Added Pandas Pivot node.

docs/reference/node_reference.md

@@ -136,6 +136,7 @@
 | [Pandas Horizontal Concat](pandas_horizontal_concat.md) | Horizontally concatenates two pandas DataFrames. |
 | [Pandas Horizontal Split](pandas_horizontal_split.md) | Horizontally splits a Pandas DataFrame into two pandas DataFrames. |
 | [Pandas Join](pandas_join.md) | Merges two pandas DataFrames based on a common column. |
+| [Pandas Pivot](pandas_pivot.md) | Computes a pivot table from a pandas DataFrame. |
 | [Pandas Rename](pandas_rename.md) | Renames an index (row label) or a column label. |
 | [Pandas Series To Data Frame](pandas_series_to_dataframe.md) | Converts a Pandas Series to a DataFrame. |
 | [Pandas Set Index](pandas_set_index.md) | Create index (row label) from existing column or columns. |

docs/reference/pandas_pivot.md

@@ -0,0 +1,100 @@
+# Pandas Pivot
+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)
+
+## Input
+| Name | Data type |
+|---|---|
+| dataframe | DataFrame |
+| index | String |
+| columns | String |
+| values | String |
+
+## Output
+| Data type |
+|---|
+| DataFrame |
+
+<HR>
+Category: Transformation
+
+ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.