_series: pd.Series) -> tuple:
+        """
+        Divide a pandas DataFrame by a pandas Series.
+
+        Args:
+            a_dataframe (DataFrame): The numerator DataFrame.
+            b_series (Series): The denominator Series.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of division.
+        """
+        df_out = a_dataframe.div(b_series)
+        return (df_out,)

modules/pandas_wrapper/pandas_dropna.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict
-from io import StringIO
 import pandas as pd
-from .utils import index_to_jsons
 
 class PandasDropNA:
     """
@@ -19,24 +17,22 @@ class PandasDropNA:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "dropna"
     CATEGORY: str = "Data Analysis"
 
-    def dropna(self, dataframe_json: str) -> tuple:
+    def dropna(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a DataFrame with missing values removed.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the DataFrame.
+            tuple: A tuple containing the DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (df.dropna().to_json(),)
+        return (dataframe.dropna().to_json(),)

modules/pandas_wrapper/pandas_group_by.py

@@ -20,46 +20,43 @@ class PandasGroupBy:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "column_name": ("STRING", {"default": ""}),
                 "aggragate_function":(("sum", "mean", "count", "std", "min", "max"),)
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "groupby_dataframes"
     CATEGORY: str = "Data Analysis"
 
     def groupby_dataframes(self,
-                           dataframe_json: str,
+                           dataframe: pd.DataFrame,
                            column_name: str,
                            aggragate_function: str) -> tuple:
         """
         Compute aggregate values for groups in a DataFrame
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             column_name (str): The name of the column for use as groups.
             aggregate_function (str): The type of aggregate function to perform.
 
         Returns:
             tuple: A tuple containing a JSON string of the merged DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-
         if aggragate_function == "max":
-            df_out = df.groupby(column_name).max()
+            df_out = dataframe.groupby(column_name).max()
         elif aggragate_function == "min":
-            df_out = df.groupby(column_name).min()
+            df_out = dataframe.groupby(column_name).min()
         elif aggragate_function == "std":
-            df_out = df.groupby(column_name).std()
+            df_out = dataframe.groupby(column_name).std()
         elif aggragate_function == "count":
-            df_out = df.groupby(column_name).count()
+            df_out = dataframe.groupby(column_name).count()
         elif aggragate_function == "sum":
-            df_out = df.groupby(column_name).sum()
+            df_out = dataframe.groupby(column_name).sum()
         elif aggragate_function == "mean":
-            df_out = df.groupby(column_name).mean()
+            df_out = dataframe.groupby(column_name).mean()
         else:
             raise ValueError("Unsupported aggregate function.")
-        return (df_out.to_json(),)
+        return (df_out,)

modules/pandas_wrapper/pandas_head.py

@@ -19,27 +19,25 @@ class PandasHead:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "rows": ("INT", {"default": 10, "min": 0, "max": 50})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "head"
     CATEGORY: str = "Data Analysis"
 
-    def head(self, dataframe_json: str, rows: int) -> tuple:
+    def head(self, dataframe: pd.DataFrame, rows: int) -> tuple:
         """
         Returns the first `rows` rows of a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             rows (int): The number of rows to retrieve.
 
         Returns:
             tuple: A tuple containing a JSON string of the resulting DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        df2 = df.head(rows)
-        return (df2.to_json(),)
+        df2 = dataframe.head(rows)
+        return (df2,)

modules/pandas_wrapper/pandas_iloc_row_series.py

@@ -21,30 +21,28 @@ class PandasIlocRowSeries:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "row_integer_position": ("INT", {"default": 0, "min": 0, "max": 2**31})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "select_row"
     CATEGORY: str = "Data Analysis"
 
-    def select_row(self, dataframe_json: str,
+    def select_row(self, dataframe: pd.DataFrame,
              row_integer_position: int) -> tuple:
         """
         Selects a specific row using a row label (row index) from a pandas DataFrame,
         and returns the JSON serialized Series object.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             row_integer_position (int): The row integer position.
 
         Returns:
             tuple: A tuple containing the value of the row in string. The string is
                    a JSON string containing the serialized Pandas Series object.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        value = df.iloc[row_integer_position]
-        return (series_to_jsons(value),)
+        value = dataframe.iloc[row_integer_position]
+        return (value,)

modules/pandas_wrapper/pandas_iloc_rows_dataframe.py

@@ -21,16 +21,16 @@ class PandasIlocRowsDataFrame:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "row_int_pos_list_json": ("STRING", {"multiline": False})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "select_rows"
     CATEGORY: str = "Data Analysis"
 
-    def select_rows(self, dataframe_json: str,
+    def select_rows(self, dataframe: pd.DataFrame,
              row_int_pos_list_json: str) -> tuple:
         """
         Select rows from a pandas DataFrame and returning it as a DataFrame.
@@ -38,15 +38,13 @@ class PandasIlocRowsDataFrame:
         the JSON serialized DataFrame object.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             row_int_pos_list_json (str): A list containing row integer positions.
 
         Returns:
             tuple: A tuple containing the value of the row in string. The string is
                    a JSON string containing the serialized Pandas Series object.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
         row_int_pos_list = json.loads(row_int_pos_list_json)
-        df2 = df.iloc[row_int_pos_list]
-        return (df2.to_json(),)
+        df2 = dataframe.iloc[row_int_pos_list]
+        return (df2,)

modules/pandas_wrapper/pandas_index.py

@@ -1,7 +1,5 @@
 from typing import Any, Dict
-from io import StringIO
 import pandas as pd
-from .utils import index_to_jsons
 
 class PandasIndex:
     """
@@ -19,25 +17,23 @@ class PandasIndex:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDINDEX",)
     FUNCTION: str = "row_index"
     CATEGORY: str = "Data Analysis"
 
-    def row_index(self, dataframe_json: str) -> tuple:
+    def row_index(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns the row labels (index) of a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the serialized pandas.Index containing row labels.
+            tuple: A tuple containing a pandas.Index for row labels.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        row_index = df.index
-        return (index_to_jsons(row_index),)
+        row_index = dataframe.index
+        return (row_index,)

modules/pandas_wrapper/pandas_index_to_string.py

@@ -0,0 +1,39 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasIndexToString:
+    """
+    PandasIndexToString:
+        A class for converting a pandas Index to a string representation.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `to_string` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "index": ("PDINDEX", {}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, ind: pd.Index) -> tuple:
+        """
+        Converts a Pandas Index to a string representation.
+
+        Args:
+            ind (pd.Index): The index.
+
+        Returns:
+            tuple: A tuple containing the string representation of the Index.
+        """
+        return (str(ind),)

modules/pandas_wrapper/pandas_isna.py

@@ -1,7 +1,6 @@
 from typing import Any, Dict
-from io import StringIO
 import pandas as pd
-from .utils import index_to_jsons
+
 
 class PandasIsNA:
     """
@@ -19,24 +18,22 @@ class PandasIsNA:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "isna"
     CATEGORY: str = "Data Analysis"
 
-    def isna(self, dataframe_json: str) -> tuple:
+    def isna(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a DataFrame with boolean values with True indicating a missing value.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the DataFrame.
+            tuple: A tuple containing the DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (df.isna().to_json(),)
+        return (dataframe.isna(),)

modules/pandas_wrapper/pandas_join.py

@@ -19,33 +19,29 @@ class PandasJoin:
         """
         return {
             "required": {
-                "left_dataframe_json": ("STRING", {"multiline": True}),
-                "right_dataframe_json": ("STRING", {"multiline": True}),
+                "left_dataframe": ("DATAFRAME", {}),
+                "right_dataframe": ("DATAFRAME", {}),
                 "on_column_name": ("STRING", {"default": ""}),
                 "join_method":(("inner", "left", "right", "outer"),)
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "join_dataframes"
     CATEGORY: str = "Data Analysis"
 
-    def join_dataframes(self, left_dataframe_json: str, right_dataframe_json: str, on_column_name: str, join_method: str) -> tuple:
+    def join_dataframes(self, left_dataframe: pd.DataFrame, right_dataframe: pd.DataFrame, on_column_name: str, join_method: str) -> tuple:
         """
         Merges two pandas DataFrames based on a common column using the specified join method.
 
         Args:
-            left_dataframe_json (str): A JSON string representation of the left DataFrame.
-            right_dataframe_json (str): A JSON string representation of the right DataFrame.
+            left_dataframe (DataFrame): The left DataFrame.
+            right_dataframe (DataFrame): The right DataFrame.
             on_column_name (str): The name of the column to join on.
             join_method (str): The type of join to perform ("inner", "left", "right", "outer").
 
         Returns:
             tuple: A tuple containing a JSON string of the merged DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df_left = pd.read_json(StringIO(left_dataframe_json))
-        df_right = pd.read_json(StringIO(right_dataframe_json))
-
-        df_out = pd.merge(df_left, df_right, on=on_column_name, how=join_method)
-        return (df_out.to_json(),)
+        df_out = pd.merge(left_dataframe, right_dataframe, on=on_column_name, how=join_method)
+        return (df_out,)

modules/pandas_wrapper/pandas_load_csv.py

@@ -23,7 +23,7 @@ class PandasLoadCSV:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "load_csv"
     CATEGORY: str = "Data Analysis"
 
@@ -39,8 +39,4 @@ class PandasLoadCSV:
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path)
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_load_csv_with_encoding.py

@@ -24,7 +24,7 @@ class PandasLoadCSVWithEncoding:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "load_csv"
     CATEGORY: str = "Data Analysis"
 
@@ -44,8 +44,4 @@ class PandasLoadCSVWithEncoding:
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path, encoding=encoding)
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_load_csv_with_index.py

@@ -25,7 +25,7 @@ class PandasLoadCSVWithIndex:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "load_csv"
     CATEGORY: str = "Data Analysis"
 
@@ -42,8 +42,4 @@ class PandasLoadCSVWithIndex:
         """
         # Read CSV file into DataFrame
         df = pd.read_csv(file_path, index_col=index_col)
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_load_json.py

@@ -23,7 +23,7 @@ class PandasLoadJSON:
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "load_json"
     CATEGORY: str = "Data Analysis"
 
@@ -39,8 +39,4 @@ class PandasLoadJSON:
         """
         # Read JSON file into DataFrame
         df = pd.read_json(file_path)
-
-        # Convert DataFrame to JSON
-        result_json = df.to_json()
-
-        return (result_json,)
+        return (df,)

modules/pandas_wrapper/pandas_loc_cell_str.py

@@ -18,7 +18,7 @@ class PandasLocCellStr:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "row_index": ("STRING", {"default": ""}),
                 "row_index_type":(("string", "int"),),
                 "column_label": ("STRING", {"default": ""}),
@@ -30,7 +30,7 @@ class PandasLocCellStr:
     FUNCTION: str = "cell"
     CATEGORY: str = "Data Analysis"
 
-    def cell(self, dataframe_json: str,
+    def cell(self, dataframe: pd.DataFrame,
              row_index: str,
              row_index_type: str,
              column_label: str,
@@ -39,7 +39,7 @@ class PandasLocCellStr:
         Selects specific cell from a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             row_index (str): The row index (row label) for the cell.
             row_index_type (str): The data type of the row index.
             column_label (str): The column label for the cell.
@@ -48,10 +48,8 @@ class PandasLocCellStr:
         Returns:
             tuple: A tuple containing the value of the cell in string.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
         row_index = int(row_index) if row_index_type == "int" else row_index
         column_label = int(column_label) if column_label_type == "int" else column_label
 
-        value = df.loc[row_index, column_label]
+        value = dataframe.loc[row_index, column_label]
         return (str(value),)

modules/pandas_wrapper/pandas_loc_row_series.py

@@ -19,35 +19,31 @@ class PandasLocRowSeries:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "row_index": ("STRING", {"default": ""}),
                 "row_index_type":(("string", "int"),)
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "select_row"
     CATEGORY: str = "Data Analysis"
 
-    def select_row(self, dataframe_json: str,
+    def select_row(self, dataframe: pd.DataFrame,
              row_index: str,
              row_index_type: str) -> tuple:
         """
         Selects a specific row using a row label (row index) from a pandas DataFrame,
-        and returns the JSON serialized Series object.
+        and returns the Series object.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             row_index (str): The row index (row label) for the row.
             row_index_type (str): The data type of the row index.
 
         Returns:
-            tuple: A tuple containing the value of the row in string. The string is
-                   a JSON string containing the serialized Pandas Series object.
+            tuple: A tuple containing the value of the row in the serialized Pandas Series object.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
         row_index = int(row_index) if row_index_type == "int" else row_index
-
-        value = df.loc[row_index]
-        return (series_to_jsons(value),)
+        value = dataframe.loc[row_index]
+        return (value,)

modules/pandas_wrapper/pandas_max.py

@@ -19,24 +19,22 @@ class PandasMax:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with max for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.max()),)
+        return (dataframe.max(),)

modules/pandas_wrapper/pandas_mean.py

@@ -19,24 +19,22 @@ class PandasMean:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with mean for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.mean()),)
+        return (dataframe.mean(),)

modules/pandas_wrapper/pandas_median.py

@@ -19,24 +19,22 @@ class PandasMedian:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with median for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.median()),)
+        return (dataframe.median(),)

modules/pandas_wrapper/pandas_min.py

@@ -19,24 +19,22 @@ class PandasMin:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with min for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.min()),)
+        return (dataframe.min(),)

modules/pandas_wrapper/pandas_mode.py

@@ -18,24 +18,22 @@ class PandasMode:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a DataFrame with mode for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the DataFrame.
+            tuple: A tuple containing the DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (df.mode().to_json(),)
+        return (dataframe.mode(),)

modules/pandas_wrapper/pandas_mul.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasMul:
+    """
+    PandasMul:
+        A class for multiplying two pandas DataFrames.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_dataframe": ("DATAFRAME", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+        """
+        Multiply two pandas DataFrames.
+
+        Args:
+            a_dataframe (DataFrame): The left DataFrame.
+            b_dataframe (DataFrame): The right DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of multiplication.
+        """
+        df_out = a_dataframe.mul(b_dataframe)
+        return (df_out,)

modules/pandas_wrapper/pandas_mul_series.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import jsons_to_series
+
+class PandasMulSeries:
+    """
+    PandasMulSeries:
+        A class for multiplying a Pandas DataFrame by a Pandas Series.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_series": ("PDSERIES", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_series: pd.Series) -> tuple:
+        """
+        Multiply a pandas DataFrame by a pandas Series.
+
+        Args:
+            a_dataframe (DataFrame): The DataFrame.
+            b_series (Series): The Series.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of multiplication.
+        """
+        df_out = a_dataframe.mul(b_series)
+        return (df_out,)

modules/pandas_wrapper/pandas_pow.py

@@ -0,0 +1,46 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasPow:
+    """
+    PandasPow:
+        A class for exponentiating two pandas DataFrames.
+    """
+    
+    @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": {
+                # "a_dataframe": ("DATAFRAME", {}),
+                # "b_dataframe": ("DATAFRAME", {})
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_dataframe": ("DATAFRAME", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    # def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+    def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+        """
+        Raise one pandas DataFrame to the power of another.
+
+        Args:
+            a_dataframe (DataFrame): The base DataFrame.
+            b_dataframe (DataFrame): The exponent DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of exponentiation.
+        """
+        df_out = a_dataframe.pow(b_dataframe)
+        return (df_out,)

modules/pandas_wrapper/pandas_pow_series.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import jsons_to_series
+
+class PandasPowSeries:
+    """
+    PandasPowSeries:
+        A class for raising a Pandas DataFrame to the power of a Pandas Series.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_series": ("PDSERIES", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_series: pd.Series) -> tuple:
+        """
+        Raise a pandas DataFrame to the power of a pandas Series.
+
+        Args:
+            a_dataframe (DataFrame): The base DataFrame.
+            b_series (Series): The exponent Series.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of exponentiation.
+        """
+        df_out = a_dataframe.pow(b_series)
+        return (df_out,)

modules/pandas_wrapper/pandas_save_csv.py

@@ -17,7 +17,7 @@ class PandasSaveCSV:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "dataframe_json": ("STRING", {"forceInput": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "file_path": ("STRING", {"multiline": False})
             },
             "hidden": {
@@ -34,7 +34,7 @@ class PandasSaveCSV:
 
     CATEGORY = "Data Analysis"
 
-    def save_csv(self, dataframe_json,file_path=None, unique_id=None, extra_pnginfo=None):
+    def save_csv(self, dataframe, file_path=None, unique_id=None, extra_pnginfo=None):
         text = None
         if unique_id is not None and extra_pnginfo is not None:
             if not isinstance(extra_pnginfo, list):
@@ -47,8 +47,7 @@ class PandasSaveCSV:
             elif file_path is None or len(file_path) == 0:
                 print("Error: file path is not specified")
             else:
-                # Deserialize JSON string to DataFrame
-                df = pd.read_json(StringIO(dataframe_json[0]))
+                df = dataframe[0]
                 df.to_csv(path_or_buf=file_path[0])  # Save as CSV
                 text = [str(df)]  # wrap in list
                 workflow = extra_pnginfo[0]["workflow"]

modules/pandas_wrapper/pandas_save_json.py

@@ -17,7 +17,7 @@ class PandasSaveJSON:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "dataframe_json": ("STRING", {"forceInput": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "file_path": ("STRING", {"multiline": False})
             },
             "hidden": {
@@ -34,7 +34,7 @@ class PandasSaveJSON:
 
     CATEGORY = "Data Analysis"
 
-    def save_json(self, dataframe_json,file_path=None, unique_id=None, extra_pnginfo=None):
+    def save_json(self, dataframe, file_path=None, unique_id=None, extra_pnginfo=None):
         text = None
         if unique_id is not None and extra_pnginfo is not None:
             if not isinstance(extra_pnginfo, list):
@@ -47,8 +47,7 @@ class PandasSaveJSON:
             elif file_path is None or len(file_path) == 0:
                 print("Error: file path is not specified")
             else:
-                # Deserialize JSON string to DataFrame
-                df = pd.read_json(StringIO(dataframe_json[0]))
+                df = dataframe[0]
                 df.to_json(path_or_buf=file_path[0])  # Save as JSON
                 text = [str(df)]  # wrap in list
                 workflow = extra_pnginfo[0]["workflow"]

modules/pandas_wrapper/pandas_select_columns.py

@@ -18,31 +18,26 @@ class PandasSelectColumns:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "column_names": ("STRING", {"default": ""})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "select_columns"
     CATEGORY: str = "Data Analysis"
 
-    def select_columns(self, dataframe_json: str, column_names: str) -> tuple:
+    def select_columns(self, dataframe: pd.DataFrame, column_names: str) -> tuple:
         """
         Selects specific columns from a pandas DataFrame.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             column_names (str): A comma-separated string of column names to select.
 
         Returns:
-            tuple: A tuple containing a JSON string of the DataFrame with selected columns.
+            tuple: A tuple containing the DataFrame with selected columns.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-
-        # Process column names
         selected_columns = [col.strip() for col in column_names.split(",")]
-        df2 = df[selected_columns]
-        
-        return (df2.to_json(),)
+        df2 = dataframe[selected_columns]
+        return (df2,)

modules/pandas_wrapper/pandas_select_rows.py

@@ -18,30 +18,26 @@ class PandasSelectRows:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "condition": ("STRING", {"default": ""})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "select_rows"
     CATEGORY: str = "Data Analysis"
 
-    def select_rows(self, dataframe_json: str, condition: str) -> tuple:
+    def select_rows(self, dataframe: pd.DataFrame, condition: str) -> tuple:
         """
         Selects specific rows from a pandas DataFrame based on a given condition.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             condition (str): A string representing the condition to filter rows.
 
         Returns:
             tuple: A tuple containing a JSON string of the filtered DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        
         # Apply condition
-        df2 = df.query(condition)
-        
-        return (df2.to_json(),)
+        df2 = dataframe.query(condition)
+        return (df2,)

modules/pandas_wrapper/pandas_series_to_string.py

@@ -19,7 +19,7 @@ class PandasSeriesToString:
         """
         return {
             "required": {
-                "series_json": ("STRING", {"multiline": True}),
+                "series": ("PDSERIES", {}),
             }
         }
 
@@ -27,16 +27,14 @@ class PandasSeriesToString:
     FUNCTION: str = "series_to_string"
     CATEGORY: str = "Data Analysis"
 
-    def series_to_string(self, series_json: str) -> tuple:
+    def series_to_string(self, series: pd.Series) -> tuple:
         """
         Converts a Pandas Series to a string representation.
 
         Args:
-            series_json (str): A JSON string representation of the Series.
+            series (Series): The Series.
 
         Returns:
             tuple: A tuple containing the string representation of the Series.
         """
-        # Deserialize JSON string to DataFrame
-        series = jsons_to_series(series_json)
         return (str(series),)

modules/pandas_wrapper/pandas_show_dataframe.py

@@ -12,7 +12,7 @@ class PandasShowDataFrame:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "dataframe_json": ("STRING", {"forceInput": True}),
+                "dataframe": ("DATAFRAME", {})
             },
             "hidden": {
                 "unique_id": "UNIQUE_ID",
@@ -28,7 +28,7 @@ class PandasShowDataFrame:
 
     CATEGORY = "Data Analysis"
 
-    def notify(self, dataframe_json, unique_id=None, extra_pnginfo=None):
+    def notify(self, dataframe, unique_id=None, extra_pnginfo=None):
         text = None
         if unique_id is not None and extra_pnginfo is not None:
             if not isinstance(extra_pnginfo, list):
@@ -39,8 +39,7 @@ class PandasShowDataFrame:
             ):
                 print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
             else:
-                # Deserialize JSON string to DataFrame
-                df = pd.read_json(StringIO(dataframe_json[0]))
+                df = dataframe[0]
                 text = [str(df)]  # wrap in list
                 workflow = extra_pnginfo[0]["workflow"]
                 node = next(
@@ -48,7 +47,11 @@ class PandasShowDataFrame:
                     None,
                 )
                 if node:
-                    node["widgets_values"] = [text]
+                    node["widgets_values"] = text  # [text]
         else:
             print("unique_id or extra_pnginfo is None.")
-        return {"ui": {"text": text}, "result": (text,)}
+
+        # "ui" value is the one that is shown on the node UI.
+        # Note that "ui" return value needs to a list of strings.
+        return {"ui": {"text": text},
+                "result": (text,)}

modules/pandas_wrapper/pandas_show_index.py

@@ -0,0 +1,52 @@
+"""
+This code is based on
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/py/show_text.py
+
+See credit/credit.md for the full license.
+"""
+import pandas as pd
+
+class PandasShowIndex:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "index_list": ("PDINDEX", {}),
+            },
+            "hidden": {
+                "unique_id": "UNIQUE_ID",
+                "extra_pnginfo": "EXTRA_PNGINFO",
+            },
+        }
+
+    INPUT_IS_LIST = True
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "notify"
+    OUTPUT_NODE = True
+    OUTPUT_IS_LIST = (True,)
+
+    CATEGORY = "Data Analysis"
+
+    def notify(self, index_list, unique_id=None, extra_pnginfo=None):
+        text = None
+        if unique_id is not None and extra_pnginfo is not None:
+            if not isinstance(extra_pnginfo, list):
+                print("Error: extra_pnginfo is not a list")
+            elif (
+                not isinstance(extra_pnginfo[0], dict)
+                or "workflow" not in extra_pnginfo[0]
+            ):
+                print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
+            else:
+                ind = index_list[0]
+                text = [str(ind)]  # wrap in list
+                workflow = extra_pnginfo[0]["workflow"]
+                node = next(
+                    (x for x in workflow["nodes"] if str(x["id"]) == str(unique_id[0])),
+                    None,
+                )
+                if node:
+                    node["widgets_values"] = [text]
+        else:
+            print("unique_id or extra_pnginfo is None.")
+        return {"ui": {"text": text}, "result": (text,)}

modules/pandas_wrapper/pandas_show_series.py

@@ -14,7 +14,7 @@ class PandasShowSeries:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "series_json": ("STRING", {"forceInput": True}),
+                "series_list": ("PDSERIES", {}),
             },
             "hidden": {
                 "unique_id": "UNIQUE_ID",
@@ -30,7 +30,7 @@ class PandasShowSeries:
 
     CATEGORY = "Data Analysis"
 
-    def notify(self, series_json, unique_id=None, extra_pnginfo=None):
+    def notify(self, series_list, unique_id=None, extra_pnginfo=None):
         text = None
         if unique_id is not None and extra_pnginfo is not None:
             if not isinstance(extra_pnginfo, list):
@@ -41,8 +41,7 @@ class PandasShowSeries:
             ):
                 print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
             else:
-                # Deserialize JSON string to DataFrame
-                series = jsons_to_series(series_json[0])
+                series = series_list[0]
                 text = [series_to_jsons(series)]  # wrap in list
                 workflow = extra_pnginfo[0]["workflow"]
                 node = next(

modules/pandas_wrapper/pandas_sort.py

@@ -18,32 +18,27 @@ class PandasSort:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
                 "column_name": ("STRING", {"default": ""}),
                 "ascending": ("BOOLEAN", {"default": True})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "sort_rows"
     CATEGORY: str = "Data Analysis"
 
-    def sort_rows(self, dataframe_json: str, column_name: str, ascending: bool) -> tuple:
+    def sort_rows(self, dataframe: pd.DataFrame, column_name: str, ascending: bool) -> tuple:
         """
         Sorts a pandas DataFrame by the specified column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
             column_name (str): The name of the column to sort by.
             ascending (bool): Whether to sort in ascending order.
 
         Returns:
             tuple: A tuple containing a JSON string of the sorted DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        
-        # Sort DataFrame
-        df2 = df.sort_values(by=column_name, ascending=ascending)
-        
-        return (df2.to_json(),)
+        df2 = dataframe.sort_values(by=column_name, ascending=ascending)
+        return (df2,)

modules/pandas_wrapper/pandas_std.py

@@ -19,24 +19,22 @@ class PandasStd:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with standard deviation for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.std()),)
+        return (dataframe.std(),)

modules/pandas_wrapper/pandas_sub.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+
+class PandasSub:
+    """
+    PandasSub:
+        A class for subtracting two pandas DataFrames.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_dataframe": ("DATAFRAME", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_dataframe: pd.DataFrame) -> tuple:
+        """
+        Subtract two pandas DataFrames.
+
+        Args:
+            a_dataframe (DataFrame): The left DataFrame.
+            b_dataframe (DataFrame): The right DataFrame.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of subtraction.
+        """
+        df_out = a_dataframe.sub(b_dataframe)
+        return (df_out,)

modules/pandas_wrapper/pandas_sub_series.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import jsons_to_series
+
+class PandasSubSeries:
+    """
+    PandasSubSeries:
+        A class for subtracting a Pandas Series from a Pandas DataFrame.
+    """
+    
+    @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": {
+                "a_dataframe": ("DATAFRAME", {}),
+                "b_series": ("PDSERIES", {})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, a_dataframe: pd.DataFrame, b_series: pd.Series) -> tuple:
+        """
+        Subtract a pandas Series from a pandas DataFrame.
+
+        Args:
+            a_dataframe (DataFrame): The DataFrame.
+            b_series (Series): The Series.
+
+        Returns:
+            tuple: A tuple containing the DataFrame containing the result of subtraction.
+        """
+        df_out = a_dataframe.sub(b_series)
+        return (df_out,)

modules/pandas_wrapper/pandas_sum.py

@@ -19,24 +19,22 @@ class PandasSum:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with sum for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.sum()),)
+        return (dataframe.sum(),)

modules/pandas_wrapper/pandas_summary.py

@@ -18,28 +18,23 @@ class PandasSummary:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("DATAFRAME",)
     FUNCTION: str = "analyze_dataframe"
     CATEGORY: str = "Data Analysis"
 
-    def analyze_dataframe(self, dataframe_json: str) -> tuple:
+    def analyze_dataframe(self, dataframe: pd.DataFrame) -> tuple:
         """
         Analyzes a pandas DataFrame and returns a summary.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
             tuple: A tuple containing a string representation of the DataFrame summary.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-
-        # Perform summary operation
-        summary = df.describe()
-
+        summary = dataframe.describe()
         return (str(summary),)

modules/pandas_wrapper/pandas_to_string.py

@@ -18,7 +18,7 @@ class PandasToString:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True}),
+                "dataframe": ("DATAFRAME", {}),
             }
         }
 
@@ -26,16 +26,14 @@ class PandasToString:
     FUNCTION: str = "to_string"
     CATEGORY: str = "Data Analysis"
 
-    def to_string(self, dataframe_json: str) -> tuple:
+    def to_string(self, dataframe: pd.DataFrame) -> tuple:
         """
         Converts a pandas DataFrame to a string representation.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
             tuple: A tuple containing the string representation of the DataFrame.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (str(df),)
+        return (str(dataframe),)

modules/pandas_wrapper/pandas_var.py

@@ -19,24 +19,22 @@ class PandasVar:
         """
         return {
             "required": {
-                "dataframe_json": ("STRING", {"multiline": True})
+                "dataframe": ("DATAFRAME", {})
             }
         }
 
-    RETURN_TYPES: tuple = ("STRING",)
+    RETURN_TYPES: tuple = ("PDSERIES",)
     FUNCTION: str = "f"
     CATEGORY: str = "Data Analysis"
 
-    def f(self, dataframe_json: str) -> tuple:
+    def f(self, dataframe: pd.DataFrame) -> tuple:
         """
         Returns a Series with variance for each column.
 
         Args:
-            dataframe_json (str): A JSON string representation of the DataFrame.
+            dataframe (DataFrame): The DataFrame.
 
         Returns:
-            tuple: A tuple containing a JSON string for the Series.
+            tuple: A tuple containing the Series.
         """
-        # Deserialize JSON string to DataFrame
-        df = pd.read_json(StringIO(dataframe_json))
-        return (series_to_jsons(df.var()),)
+        return (dataframe.var(),)