Added Pandas Iloc Row Series node.

__init__.py

@@ -6,6 +6,7 @@ from .modules.pandas_wrapper.pandas_columns import PandasColumns
 from .modules.pandas_wrapper.pandas_create import PandasCreate
 from .modules.pandas_wrapper.pandas_create_with_index import PandasCreateWithIndex
 from .modules.pandas_wrapper.pandas_head import PandasHead
+from .modules.pandas_wrapper.pandas_iloc_row_series import PandasIlocRowSeries
 from .modules.pandas_wrapper.pandas_index import PandasIndex
 from .modules.pandas_wrapper.pandas_join import PandasJoin
 from .modules.pandas_wrapper.pandas_load_csv import PandasLoadCSV
@@ -33,6 +34,7 @@ NODE_CLASS_MAPPINGS: Dict[str, Type[T]] = {
     "PandasCreate": PandasCreate,
     "PandasCreateWithIndex": PandasCreateWithIndex,
     "PandasHead": PandasHead,
+    "PandasIlocRowSeries": PandasIlocRowSeries,
     "PandasIndex": PandasIndex,
     "PandasJoin": PandasJoin,
     "PandasLoadCSV": PandasLoadCSV,
@@ -63,6 +65,7 @@ NODE_DISPLAY_NAME_MAPPINGS: Dict[str, str] = {
     "PandasCreate": "Pandas Create",
     "PandasCreateWithIndex": "Pandas Create With Index",
     "PandasHead": "Pandas Head",
+    "PandasIlocRowSeries": "Pandas Iloc Row Series",
     "PandasIndex": "Pandas Index",
     "PandasJoin": "Pandas Join",
     "PandasLoadCSV": "Pandas Load CSV",

docs/ug.md

@@ -23,6 +23,7 @@ You can right-click, select **Add Node**, go to **Data Analysis**, and look for
 | **Pandas Index**        | Extract row labels (index)     |
 | **Pandas Select Columns** | Select specific columns from a DataFrame |
 | **Pandas Select Rows**  | Filter rows based on conditions    |
+| **Pandas Iloc Row Series**  | Select a row by row integer position |
 | **Pandas Loc Row Series**  | Select a row by row label (index) |
 | **Pandas Loc Cell Str**  | Select a cell by row label (index) and column label (index) |
 | **Pandas Join**         | Join two DataFrames                |
@@ -63,14 +64,23 @@ Use **Pandas Columns** and **Pandas Index** nodes.  As these nodes output JSON-s
 ![Pandas Create and Pandas Create With Index](images/create.png)
 
 ## Selecting columns and rows
-Use **Pandas Select Columns** for selecting columns, **Pandas Select Rows** to select rows by a filter condition.
+Use **Pandas Select Columns** for selecting columns.
+For rows, there are multiple options.
+Use **Pandas Select Rows** to select rows by a filter condition.
 
 ![Pandas select subset](images/subset.jpg)
 
-You can use **Pandas Loc Row Series** to select a row by row label (index). This node returns a JSON-serialized Series object. Data type of the labels needs to be specified as shown below:
+You can use **Pandas Loc Row Series** to select a row by row label (index), or you can use  **Pandas Iloc Row Series** to select a row by row integer position. Both nodes returns a JSON-serialized Series object.
 
+For  **Pandas Loc Row Series**, data type of the labels needs to be specified as shown below:
 ![Pandas Loc Row Series](images/loc_row_series.png)
 
+For  **Pandas Iloc Row Series**, you only need to specify the row integer position:
+![Pandas Iloc Row Series](images/iloc_row_series.png)
+
+Note that in Pandas, integer position is an equivalent of an index in other software,
+while "index" means a unique label assigned to each row or column. See [pandas.DataFrame.iloc.html](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html).
+
 ## Selecting a cell by row label (index) and column label (index)
 Use **Pandas Loc Cell Str**  for selecting a cell by row label (index) and column label (index). Data type of the labels needs to be specified as shown below:
 

modules/pandas_wrapper/pandas_iloc_row_series.py

@@ -0,0 +1,50 @@
+from typing import Any, Dict
+from io import StringIO
+import pandas as pd
+from .utils import series_to_jsons
+
+class PandasIlocRowSeries:
+    """
+    PandasLocRowSeries:
+        A class for selecting a row from a pandas DataFrame and returning it as a Series.
+        The row is specified by its integer position, similar to an index in other software.
+        Note: In Pandas, "index" refers to a unique label assigned to each row or column. See https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iloc.html
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `select_row` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe_json": ("STRING", {"multiline": True}),
+                "row_integer_position": ("INT", {"default": 0, "min": 0, "max": 2**31})
+            }
+        }
+
+    RETURN_TYPES: tuple = ("STRING",)
+    FUNCTION: str = "select_row"
+    CATEGORY: str = "Data Analysis"
+
+    def select_row(self, dataframe_json: str,
+             row_integer_position: int) -> tuple:
+        """
+        Selects a specific row using a row label (row index) from a pandas DataFrame,
+        and returns the JSON serialized Series object.
+
+        Args:
+            dataframe_json (str): A JSON string representation of the DataFrame.
+            row_integer_position (int): The row integer position.
+
+        Returns:
+            tuple: A tuple containing the value of the row in string. The string is
+                   a JSON string containing the serialized Pandas Series object.
+        """
+        # Deserialize JSON string to DataFrame
+        df = pd.read_json(StringIO(dataframe_json))
+        value = df.iloc[row_integer_position]
+        return (series_to_jsons(value),)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "data-analysis"
 description = "Data analysis custom modules for ComfyUI - Use Pandas & Matplotlib within ComfyUI"
-version = "1.0.12"
+version = "1.0.13"
 license = {file = "LICENSE"}
 
 [project.urls]