Initial code commit

.gitignore

@@ -0,0 +1,3 @@
+__pycache__/
+**/__pycache__/
+

__init__.py

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+from typing import Dict, Type, TypeVar
+from .modules.matplotlib.mpl_bar import MPLBar
+from .modules.matplotlib.mpl_line import MPLLine
+from .modules.matplotlib.mpl_scatter import MPLScatter
+from .modules.pandas.pandas_head import PandasHead
+from .modules.pandas.pandas_join import PandasJoin
+from .modules.pandas.pandas_load_csv import PandasLoadCSV
+from .modules.pandas.pandas_select_columns import PandasSelectColumns
+from .modules.pandas.pandas_select_rows import PandasSelectRows
+from .modules.pandas.pandas_show_dataframe import PandasShowDataFrame
+from .modules.pandas.pandas_sort import PandasSort
+from .modules.pandas.pandas_summary import PandasSummary
+from .modules.pandas.pandas_to_string import PandasToString
+
+T = TypeVar("T")
+
+NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
+    "MPLBar": MPLBar,
+    "MPLLine": MPLLine,
+    "MPLScatter": MPLScatter,
+    "PandasHead": PandasHead,
+    "PandasJoin": PandasJoin,
+    "PandasLoadCSV": PandasLoadCSV,
+    "PandasSelectColumns": PandasSelectColumns,
+    "PandasSelectRows": PandasSelectRows,
+    "PandasShowDataFrame": PandasShowDataFrame,
+    "PandasSort": PandasSort,
+    "PandasSummary": PandasSummary,
+    "PandasToString": PandasToString,
+}
+"""
+NODE_CLASS_MAPPINGS (Dict[str, Type[T]]):
+    A dictionary mapping node names to their corresponding class implementations.
+
+    - "MPLBar": Generates a bar chart from DataFrame data.
+    - "MPLLine": Generates a line plot from DataFrame data.
+    - "MPLScatter": Generates a scatter plot from DataFrame data.
+    - "PandasHead": Handles retrieving the first few rows of a DataFrame.
+    - "PandasJoin": Provides functionality to join multiple DataFrames.
+    - "PandasLoadCSV": Loads CSV data into a DataFrame.
+    - "PandasSelectColumns": Selects specific columns from a DataFrame.
+    - "PandasSelectRows": Filters specific rows from a DataFrame.
+    - "PandasShowDataFrame": Displays the content of the DataFrame.
+    - "PandasSort": Sorts a DataFrame based on given criteria.
+    - "PandasSummary": Provides a summary of a DataFrame.
+    - "PandasToString": Converts a DataFrame to a string representation.
+"""
+
+NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
+    "MPLBar": "MPL Bar Chart",
+    "MPLLine": "MPL Line Plot",
+    "MPLScatter": "MPL Scatter Plot",
+    "PandasHead": "Pandas Head",
+    "PandasJoin": "Pandas Join",
+    "PandasLoadCSV": "Pandas Load CSV",
+    "PandasSelectColumns": "Pandas Select Columns",
+    "PandasSelectRows": "Pandas Select Rows",
+    "PandasShowDataFrame": "Pandas Show DataFrame",
+    "PandasSort": "Pandas Sort",
+    "PandasSummary": "Pandas Summary",
+    "PandasToString": "Pandas To String",
+}
+"""
+NODE_DISPLAY_NAME_MAPPINGS (Dict[str, str]):
+    A dictionary mapping node names to user-friendly display names.
+"""
+
+"""
+Below two lines were taken from:
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/__init__.py
+See credit/credit.md for the full license.
+"""
+WEB_DIRECTORY = "./web"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]

credit/credit.md

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+# Third-party license
+
+## pythongosssss/ComfyUI-Custom-Scripts
+This software is licensed under the following license:
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/LICENSE
+
+Full text:
+```
+MIT License
+
+Copyright (c) 2023 pythongosssss
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```

modules/matplotlib/common.py

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+def common_input_types():
+    """
+    Defines the input types for a plot function.
+
+    Returns:
+        Dict[str, Any]: A dictionary specifying required input types.
+    """
+    return {
+        "required": {
+            "dataframe_json": ("STRING", {"multiscatter": True}),
+            "x_column_name": ("STRING", {"default": ""}),
+            "y_column_name": ("STRING", {"default": ""}),
+            "title": ("STRING", {"default": ""}),
+            "x_axis_label": ("STRING", {"default": ""}),
+            "y_axis_label": ("STRING", {"default": ""}),
+            "x_tick_as_int": ("BOOLEAN", {"default": False})  # To show 2002 instead of 2002.5 on x-axis
+        }
+    }

modules/matplotlib/mpl_bar.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLBar:
+    """
+    MPLBar:
+        A class for generating bar charts from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `bar_chart` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "bar_chart"
+    CATEGORY: str = "Data Analysis"
+
+    def bar_chart(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a bar chart from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of 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.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.bar(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/mpl_line.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLLine:
+    """
+    MPLPlotLine:
+        A class for generating line plots from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `line_plot` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "line_plot"
+    CATEGORY: str = "Data Analysis"
+
+    def line_plot(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a line plot from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of 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.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.plot(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/mpl_scatter.py

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+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .common import common_input_types
+from .util import plot_post_steps
+
+class MPLScatter:
+    """
+    MPLPlotScatter:
+        A class for generating scatter plots from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `scatter_plot` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return common_input_types()
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "scatter_plot"
+    CATEGORY: str = "Data Analysis"
+
+    def scatter_plot(self,
+                  dataframe_json: str,
+                  x_column_name: str,
+                  y_column_name: str,
+                  title: str,
+                  x_axis_label: str,
+                  y_axis_label: str,
+                  x_tick_as_int: bool) -> tuple:
+        """
+        Generates a scatter plot from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of 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.
+            x_axis_label (str): The label for the x-axis data.
+            y_axis_label (str): The label for the y-axis data.
+            x_tick_as_int (bool): If set, use integers for x ticks. For example,
+                                  if ticks show floating point numbers for years, e.g. 2002.5,
+                                  setting this flag will show integer values instead.
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.plot(df[x_column_name], df[y_column_name])
+
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            x_axis_label,
+            y_axis_label,
+            x_tick_as_int)

modules/matplotlib/util.py

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+import io
+from typing import Any, Dict
+import numpy as np
+import matplotlib.figure
+import matplotlib.axes
+from matplotlib import pyplot as plt
+import matplotlib.ticker as mticker
+import pandas as pd
+from PIL import Image
+import torch
+
+
+def plot_post_steps(
+    fig: matplotlib.figure.Figure,
+    ax: matplotlib.axes.Axes,
+    plt: plt,
+    title: str,
+    x_axis_label: str,
+    y_axis_label: str,
+    x_tick_as_int: bool
+) -> tuple:
+    r"""Plots a post-processed step plot using Matplotlib.
+
+    Args:
+        fig (matplotlib.figure.Figure): The figure object for the plot.
+        ax (matplotlib.axes.Axes): The axes object for the plot.
+        plt (module): The Matplotlib module.
+        title (str): The title of the plot.
+        x_axis_label (str): The label for the x-axis.
+        y_axis_label (str): The label for the y-axis.
+        x_tick_as_int (bool): If ``True``, use integers for x-axis ticks. 
+            For example, if ticks display floating point numbers (e.g., 2002.5), 
+            setting this flag forces integer values instead.
+
+    Returns:
+        tuple: A tuple containing the image tensor representation of the plot.
+    """
+    if x_tick_as_int:
+        ax.xaxis.set_major_locator(mticker.MaxNLocator(integer=True))
+
+    # Set title and labels
+    ax.set_title(title)
+    ax.set_xlabel(x_axis_label)
+    ax.set_ylabel(y_axis_label)
+
+    # Save the plot to a buffer
+    buf = io.BytesIO()
+    fig.savefig(buf, format='PNG')
+    buf.seek(0)
+    plt.close(fig)  # Close the figure to free memory
+
+    # Load image and convert to tensor
+    image = Image.open(buf).convert("RGB")
+    image_tensor = torch.from_numpy(np.array(image)).float() / 255.0  # Normalize to [0,1]
+    image_tensor = image_tensor.unsqueeze(0)
+    return (image_tensor,)

modules/pandas/pandas_head.py

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+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasHead:
+    """
+    PandasHead:
+        A class for retrieving the first few rows of a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `head` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+                "rows": ("INT", {"default": 10, "min": 0, "max": 50})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "head"
+    CATEGORY: str = "Data Analysis"
+
+    def head(self, dataframe_json: str, rows: int) -> tuple:
+        """
+        Returns the first `rows` rows of a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of 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(dataframe_json)
+        df2 = df.head(rows)
+        return (df2.to_json(),)

modules/pandas/pandas_join.py

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+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasJoin:
+    """
+    PandasJoin:
+        A class for merging two pandas DataFrames based on a common column.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `sort_rows` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "left_dataframe_json": ("STRING", {"multiline": True}),
+                "right_dataframe_json": ("STRING", {"multiline": True}),
+                "on_column_name": ("STRING", {"default": ""}),
+                "join_method":(("inner", "left", "right", "outer"),)
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    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:
+        """
+        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.
+            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(left_dataframe_json)
+        df_right = pd.read_json(right_dataframe_json)
+
+        df_out = pd.merge(df_left, df_right, on=on_column_name, how=join_method)
+        return (df_out.to_json(),)

modules/pandas/pandas_load_csv.py

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+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasLoadCSV:
+    """
+    PandasLoadCSV:
+        A class for loading CSV files into a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `load_csv` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "file_path": ("STRING", {"multiline": False}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "load_csv"
+    CATEGORY: str = "Data Analysis"
+
+    def load_csv(self, file_path: str) -> tuple:
+        """
+        Loads a CSV file into a pandas DataFrame and converts it to a JSON string.
+
+        Args:
+            file_path (str): The path to the CSV file.
+
+        Returns:
+            tuple: A tuple containing a JSON string representation of the DataFrame.
+        """
+        # Read CSV file into DataFrame
+        df = pd.read_csv(file_path)
+
+        # Convert DataFrame to JSON
+        result_json = df.to_json()
+
+        return (result_json,)

modules/pandas/pandas_select_columns.py

@@ -0,0 +1,47 @@
+from typing import Any, Dict
+import pandas as pd
+
+class PandasSelectColumns:
+    """
+    PandasSelectColumns:
+        A class for selecting specific columns from a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `select_columns` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+                "column_names": ("STRING", {"default": ""})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "select_columns"
+    CATEGORY: str = "Data Analysis"
+
+    def select_columns(self, dataframe_json: str, column_names: str) -> tuple:
+        """
+        Selects specific columns from a pandas DataFrame.
+
+        Args:
+            dataframe_json (str): A JSON string representation of 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.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Process column names
+        selected_columns = [col.strip() for col in column_names.split(",")]
+        df2 = df[selected_columns]
+        
+        return (df2.to_json(),)

modules/pandas/pandas_select_rows.py

@@ -0,0 +1,47 @@
+import ast
+from typing import Any, Dict
+import pandas as pd
+
+class PandasSelectRows:
+    """
+    PandasSelectRows:
+        A class for selecting specific rows from a pandas DataFrame based on a condition.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `select_rows` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+                "condition": ("STRING", {"default": ""})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "select_rows"
+    CATEGORY: str = "Data Analysis"
+
+    def select_rows(self, dataframe_json: str, 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.
+            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(dataframe_json)
+        
+        # Apply condition
+        df2 = df.query(condition)
+        
+        return (df2.to_json(),)

modules/pandas/pandas_show_dataframe.py

@@ -0,0 +1,53 @@
+"""
+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 PandasShowDataFrame:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"forceInput": True}),
+            },
+            "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, dataframe_json, 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:
+                # Deserialize JSON string to DataFrame
+                df = pd.read_json(dataframe_json[0])
+                text = [str(df)]  # 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/pandas_sort.py

@@ -0,0 +1,48 @@
+from typing import Any, Dict
+import pandas as pd
+
+class PandasSort:
+    """
+    PandasSort:
+        A class for sorting a pandas DataFrame by a specified column.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `sort_rows` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+                "column_name": ("STRING", {"default": ""}),
+                "ascending": ("BOOLEAN", {"default": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "sort_rows"
+    CATEGORY: str = "Data Analysis"
+
+    def sort_rows(self, dataframe_json: str, 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.
+            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(dataframe_json)
+        
+        # Sort DataFrame
+        df2 = df.sort_values(by=column_name, ascending=ascending)
+        
+        return (df2.to_json(),)

modules/pandas/pandas_summary.py

@@ -0,0 +1,44 @@
+from typing import Any, Dict
+import pandas as pd
+
+class PandasSummary:
+    """
+    PandasSummary:
+        A class for analyzing and summarizing a pandas DataFrame.
+    """
+    
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `analyze_dataframe` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "analyze_dataframe"
+    CATEGORY: str = "Data Analysis"
+
+    def analyze_dataframe(self, dataframe_json: str) -> tuple:
+        """
+        Analyzes a pandas DataFrame and returns a summary.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a string representation of the DataFrame summary.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+
+        # Perform summary operation
+        summary = df.describe()
+
+        return (str(summary),)

modules/pandas/pandas_to_string.py

@@ -0,0 +1,40 @@
+from typing import Any, Dict
+import pandas as pd
+
+class PandasToString:
+    """
+    PandasToString:
+        A class for converting a pandas DataFrame 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": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "to_string"
+    CATEGORY: str = "Data Analysis"
+
+    def to_string(self, dataframe_json: str) -> tuple:
+        """
+        Converts a pandas DataFrame to a string representation.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing the string representation of the DataFrame.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(dataframe_json)
+        return (str(df),)

web/js/ShowDataFrame.js

@@ -0,0 +1,64 @@
+/*
+This code is based on
+https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/web/js/showText.js
+
+See credit/credit.md for the full license.
+*/
+
+import { app } from "../../../scripts/app.js";
+import { ComfyWidgets } from "../../../scripts/widgets.js";
+
+// Displays input text on a node
+app.registerExtension({
+	name: "HowToSD.ShowDataFrame",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "PandasShowDataFrame") {  // This needs to match NODE_CLASS_MAPPINGS in Python
+			function populate(text) {
+				if (this.widgets) {
+					for (let i = 1; i < this.widgets.length; i++) {
+						this.widgets[i].onRemove?.();
+					}
+					this.widgets.length = 1;
+				}
+
+				const v = [...text];
+				if (!v[0]) {
+					v.shift();
+				}
+				for (const list of v) {
+					const w = ComfyWidgets["STRING"](this, "text2", ["STRING", { multiline: true }], app).widget;
+					w.inputEl.readOnly = true;
+					w.inputEl.style.opacity = 0.6;
+					w.value = list;
+				}
+
+				requestAnimationFrame(() => {
+					const sz = this.computeSize();
+					if (sz[0] < this.size[0]) {
+						sz[0] = this.size[0];
+					}
+					if (sz[1] < this.size[1]) {
+						sz[1] = this.size[1];
+					}
+					this.onResize?.(sz);
+					app.graph.setDirtyCanvas(true, false);
+				});
+			}
+
+			// When the node is executed we will be sent the input text, display this in the widget
+			const onExecuted = nodeType.prototype.onExecuted;
+			nodeType.prototype.onExecuted = function (message) {
+				onExecuted?.apply(this, arguments);
+				populate.call(this, message.text);
+			};
+
+			const onConfigure = nodeType.prototype.onConfigure;
+			nodeType.prototype.onConfigure = function () {
+				onConfigure?.apply(this, arguments);
+				if (this.widgets_values?.length) {
+					populate.call(this, this.widgets_values.slice(+this.widgets_values.length > 1));
+				}
+			};
+		}
+	},
+});