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:
| Size (UAA-ha)                 |    2 - |   10 - |   20 - |   30 - |    50 - |   >= 100 |   Hill Farms |   All Sizes |
|                               |   < 10 |   < 20 |   < 30 |   < 50 |   < 100 |          |              |             |
|:------------------------------|-------:|-------:|-------:|-------:|--------:|---------:|-------------:|------------:|
| No. of Farms in Sample        |   10   |  48    |  41    |  60    |   23    |      2   |        25    |      209    |
| Per Cent of Population        |    3   |   7.8  |   5.3  |   3.3  |    1    |      0.1 |         2.3  |       22.7  |
| Age of Holder                 |   52.8 |  56.1  |  51.6  |  49.5  |   52.7  |      0   |        49.1  |       52.8  |
| Marital status :- Married %   |   50   |  58.3  |  73.2  |  76.7  |   78.3  |      0   |        59.6  |       64.5  |
| Widowed %                     |   10   |   6.3  |   2.4  |   1.7  |    0    |      0   |         7    |        5    |
| Single %                      |   40   |  33.3  |  24.4  |  21.7  |   21.7  |      0   |        26.5  |       29.1  |
| Household Size (no.)          |    3.3 |   3.31 |   3.32 |   3.9  |    3.83 |      0   |         3.56 |        3.45 |
| < 24 (no.)                    |    1.2 |   1.33 |   1.17 |   1.6  |    1.48 |      0   |         1.44 |        1.34 |
| < 24 % HH                     |   50   |  50    |  48.8  |  65    |   56.5  |      0   |        44.8  |       51.8  |
| 25-44 (no.)                   |    0.8 |   0.48 |   0.71 |   0.85 |    0.96 |      0   |         0.65 |        0.67 |
| 25-44 % HH                    |   50   |  39.6  |  51.2  |  53.3  |   69.6  |      0   |        48.6  |       48.1  |
| Demograph. viable % HH        |   80   |  72.9  |  75.6  |  86.7  |   91.3  |      0   |        81.5  |       78.2  |
| Off-farm Job Hold/Spouse % HH |   50   |  52.1  |  63.4  |  65    |   56.5  |      0   |        75.8  |       58.9  |
| " Holder  % HH                |   50   |  47.9  |  53.7  |  46.7  |   26.1  |      0   |        64.6  |       50.1  |
| " Spouse % HH                 |   10   |  14.6  |  29.3  |  38.3  |   47.8  |      0   |        24.4  |       23.4  |
| Pensions (no.)                |    0.4 |   0.33 |   0.22 |   0.07 |    0.09 |      0   |         0.15 |        0.25 |
| 0                             |   30   |  27.1  |  17.1  |   5    |    4.3  |      0   |        14.7  |       19.6  |
| Unemploy Etc. (no.)           |    0.3 |   0.25 |   0.12 |   0.05 |    0.04 |      0   |         0.25 |        0.19 |
| 0                             |   30   |  20.8  |  12.2  |   5    |    4.3  |      0   |        25.5  |       17.4  |

## Output:


source: excel_file
label: transpose