Added MPL Pie Chart node.

__init__.py

@@ -12,6 +12,7 @@ from .modules.cda.py_kv_int_create import PyKvIntCreate
 from .modules.cda.py_kv_string_create import PyKvStringCreate
 from .modules.matplotlib.mpl_bar import MPLBar
 from .modules.matplotlib.mpl_line import MPLLine
+from .modules.matplotlib.mpl_pie_chart import MPLPieChart
 from .modules.matplotlib.mpl_scatter import MPLScatter
 from .modules.numpy_wrapper.numpy_float_create import NumpyFloatCreate
 from .modules.numpy_wrapper.numpy_int_create import NumpyIntCreate
@@ -153,6 +154,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "CDATextCreate": CDATextCreate,
     "MPLBar": MPLBar,
     "MPLLine": MPLLine,
+    "MPLPieChart": MPLPieChart,
     "MPLScatter": MPLScatter,
     "NumpyFloatCreate": NumpyFloatCreate,
     "NumpyIntCreate": NumpyIntCreate,
@@ -297,6 +299,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "CDATextCreate": "CDA Text Create",
     "MPLBar": "MPL Bar Chart",
     "MPLLine": "MPL Line Plot",
+    "MPLPieChart": "MPL Pie Chart",
     "MPLScatter": "MPL Scatter Plot",
     "NumpyFloatCreate": "Numpy Float Create",
     "NumpyIntCreate": "Numpy Int Create",

docs/reference/mpl_pie_chart.md

@@ -0,0 +1,24 @@
+# M P L Pie Chart
+:MPL Pie Chart
+Generates a pie chart from a pandas DataFrame.
+
+Below screenshot shows a workflow to generate a pie chart from categorical data.
+![Pie chart](../images/pie_chart.png)
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| label_column_name | String |
+| data_column_name | String |
+| title | String |
+
+## Output
+| Data type |
+|---|
+| Image |
+
+<HR>
+Category: Plot
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/node_reference.md

@@ -131,6 +131,7 @@
 ## Plot
 | Node | Description |
 | --- | --- |
+| [M P L Pie Chart](mpl_pie_chart.md) | :MPL Pie Chart |
 | [MPL Bar Chart](mpl_bar.md) |     Generates a bar chart from a Pandas DataFrame. |
 | [MPL Line Plot](mpl_line.md) |     Generates a line plot from a pandas DataFrame. |
 | [MPL Scatter Plot](mpl_scatter.md) |     Generates a scatter plot from a pandas DataFrame. |

examples/workflows/pie_chart.json

@@ -0,0 +1 @@
+{"last_node_id":23,"last_link_id":39,"nodes":[{"id":2,"type":"PandasCreate","pos":[2874.72607421875,520.831298828125],"size":[270.51373291015625,144.48915100097656],"flags":{},"order":0,"mode":0,"inputs":[],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[1],"slot_index":0}],"properties":{"Node name for S&R":"PandasCreate"},"widgets_values":["Fruits\napple\napple\napple\nbanana\nbanana\ncoconut"]},{"id":1,"type":"PandasValueCounts","pos":[2878.360107421875,709.3477172851562],"size":[269.8277587890625,66.52304077148438],"flags":{},"order":1,"mode":0,"inputs":[{"name":"dataframe","type":"DATAFRAME","link":1}],"outputs":[{"name":"DATAFRAME","type":"DATAFRAME","links":[2,38],"slot_index":0}],"properties":{"Node name for S&R":"PandasValueCounts"},"widgets_values":["Fruits"]},{"id":23,"type":"PreviewImage","pos":[3220.225341796875,853.7537841796875],"size":[470.0382995605469,449.1227722167969],"flags":{},"order":4,"mode":0,"inputs":[{"name":"images","type":"IMAGE","link":39}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"}},{"id":22,"type":"MPLPieChart","pos":[2881.26220703125,852.089111328125],"size":[263.0093688964844,109.4092025756836],"flags":{},"order":3,"mode":0,"inputs":[{"name":"dataframe","type":"DATAFRAME","link":38}],"outputs":[{"name":"IMAGE","type":"IMAGE","links":[39],"slot_index":0}],"properties":{"Node name for S&R":"MPLPieChart"},"widgets_values":["Fruits","count","Fruit distribution"]},{"id":3,"type":"PandasShowDataFrame","pos":[3227.885498046875,700.7517700195312],"size":[210,107.45079040527344],"flags":{},"order":2,"mode":0,"inputs":[{"name":"dataframe","type":"DATAFRAME","link":2}],"outputs":[{"name":"STRING","type":"STRING","links":null,"shape":6}],"properties":{"Node name for S&R":"PandasShowDataFrame"},"widgets_values":["    Fruits  count\n0    apple      3\n1   banana      2\n2  coconut      1"]}],"links":[[1,2,0,1,0,"DATAFRAME"],[2,1,0,3,0,"DATAFRAME"],[38,1,0,22,0,"DATAFRAME"],[39,22,0,23,0,"IMAGE"]],"groups":[],"config":{},"extra":{"ds":{"scale":0.9777396879878871,"offset":[-2464.9461010126247,-450.0436961304677]},"node_versions":{"data-analysis":"bebf4760bc61e4ce5326061b2f56e96c2c9f5d60","comfy-core":"v0.3.10-40-gd0f3752"},"ue_links":[]},"version":0.4}

modules/matplotlib/mpl_pie_chart.py

@@ -0,0 +1,68 @@
+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+from .util import plot_post_steps
+
+class MPLPieChart:
+    """
+    :MPL Pie Chart
+    Generates a pie chart from a pandas DataFrame.
+
+    Below screenshot shows a workflow to generate a pie chart from categorical data.
+    ![Pie chart](../images/pie_chart.png)
+
+    category: Plot
+    """
+    
+    @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 {
+            "required": {
+                "dataframe": ("DATAFRAME", {}),
+                "label_column_name": ("STRING", {"default": ""}),
+                "data_column_name": ("STRING", {"default": ""}),
+                "title": ("STRING", {"default": ""})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self,
+                  dataframe: pd.DataFrame,
+                  label_column_name: str,
+                  data_column_name: str,
+                  title: str) -> tuple:
+        """
+        Generates a pie chart from a Pandas DataFrame.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+            label_column_name (str): The column name for the labels.
+            data_column_name (str): The column name for the data.
+            title (str): The title of the plot.
+
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+
+        df = dataframe
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        ax.pie(df[data_column_name], labels=df[label_column_name], autopct='%1.1f%%', startangle=140)
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            "",
+            "",
+            False)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.3.4"
+version = "1.3.5"
 license = {file = "LICENSE"}
 
 [project.urls]