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
@@ -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)