Added Pandas Pivot node.

modules/pandas_wrapper/pandas_pivot.py

@@ -0,0 +1,140 @@
+from typing import Any, Dict
+import pandas as pd
+from .utils import column_labels_string_to_list
+
+class PandasPivot:
+    """
+    Computes a pivot table from a pandas DataFrame.
+
+    ## Note on a pivot table
+    A pivot table is easier to understand if we start off from the table itself:  
+    In the below DataFrame:
+
+    ```
+       Column labels
+        X  Y  Z
+    Index
+    A   1  2  3
+    B   4  5  6
+    C   7  8  9
+    ```
+
+    We have index `["A", "B", "C"]` and column labels `["X", "Y", "Z"]`.  
+    Values in this DataFrame can be accessed by specifying the index and the column label.  
+    For example:
+
+    ```python
+    df.loc["A", "X"]  # 1
+    df.loc["A", "Y"]  # 2
+    ```
+
+    Now, let's restructure the table by converting column labels into row values:
+
+    ```
+    A   X  1
+    A   Y  2
+    A   Z  3
+    B   X  4
+    B   Y  5
+    B   Z  6
+    C   X  7
+    C   Y  8
+    C   Z  9
+    ```
+
+    Let's add a column label to each of these columns:
+
+    ```
+    R   C  V
+    --------
+    A   X  1
+    A   Y  2
+    A   Z  3
+    B   X  4
+    B   Y  5
+    B   Z  6
+    C   X  7
+    C   Y  8
+    C   Z  9
+    ```
+
+    Now, the first two columns uniquely identify the value in the third column.
+
+    **Pivoting** is the reverse of this operation.  
+    - You start with the table above and produce the original one.  
+    - In pandas, this means telling it to use:
+      - R column: `A`, `B`, `C` as the index (row labels).
+      - C column: `X`, `Y`, `Z` as the column labels.
+      - V column: The numbers (`1, 2, 3, ...`) as the values for each cell indexed by `[A, B, or C]` and `[X, Y, or Z]`.
+
+    This can be done using:
+
+    ```python
+    df.pivot(index="R", columns="C", values="V")
+    ```
+
+    The result:
+
+    ```
+       X  Y  Z
+    A  1  2  3
+    B  4  5  6
+    C  7  8  9
+    ```
+
+    Now the data is back to its original, structured form.
+
+    ![Pivot](../images/pivot.png)
+    category: Transformation
+    """
+
+    @classmethod
+    def INPUT_TYPES(cls) -> Dict[str, Any]:
+        """
+        Defines the input types for the `select_columns` function.
+
+        Returns:
+            Dict[str, Any]: A dictionary specifying required input types.
+        """
+        return {
+            "required": {
+                "dataframe": ("DATAFRAME", {}),
+                "index": ("STRING", {"default": ""}),
+                "columns": ("STRING", {"default": ""}),
+                "values": ("STRING", {"default": ""}),
+            }
+        }
+
+    RETURN_TYPES: tuple = ("DATAFRAME",)
+    FUNCTION: str = "f"
+    CATEGORY: str = "Data Analysis"
+
+    def f(self, dataframe: pd.DataFrame,
+          index: str,
+          columns: str,
+          values: str) -> tuple:
+        """
+        Selects specific columns from a pandas DataFrame.
+
+        Args:
+            dataframe (DataFrame): The DataFrame.
+            index (str): A comma-separated string of column names to use as index in the pivot table.
+            columns (str): A comma-separated string of column names to use as columns in the pivot table.
+            values (str): A comma-separated string of column names to use as values in the pivot table.
+
+        Returns:
+            tuple: A tuple containing the DataFrame.
+        """
+        ind = column_labels_string_to_list(dataframe, index)
+        col = column_labels_string_to_list(dataframe, columns)
+        val = column_labels_string_to_list(dataframe, values)
+
+        if len(ind) == 1:
+            ind = ind[0]
+        if len(col) == 1:
+            col = col
+        if len(val) == 1:
+            val = val[0]
+
+        df2 = dataframe.pivot(index=ind, columns=col, values=val)
+        return (df2,)