Added Pandas Cov and Corr nodes

__init__.py

@@ -18,8 +18,10 @@ from .modules.pandas_wrapper.pandas_as_float import PandasAsFloat
 from .modules.pandas_wrapper.pandas_as_int import PandasAsInt
 from .modules.pandas_wrapper.pandas_as_string import PandasAsString
 from .modules.pandas_wrapper.pandas_columns import PandasColumns
+from .modules.pandas_wrapper.pandas_corr import PandasCorr
 from .modules.pandas_wrapper.pandas_cos import PandasCos
 from .modules.pandas_wrapper.pandas_count import PandasCount
+from .modules.pandas_wrapper.pandas_cov import PandasCov
 from .modules.pandas_wrapper.pandas_create import PandasCreate
 from .modules.pandas_wrapper.pandas_create_with_index import PandasCreateWithIndex
 from .modules.pandas_wrapper.pandas_cummax import PandasCummax
@@ -123,8 +125,10 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasAsInt": PandasAsInt,
     "PandasAsString": PandasAsString,
     "PandasColumns": PandasColumns,
+    "PandasCorr": PandasCorr,
     "PandasCos": PandasCos,
     "PandasCount": PandasCount,
+    "PandasCov": PandasCov,
     "PandasCreate": PandasCreate,
     "PandasCreateWithIndex": PandasCreateWithIndex,
     "PandasCummax": PandasCummax,
@@ -228,8 +232,10 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasAsInt": "Pandas As Int",
     "PandasAsString": "Pandas As String",
     "PandasColumns": "Pandas Columns",
+    "PandasCorr": "Pandas Corr",
     "PandasCos": "Pandas Cos",
     "PandasCount": "Pandas Count",
+    "PandasCov": "Pandas Cov",
     "PandasCreate": "Pandas Create",
     "PandasCreateWithIndex": "Pandas Create With Index",
     "PandasCummax": "Pandas Cummax",

docs/reference/node_reference.md

@@ -111,7 +111,9 @@
 ## Summary statistics
 | Node | Description |
 | --- | --- |
+| [Pandas Corr](pandas_corr.md) | Computes the correlation of the DataFrame. You can select from the Pearson, Kendall or Spearman methods. |
 | [Pandas Count](pandas_count.md) | Computes the count of a pandas DataFrame. |
+| [Pandas Cov](pandas_cov.md) | Computes the covariance of the DataFrame. |
 | [Pandas Max](pandas_max.md) | Computes the max of a pandas DataFrame. |
 | [Pandas Mean](pandas_mean.md) | Computes the mean of a pandas DataFrame. |
 | [Pandas Median](pandas_median.md) | Computes the median of a pandas DataFrame. |

docs/reference/pandas_corr.md

@@ -0,0 +1,18 @@
+# Pandas Corr
+Computes the correlation of the DataFrame. You can select from the Pearson, Kendall or Spearman methods.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| method |  |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Summary statistics
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/pandas_cov.md

@@ -0,0 +1,17 @@
+# Pandas Cov
+Computes the covariance of the DataFrame.
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Summary statistics
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

modules/pandas_wrapper/pandas_corr.py

@@ -0,0 +1,41 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasCorr:
+    """
+    Computes the correlation of the DataFrame. You can select from the Pearson, Kendall or Spearman methods.
+
+    category: Summary statistics
+    """
+    
+    @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", {}),
+                "method": (("pearson", "kendall", "spearman"),)
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame, method: str) -> tuple:
+        """
+        Computes the correlation of the DataFrame.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the result.
+        """
+        return (dataframe.corr(method),)

modules/pandas_wrapper/pandas_cov.py

@@ -0,0 +1,40 @@
+from typing import Any, Dict
+import pandas as pd
+
+
+class PandasCov:
+    """
+    Computes the covariance of the DataFrame.
+
+    category: Summary statistics
+    """
+    
+    @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:
+        """
+        Computes the covariance of the DataFrame.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+
+        Returns:
+            tuple: A tuple containing the result.
+        """
+        return (dataframe.cov(),)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.1.18"
+version = "1.1.19"
 license = {file = "LICENSE"}
 
 [project.urls]