Added Pandas Count, Max, Mean, Median, Min, Mode, Std, Sum, Var, Show Series nodes.

modules/pandas_wrapper/pandas_count.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasCount:
+    """
+    PandasCount:
+        A class for computing the count of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with count for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.count()),)

modules/pandas_wrapper/pandas_max.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMax:
+    """
+    PandasMin:
+        A class for computing the max of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with max for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.max()),)

modules/pandas_wrapper/pandas_mean.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMean:
+    """
+    PandasMin:
+        A class for computing the mean of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with mean for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.mean()),)

modules/pandas_wrapper/pandas_median.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMedian:
+    """
+    PandasMin:
+        A class for computing the median of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with median for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.median()),)

modules/pandas_wrapper/pandas_min.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasMin:
+    """
+    PandasMin:
+        A class for computing the min of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with min for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.min()),)

modules/pandas_wrapper/pandas_mode.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+
+class PandasMode:
+    """
+    PandasMode:
+        A class for computing the mode of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a DataFrame with mode for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the DataFrame.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (df.mode().to_json(),)

modules/pandas_wrapper/pandas_show_series.py

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+"""
+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.
+"""
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+from .utils import jsons_to_series
+
+class PandasShowSeries:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "series_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, series_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
+                series = jsons_to_series(series_json[0])
+                text = [series_to_jsons(series)]  # 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_wrapper/pandas_std.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasStd:
+    """
+    PandasStd:
+        A class for computing the standard deviation of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with standard deviation for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.std()),)

modules/pandas_wrapper/pandas_sum.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasSum:
+    """
+    PandasSum:
+        A class for computing the sum of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with sum for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.sum()),)

modules/pandas_wrapper/pandas_var.py

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+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasVar:
+    """
+    PandasVar:
+        A class for computing the variance of a pandas DataFrame.
+    """
+    
+    @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_json": ("STRING", {"multiline": True})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe_json: str) -> tuple:
+        """
+        Returns a Series with variance for each column.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+
+        Returns:
+            tuple: A tuple containing a JSON string for the Series.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        return (series_to_jsons(df.var()),)