Added SNS Boxplot node

2025-02-25 15:40:01 -08:00
parent d330efa71c
commit 0316f8949b
6 changed files with 148 additions and 1 deletions
@@ -133,6 +133,7 @@ from .modules.pytorch_wrapper.pt_to_latent import PtToLatent
 from .modules.pytorch_wrapper.pt_to_numpy import PtToNumpy
 from .modules.pytorch_wrapper.pt_to_rgb_tensors import PtToRgbTensors
 from .modules.seaborn_wrapper.sns_bar import SNSBar
+from .modules.seaborn_wrapper.sns_boxplot import SNSBoxplot
 from .modules.seaborn_wrapper.sns_histogram import SNSHistogram
 from .modules.seaborn_wrapper.sns_line import SNSLine
 from .modules.seaborn_wrapper.sns_scatter import SNSScatter
@@ -278,6 +279,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
    "PyKvIntCreate": PyKvIntCreate,
    "PyKvStringCreate": PyKvStringCreate,
    "SNSBar": SNSBar,
+    "SNSBoxplot": SNSBoxplot,
    "SNSHistogram": SNSHistogram,
    "SNSLine": SNSLine,
    "SNSScatter": SNSScatter,
@@ -423,6 +425,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
    "PyKvIntCreate": "Py Kv Int Create",
    "PyKvStringCreate": "Py Kv String Create",
    "SNSBar": "SNS Bar Chart",
+    "SNSBoxplot": "SNS Boxplot",
    "SNSHistogram": "SNS Histogram",
    "SNSLine": "SNS Line Plot",
    "SNSScatter": "SNS Scatter Plot",
@@ -136,6 +136,7 @@
 | [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. |
 | [SNS Bar Chart](sns_bar.md) | Generates a bar chart from a Pandas DataFrame using Seaborn. |
+| [SNS Boxplot](sns_boxplot.md) | Generates a boxplot from a Pandas DataFrame using Seaborn. |
 | [SNS Histogram](sns_histogram.md) | Generates a histogram from a Pandas DataFrame using Seaborn. |
 | [SNS Line Plot](sns_line.md) | Generates a line plot from a pandas DataFrame using Seaborn. |
 | [SNS Scatter Plot](sns_scatter.md) | Generates a scatter plot from a pandas DataFrame using Seaborn. |
@@ -0,0 +1,44 @@
+# SNS Boxplot
+Generates a boxplot from a Pandas DataFrame using Seaborn.
+
+You can generate a boxplot from either a single column or two columns.  
+* For two columns, the first column should contain the category, and the second column should contain the corresponding numerical values.  
+* For a wide-format table, use the Pandas Melt node to create a category column first. This column is referred to as the "variable" column in melting.  
+* For a single-column dataset, leave the category field blank.
+
+For example, if you have a DataFrame tracking apple and banana prices at various stores:
+```
+ Fruit  Price
+ apple    5
+ apple    3
+ apple    2
+ appel    2.5
+ banana   4
+ banana   2
+ banana   2
+```
+If you specify `Fruit` as the category and `Price` as the value, two boxplots will be generated: one for apples and one for bananas.
+
+![Boxplot](../images/boxplot.png)
+
+Refer to [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.boxplot.html) for more information.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| category_column_name | String |
+| value_column_name | String |
+| title | String |
+| style | String |
+| palette | String |
+
+## Output
+| Data type |
+|---|
+| Image |
+
+<HR>
+Category: Plot
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.
@@ -0,0 +1,99 @@
+from typing import Any, Dict
+from matplotlib import pyplot as plt
+import pandas as pd
+import seaborn as sns
+from .util import plot_post_steps
+from .util import set_up_sns_style_palette
+
+
+class SNSBoxplot:
+    """
+    SNS Boxplot:
+    Generates a boxplot from a Pandas DataFrame using Seaborn.
+
+    You can generate a boxplot from either a single column or two columns.  
+    * For two columns, the first column should contain the category, and the second column should contain the corresponding numerical values.  
+    * For a wide-format table, use the Pandas Melt node to create a category column first. This column is referred to as the "variable" column in melting.  
+    * For a single-column dataset, leave the category field blank.
+
+    For example, if you have a DataFrame tracking apple and banana prices at various stores:
+    ```
+     Fruit  Price
+     apple    5
+     apple    3
+     apple    2
+     appel    2.5
+     banana   4
+     banana   2
+     banana   2
+    ```
+    If you specify `Fruit` as the category and `Price` as the value, two boxplots will be generated: one for apples and one for bananas.
+
+    ![Boxplot](../images/boxplot.png)
+
+    Refer to [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.boxplot.html) for more information.
+
+    category: Plot
+    """
+    
+    @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 {
+            "required": {
+                "dataframe": ("DATAFRAME", {}),
+                "category_column_name": ("STRING", {"default": ""}),
+                "value_column_name": ("STRING", {"default": ""}),
+                "title": ("STRING", {"default": ""}),
+                "style": ("STRING", {"default": ""}),
+                "palette": ("STRING", {"default": ""})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("IMAGE",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self,
+                  dataframe: pd.DataFrame,
+                  category_column_name: str,
+                  value_column_name: str,
+                  title: str,
+                  style: str=None,
+                  palette: str=None) -> tuple:
+        """
+        Generates a bar chart from a pandas DataFrame.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+            category_column_name (str): The category (variable) column name.
+            value_column_name (str): The column name for the value data.
+            title (str): The title of the plot.
+            style (str): Style of the plot
+            palette (str): Palette of the plot
+        Returns:
+            tuple: A tuple containing the image tensor representation of the plot.
+        """
+        df = dataframe
+        set_up_sns_style_palette(style, palette)
+
+        # Create the plot
+        fig, ax = plt.subplots()
+        if category_column_name:
+            sns.boxplot(x=df[category_column_name],y=df[value_column_name], ax=ax)
+        else:
+            sns.boxplot(y=df[value_column_name], ax=ax)
+        
+        return plot_post_steps(
+            fig,
+            ax,
+            plt,
+            title,
+            None,
+            None,
+            None)
@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.3.5"
+version = "1.3.6"
 license = {file = "LICENSE"}

 [project.urls]