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:
| Index   |       label |    feature_1 |    feature_2 |   feature_3 |   feature_4 |   feature_5 |    feature_6 |   feature_7 |   feature_8 |   feature_9 |   feature_10 |   feature_11 |   feature_12 |   feature_13 |   feature_14 |   feature_15 |   feature_16 |   feature_17 |   feature_18 |   feature_19 |   feature_20 |
|:--------|------------:|-------------:|-------------:|------------:|------------:|------------:|-------------:|------------:|------------:|------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|-------------:|
| count   | 1200        | 1200         | 1200         | 1200        | 1200        | 1200        | 1200         | 1200        | 1200        | 1200        | 1200         |  1200        |  1200        |   1200       |  1200        | 1200         |  1200        | 1200         |  1200        |  1200        |  1200        |
| mean    |    0.494167 |    0.0453312 |    0.0646923 |    0.469713 |    0.824432 |    0.696126 |    0.148728  |    0.121921 |    0.123838 |    0.131366 |    0.0989935 |    -0.913202 |    -0.470814 |     -0.295   |     0.649646 |    0.0779178 |    -0.747141 |    0.0683418 |     0.106923 |    -0.747141 |    -0.399276 |
| std     |    0.500174 |    1.02326   |    1.97738   |    3.31213  |    3.15149  |    2.07877  |    2.23766   |    1.0038   |    0.986941 |    1.02063  |    1.00246   |     1.80325  |     1.97011  |      3.1543  |     1.8696   |    1.01313   |     2.7938   |    1.90023   |     2.17615  |     2.7938   |     2.05165  |
| min     |    0        |   -3.06505   |   -5.55217   |  -10.3231   |   -7.27702  |   -6.01384  |   -6.55528   |   -3.44346  |   -3.59995  |   -3.35413  |   -3.13456   |    -6.94079  |    -7.16337  |    -10.9197  |    -5.83341  |   -3.02039   |    -9.86004  |   -6.93591   |    -7.40592  |    -9.86004  |    -7.04145  |
| 25%     |    0        |   -0.630171  |   -1.32467   |   -1.68902  |   -1.39953  |   -0.637129 |   -1.3926    |   -0.551685 |   -0.530281 |   -0.550804 |   -0.598477  |    -2.14206  |    -1.79643  |     -2.35832 |    -0.664822 |   -0.614359  |    -2.66689  |   -1.24131   |    -1.3659   |    -2.66689  |    -1.75381  |
| 50%     |    0        |    0.0432454 |   -0.0358155 |    0.584852 |    0.761672 |    0.725794 |    0.0700194 |    0.142577 |    0.155913 |    0.132641 |    0.103372  |    -0.961021 |    -0.626837 |     -0.20016 |     0.581841 |    0.0958857 |    -0.748945 |    0.0877869 |     0.125861 |    -0.748945 |    -0.436407 |
| 75%     |    1        |    0.761692  |    1.39063   |    2.5813   |    2.83162  |    2.1169   |    1.56414   |    0.834815 |    0.781617 |    0.820978 |    0.789346  |     0.242986 |     0.672695 |      1.8329  |     1.95271  |    0.7565    |     1.11871  |    1.34056   |     1.61033  |     1.11871  |     1.02565  |
| max     |    1        |    3.72353   |    7.40901   |   12.361    |   13.2111   |   10.6725   |    7.46251   |    3.9109   |    3.60884  |    3.61165  |    2.76641   |     4.695    |     6.22688  |      9.2014  |     6.04216  |    2.99605   |     7.62479  |    5.65812   |     7.43981  |     7.62479  |     6.74396  |

## Output:


source: notebook
label: transpose