Task: Data-transform-reshape (source dataset: Auto-Tables)

Given a table, predict what python pandas transformation may be needed on the given input table. Valid choices for transformation include: {"stack", "wide_to_long", "transpose", "pivot", "explode", "ffill", "subtitles"}. Note that each of these is a common Python Pandas operator. Some transformations can have parameters. Each transformation and its parameters are describe as follows.
 
- stack: collapse homogeneous cols into rows.
@param (int): stack_start_idx: zero-based starting column index of the homogeneous column-group.
@param (int): stack_end_idx: zero-based ending column index of the homogeneous column-group.
 
- wide_to_long: collapse repeating col-groups into rows.
@param (int): wide_to_long_start_idx: zero-based starting column index of the repeating col-groups.
@param (int): wide_to_long_end_idx: zero-based ending column index of the repeating col-groups.
 
- transpose: convert rows to columns and vice versa.
 
- pivot: pivot repeating row-groups into cols.
@param (int): pivot_row_frequency: frequency of repeating row-groups.
 
- ffill: fill structurally empty cells in tables.
@param (int): ffill_end_idx: zero-based ending column index of the columns to be filled.
 
- explode: convert composite cells into atomic values
@param (int): explode_column_idx: zero-based column index of the column to be exploded.
 
- subtitle: convert table subtitles into a column.
 
OUTPUT the predicted transformation name along with its parameters for the given INPUT table in a JSON, e.g., {"transformation": "stack", "stack_start_idx": 1, "stack_end_idx": 5}. If the input table needs multi-step transformations (e.g, ffill followed by stack), list JSON of each transformation sequentially in a list, e.g., [{"transformation": "ffill", "ffill_end_idx": 1}, {"transformation": "stack", "stack_start_idx": 1, "stack_end_idx": 5}]. No explanation is needed.

## Input:
| Column1   | Column2            | Per Year   | Per Month    | Bi-Weekly   | Per Week    |
|:----------|:-------------------|:-----------|:-------------|:------------|:------------|
| Income:   |                    |            |              |             |             |
|           | Job                | 46990.0    | 3915.833333  | 1807.307692 | 903.6538462 |
|           | Investments        | 2000.0     | 166.6666667  | 83.33333333 | 38.46153846 |
|           | Allowances         | 2000.0     | 166.6666667  | 83.33333333 | 38.46153846 |
|           | Game Show Winnings | 649.0      | 54.08333333  | 27.04166667 | 12.48076923 |
| Taxes:    |                    |            |              |             |             |
|           | Federal            | 11876.97   | 989.7475     | 494.87375   | 228.4032692 |
|           | State              | 4647.51    | 387.2925     | 193.64625   | 89.37519231 |
| Expenses: |                    |            |              |             |             |
|           | Housing            | 10800.0    | 900.0        | 450.0       | 207.6923077 |
|           | Utilities          | 2000.0     | 166.6666667  | 83.33333333 | 38.46153846 |
|           | Food               | 4000.0     | 333.3333333  | 166.6666667 | 76.92307692 |
|           | Clothing           | 2000.0     | 166.6666667  | 83.33333333 | 38.46153846 |
|           | Car Payments       | 2200.0     | 183.3333333  | 91.66666667 | 42.30769231 |
|           | Car Insurance      | 1700.0     | 141.6666667  | 70.83333333 | 32.69230769 |
|           | Transportation     | 700.0      | 58.33333333  | 29.16666667 | 13.46153846 |
|           | Travel             | 500.0      | 41.66666667  | 20.83333333 | 9.615384615 |

## Output:


source: excel_file
label: subtitle