MMTU-benchmark/MMTU huggingface dataset:
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:
| Property type   | 1 bed         | 2 beds       | 3 beds         | 4 beds         | 5 beds         |
|:----------------|:--------------|:-------------|:---------------|:---------------|:---------------|
| Houses          | 424 pcm (50)  | 983 pcm (8)  | 1,208 pcm (21) | 1,403 pcm (36) | 1,698 pcm (19) |
| Flats           | 488 pcm (59)  | 966 pcm (18) | 984 pcm (2)    | -              | -              |
| All             | 458 pcm (109) | 972 pcm (26) | 1,188 pcm (23) | 1,403 pcm (36) | 1,698 pcm (19) |

## Output:


source: web
label: operator: stack
stack_start_idx: 1
stack_end_idx: 5
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:
|   Class |   Election Year | End of Term   |   Democratic |   Republican |   Independent |
|--------:|----------------:|:--------------|-------------:|-------------:|--------------:|
|       1 |            2018 | 3-Jan-19      |           23 |            8 |             2 |
|       2 |            2020 | 3-Jan-21      |           11 |           22 |             0 |
|       3 |            2022 | 3-Jan-23      |           12 |           22 |             0 |

## Output:


source: web
label: operator: stack
stack_start_idx: 3
stack_end_idx: 5
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:
| product   |   red |   green |   yellow |   blue |   purple |   black |   white |   gray |
|:----------|------:|--------:|---------:|-------:|---------:|--------:|--------:|-------:|
| chair     |   100 |     173 |      114 |    143 |       34 |     120 |      76 |     25 |
| table     |   129 |     124 |      144 |     19 |      190 |     140 |      64 |     62 |
| bed       |    71 |      81 |      106 |     42 |      192 |     165 |      53 |     76 |
| cabinet   |   167 |      36 |       98 |     59 |       94 |      63 |      60 |    104 |
| clock     |   142 |      74 |       80 |     70 |       38 |      23 |     174 |     74 |
| desk      |   116 |      89 |      126 |     23 |       81 |      73 |     116 |    123 |
| futon     |    26 |      70 |       20 |     32 |       30 |     165 |      44 |     54 |
| sofa      |   177 |     176 |       85 |     42 |       94 |      46 |     158 |     71 |

## Output:


source: excel_forum
label: operator: stack
stack_start_idx: 1
stack_end_idx: 8
alternative_label: operator: transpose
operator: stack
stack_start_idx: 1
stack_end_idx: 8
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:
| Contractors   | Principal     | eMail   | Mobile/ Phone   | Project Manager   |
|:--------------|:--------------|:--------|:----------------|:------------------|
| NEW MAINS     |               |         |                 |                   |
| 0             | Peter Magnone | 0.0     | 0               | Chris Cow         |
| 1             | Stacy Miller  | 1.0     |                 |                   |
| 2             | 0             | 2.0     | 1               |                   |
| 3             | 1             | 3.0     | 2               |                   |
| NEW SERVICES  |               |         |                 |                   |
| 0             | Peter         | 0.0     | 0               |                   |
| 1             | Peter Beriman | 1.0     | 0458 712 393    |                   |
| 2             | 0             | 2.0     | 1               |                   |
| 3             | 1             | 3.0     | 2               |                   |

## Output:


source: excel_file
label: subtitle
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:
| Account_Nbr   | B999     | B888     | B777     | B666     | B555     | B444     | B333     | B222     | B111     |
|:--------------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| Identifier    | 189831   | 119461   | 145543   | 198741   | 150394   | 126456   | 195709   | 188088   | 133626   |
| Currency      | USD      | USD      | CNY      | CNY      | CNY      | USD      | USD      | USD      | USD      |
| Multiplier    | 100000   | 100000   | 100000   | 100000   | 100000   | 100000   | 100000   | 100000   | 100000   |
| Unit          | Currency | Currency | Currency | Currency | Currency | Currency | Currency | Currency | Currency |

## Output:


source: notebook
label: transpose
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       | 0                   | 1                   | 2                   | 3                   |
|:------------|:--------------------|:--------------------|:--------------------|:--------------------|
| City        | MSK                 | SPB                 | NN                  | SPB                 |
| Temperature | -3                  | 2                   | -3                  | 2                   |
| State       | Cloudy              | Clear               | Snow                | Clear               |
| Date        | 2017-01-16 00:00:00 | 2017-01-13 00:00:00 | 2017-03-13 00:00:00 | 2017-01-01 00:00:00 |

## Output:


source: notebook
label: transpose
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      | birthdate   | deathdate   | gender   | label   | work   |
|:-----------|:------------|:------------|:---------|:--------|:-------|
| Data Type  | date        | date        | string   | string  | list   |
| Density    | 0.864       | 0.447       | 1.0      | 1.0     | 0.234  |
| Uniqueness | 0.688       | 0.809       | 0.0      | 1.0     | 0.989  |

## Output:


source: notebook
label: transpose
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:
| Month                             | Jan      | Oct     | Nov      | Dec      | Year       | Feb      | Mar      | Apr      | May      | Jun      | Jul      | Aug      | Sep      |
|:----------------------------------|:---------|:--------|:---------|:---------|:-----------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|:---------|
| Average high °C (°F)              | 13 (55)  | 25 (77) | 18 (65)  | 14 (57)  | 24 (76)    | 16 (60)  | 20 (68)  | 26 (78)  | 29 (85)  | 34 (93)  | 34 (94)  | 34 (93)  | 30 (86)  |
| Average low °C (°F)               | −3 (26)  | 9 (48)  | 3 (37)   | −2 (28)  | 8 (46)     | −1 (30)  | 2 (36)   | 7 (45)   | 13 (55)  | 18 (64)  | 19 (66)  | 18 (65)  | 15 (59)  |
| Average precipitation mm (inches) | 15 (0.6) | 50 (2)  | 20 (0.8) | 18 (0.7) | 452 (17.8) | 18 (0.7) | 20 (0.8) | 28 (1.1) | 56 (2.2) | 61 (2.4) | 53 (2.1) | 46 (1.8) | 69 (2.7) |

## Output:


source: web
label: transpose
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:
| Unnamed: 0    | Rank     | Net worth   | Minimum assets   | Minimum liabilities   |
|:--------------|:---------|:------------|:-----------------|:----------------------|
| This year     | #120     | $2.19M      | $2.24M           | $0.05M                |
| Previous year | freshman |             |                  |                       |
| Change        |          | 0.00%       | 0.00%            | 0.00%                 |

## Output:


source: web
label: transpose
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:
| Statistics     |   JAN |   OCT |   NOV |   DEC |   FEB |   MAR |   APR |   MAY |   JUN |   JUL |   AUG |   SEP |
|:---------------|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|------:|
| Rain (mm)      |    34 |   103 |   105 |    76 |    14 |    56 |   154 |   236 |    88 |    72 |    68 |    67 |
| Rain days      |     3 |     9 |     8 |     7 |     1 |     5 |    10 |    14 |    10 |    10 |     8 |     9 |
| Day Temp       |    33 |    31 |    32 |    33 |    34 |    34 |    33 |    31 |    29 |    29 |    29 |    30 |
| Night Temp     |    22 |    21 |    22 |    22 |    23 |    23 |    23 |    22 |    20 |    19 |    19 |    20 |
| Sunshine hours |   270 |   273 |   264 |   260 |   255 |   270 |   225 |   205 |   207 |   211 |   245 |   246 |

## Output:


source: web
label: transpose
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:
| Data Type                |       100% |        75% |        50% |          25% |           0% |
|:-------------------------|-----------:|-----------:|-----------:|-------------:|-------------:|
| Qe [W]                   |  0.997344  |  0.990225  |  0.828582  |  0.860911    |  0.851212    |
| Qa [W]                   |  1.00835   |  1.03007   |  0.856254  |  0.891016    |  0.880588    |
| Mdot Hot [kg/s]          |  0.0411665 |  0.0411741 |  0.04129   |  0.0412668   |  0.0412738   |
| Mdot Cold [kg/s]         |  0.0426654 |  0.0393112 |  0.0266631 |  0.0291927   |  0.0284338   |
| Qf [W]                   | -0.0237655 | -0.0398407 | -0.0276719 | -0.0301056   | -0.0293755   |
| U [W/m2.K]               |  2.05004   |  1.97341   |  1.70195   |  1.75624     |  1.73995     |
| Mean Temp Efficiency [%] | 21.4242    | 21.6652    | 23.0189    | 22.7482      | 22.8294      |
| Vdot Hot [m3/min]        |  4.16e-05  |  4.16e-05  |  4.18e-05  |  4.17474e-05 |  4.17548e-05 |
| Vdot Cold [m3/min]       |  4.28e-05  |  3.94e-05  |  2.67e-05  |  2.92767e-05 |  2.85162e-05 |

## Output:


source: excel_file
label: transpose
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:
| Basis   | Description   | S1.Typ   | S1.H.UUID   |   S1.n | S1.E.UUID   | S2.Typ   | S2.H.UUID   |   S2.n | S2.E.UUID   | S3.Typ   | S3.H.UUID   | S3.n   | S3.E.UUID   |
|:--------|:--------------|:---------|:------------|-------:|:------------|:---------|:------------|-------:|:------------|:---------|:------------|:-------|:------------|
| AW01    | lore lipsum   | abc      | aaaa        |      1 | bbbb        | def      | cccc        |     33 | dddd        |          |             |        |             |
| AW02    | Shubi duu     | x11      | wwww        |      2 | zzzz        | y22      | tttt        |     44 | hhhh        | z33      | qqqq        | 3.0    | pppp        |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 2
wide_to_long_end_idx: 13
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:
| title             | authors             |        isbn13 |     isbn10 |   prefix13 |   prefix10 |
|:------------------|:--------------------|--------------:|-----------:|-----------:|-----------:|
| The Great Gatsby  | F. Scott Fitzgerald | 9780060098919 | 1572702567 |        978 |          1 |
| The Short Stories | Ernest Hemingway    | 9780684837864 |  684837862 |        978 |          0 |
| To the Lighthouse | Virginia Woolf      | 9780156030472 |  156030470 |        978 |          0 |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 2
wide_to_long_end_idx: 5
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:
| Title   | 1st Platform Impacted   | 1st Vendor Used   |   1st Vendor Spend | 2nd Platform Impacted   | 2nd Vendor Used   |   2nd Vendor Spend |
|:--------|:------------------------|:------------------|-------------------:|:------------------------|:------------------|-------------------:|
| Nori    | Engagement              | AXTRIA            |               0.01 | Pricing                 | ACCENTURE         |               0.01 |
| Deliver | Experience              | ACCENTURE         |               0.01 | Engagement              | AXTRIA            |               0.01 |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 1
wide_to_long_end_idx: 6
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:
| State   | Country   |   group1_a1 |   group1_b2 |   group2_a1 |   group2_b2 |
|:--------|:----------|------------:|------------:|------------:|------------:|
| TX      | US        |        0.45 |         0.3 |           2 |           6 |
| MA      | US        |        0.03 |         1.2 |           9 |           7 |
| ON      | CAN       |        0.7  |         4.2 |           4 |           2 |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 2
wide_to_long_end_idx: 5
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:
|   PK | Date       |   AG | ART   | MA1   | MA2   | MA3   |   BA1 |   BA2 | BA3   | BS1   | BS2   | BS3   | BZ1   | BZ2   | BZ3   |
|-----:|:-----------|-----:|:------|:------|:------|:------|------:|------:|:------|:------|:------|:------|:------|:------|:------|
|    1 | 01.01.2021 |    1 | A1    | M1    | M2    | M3    |   100 |   105 | 110.0 | 10.0  | 15.0  | 20.0  | 50.0  | 60.0  | 70.0  |
|    2 | 01.01.2021 |    1 | A2    | M2    | M3    |       |   100 |   113 |       | 11.0  | 12.0  | 18.0  | 8.0   | 9.0   |       |
|    3 | 02.01.2021 |    2 | A3    | M3    |       |       |   100 |     2 |       | 3.0   |       |       | 2.0   |       | 6.0   |
|    4 | 02.01.2021 |    5 | A3    |       |       |       |    12 |    13 |       |       |       | 8.0   |       |       | 8.0   |
|    5 | 03.01.2021 |    3 | A4    | M2    | M3    | M1    |   100 |   102 | 103.0 | 3.0   | 4.0   | 5.0   | 7.0   | 8.0   | 9.0   |
|    6 | 03.01.2021 |    1 | A5    | M3    | M1    |       |   100 |   105 | 7.0   | 3.0   | 5.0   |       | 8.0   | 8.0   |       |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 4
wide_to_long_end_idx: 15
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:
| Indicator   | Product   |   Val 2019 |   Val 2020 |   Qty 2019 |   Qty 2020 |
|:------------|:----------|-----------:|-----------:|-----------:|-----------:|
| Albania     | a         |       1571 |       1008 |          9 |          8 |
| Australia   | b         |       1994 |       2745 |          2 |         10 |
| Afghanistan | c         |       2453 |       2568 |          5 |          6 |
| Angola      | d         |       1868 |       2772 |          2 |          5 |
| Bahamas     | e         |       2879 |       1977 |          2 |          6 |
| Belarus     | f         |       2801 |       1411 |          8 |          8 |
| Belize      | g         |       1063 |       2482 |          2 |          8 |

## Output:


source: excel_forum
label: operator: wide_to_long
wide_to_long_start_idx: 2
wide_to_long_end_idx: 5
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:
| A1970   | A1980   |   B1970 |   B1980 |         X |   id |
|:--------|:--------|--------:|--------:|----------:|-----:|
| a       | d       |     2.5 |     3.2 | -1.08563  |    0 |
| b       | e       |     1.2 |     1.3 |  0.997345 |    1 |
| c       | f       |     0.7 |     0.1 |  0.282978 |    2 |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 0
wide_to_long_end_idx: 3