HowToSD-ComfyUI-Data-Analysis/docs/reference/pandas_pivot.md

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:

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:

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.

Input

Name	Data type
dataframe	DataFrame
index	String
columns	String
values	String

Output

Data type
DataFrame

---

Category: Transformation

ComfyUI Data Analysis Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.