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:
| Paper Numb   | Speaker 1 Name   | Speaker 1 Country   | Speaker 2 Name   | Speaker 2 Country   | Speaker 3 Name   | Speaker 3 Country   | Chair 1 Name   | Chair 1 Country   | Chair 2 Name   | Chair 2 Country   |
|:-------------|:-----------------|:--------------------|:-----------------|:--------------------|:-----------------|:--------------------|:---------------|:------------------|:---------------|:------------------|
| Paper 1      | Ali              | Malaysia            | Burhan           | China               | Daud             | Brazil              | Dollah         | UAE               | Zikri          | Pakistan          |
| Paper 2      | Abu              | Malaysia            | Bashah           | India               | Emilia           | US                  | Farid          | Vietnam           | Adli           | Pakistan          |
| Paper 3      | Arif             | Malaysia            | Brent            | US                  | Emy              | Singapore           | Fuad           | India             | Harun          | Brazil            |

## Output:


source: excel_forum
label: operator: wide_to_long
wide_to_long_start_idx: 1
wide_to_long_end_idx: 10
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:
|   id | a_1   | a_2   | a_3   |   b_1 |   b_2 |   b_3 | c_1   | c_2   | c_3   |
|-----:|:------|:------|:------|------:|------:|------:|:------|:------|:------|
|  101 | a     | b     | c     |     1 |     2 |     3 | aa    | bb    | cc    |
|  102 | d     | e     | f     |     4 |     5 |     6 | dd    | ee    | ff    |
|  103 | g     | h     | i     |     7 |     8 |     9 | gg    | hh    | ii    |
|  104 | o     | p     | q     |    10 |    11 |    12 | oo    | pp    | qq    |
|  105 | r     | s     | t     |    13 |    14 |    15 | rr    | ss    | tt    |
|  106 | u     | v     | w     |    16 |    17 |    18 | uu    | vv    | ww    |
|  107 | x     | y     | z     |    19 |    20 |    21 | xx    | yy    | zz    |

## Output:


source: stackoverflow
label: operator: wide_to_long
wide_to_long_start_idx: 1
wide_to_long_end_idx: 9
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:
|   ID | Category_1   |   Quantity_1 | Category_2   |   Quantity_2 | Category_3   |   Quantity_3 |
|-----:|:-------------|-------------:|:-------------|-------------:|:-------------|-------------:|
|    1 | kitchen      |           64 | Home         |           24 | Bath         |           45 |
|    2 | Bed          |           24 | Men          |           35 | Cosemestics  |           49 |
|    3 | Kids         |           22 | Kitchen      |           45 | Men          |           52 |

## 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:
| Date       |   Invoice# | Customer   | PayMethod   |   Total | serviceCat1   | serviceCat2   | prodCat1   | prodCat2   |
|:-----------|-----------:|:-----------|:------------|--------:|:--------------|:--------------|:-----------|:-----------|
| 01.01.2022 |          1 | A          | cash        |  1000   | 200.0         |               | 800.0      |            |
| 01.01.2022 |          2 | B          | cc          |    25   |               |               |            | 25.0       |
| 02.01.2022 |          3 | C          | paypall     |   333.5 |               |               | 233.5      | 100.0      |
| 02.01.2022 |          4 | D          | cash        |   666   |               | 666.0         |            |            |

## Output:


source: notebook
label: operator: wide_to_long
wide_to_long_start_idx: 5
wide_to_long_end_idx: 8
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Role                      | Voice type    | Premiere cast, Kiev Opera House, 1975 (Conductor: Stefan Turchak)   |
|:--------------------------|:--------------|:--------------------------------------------------------------------|
| [MISSING]                 | bass          | Oleksandr Zgurovskiy                                                |
| Ingigerda, his wife       | soprano       | Gisela Tsipola                                                      |
| Elizaveta, their daughter | soprano       | Nadiya Kudelya                                                      |
| Anna, their daughter      | mezzo-soprano | nan                                                                 |
| Harald, a Norwegian noble | baritone      | Anatoliy Mokryenko                                                  |
| Mykyta, a scribe          | tenor         | nan                                                                 |
| Djemma, a Sicilian girl   | mezzo-soprano | Galyna Tuftina                                                      |
| Zhureyko, a stonemason    | tenor         | Andriy Ishchenko                                                    |
| Mylusha, his wife         | soprano       | nan                                                                 |
| Silvestr, head scribe     | bass          | Anatoliy Kocherga                                                   |



row_idx: 0
col_idx: 0
col_name: Role
label: Yaroslav the Wise, Prince of Kiev
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Episode   | Broadcasting   | Challenge Winner(Initial Challenge)   |
|:----------|:---------------|:--------------------------------------|
| Episode 1 | April 6, 2012  | Freddy                                |
| Episode 1 | April 6, 2012  | Klahan                                |
| Episode 2 | April 13, 2012 | Klahan                                |
| Episode 3 | April 20, 2012 | Nekmao                                |
| Episode 4 | April 27, 2012 | Freddy                                |
| Episode 5 | May 4, 2012    | Bertrand                              |
| Episode 6 | May 11, 2012   | Teheiura                              |
| Episode 7 | May 18, 2012   | Claude                                |
| [MISSING] | May 25, 2012   | Bertrand                              |
| Episode 9 | June 1, 2012   | Claude                                |



row_idx: 8
col_idx: 0
col_name: Episode
label: Episode 8
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Name               | League    |   FA Cup |   Total |
|:-------------------|:----------|---------:|--------:|
| Jimmy Hampson      | 45        |        1 |      46 |
| Percy Downes       | [MISSING] |        0 |      13 |
| Billy Upton        | 12        |        0 |      12 |
| Charles Broadhurst | 5         |        0 |       5 |
| Alex Ritchie       | 5         |        0 |       5 |
| Jock Lauderdale    | 4         |        0 |       4 |
| Jack Oxberry       | 3         |        0 |       3 |
| Cyril Quinn        | 3         |        0 |       3 |
| Billy Tremelling   | 3         |        0 |       3 |
| Dick Neal          | 2         |        0 |       2 |
| Tommy Browell      | 0         |        2 |       2 |
| Willie Hall        | 1         |        0 |       1 |
| Charlie Rattray    | 1         |        0 |       1 |
| Albert Watson      | 1         |        0 |       1 |



row_idx: 1
col_idx: 1
col_name: League
label: 13
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Model                          | Year(s)      | Country of assembly   | Original model      |
|:-------------------------------|:-------------|:----------------------|:--------------------|
| Ford Maverick (Australia)      | 1988-1994    | [MISSING]             | Nissan Patrol       |
| Ford Courier                   | 1985-2006    | Japan/Thailand        | Mazda B-Series      |
| Ford Laser                     | 1981-2003    | Japan                 | Mazda 323           |
| Ford Taurus Ghia (Australia)   | 1996         | United States         | Ford Taurus         |
| Ford Taurus Ghia (New Zealand) | 1996-1998    | United States         | Ford Taurus         |
| Holden Barina                  | 1985-1994    | Japan                 | Suzuki Cultus       |
| Holden Barina                  | 1994-2005    | Spain                 | Opel Corsa          |
| Holden Barina                  | 2005-2011    | South Korea           | Daewoo Kalos        |
| Holden Camira JJ (New Zealand) | 1984-1987    | Japan                 | Isuzu Aska          |
| Holden Commodore               | 2017-present | Germany               | Opel Insignia       |
| Holden Cruze                   | 2001-2008    | Japan                 | Suzuki Swift        |
| Holden Cruze                   | 2008-present | South Korea           | Chevrolet Cruze     |
| Holden Jackaroo                | 1981-2005    | Japan                 | Isuzu Bighorn       |
| Holden Suburban                | 1998-2001    | Mexico                | Chevrolet Suburban  |
| Holden Vectra                  | 1995-2007    | Germany               | Opel Vectra         |
| Holden Viva                    | 2005-2008    | South Korea           | Daewoo Lacetti      |
| Holden Volt                    | 2013-present | United States         | Chevrolet Volt      |
| Pontiac LeMans (New Zealand)   | 1986-1994    | South Korea           | Daewoo LeMans       |
| Rambler Rebel                  | 1967-1970    | United States         | AMC Rebel           |
| Rover 416i                     | 1985-1989    | Japan                 | Honda Quint Integra |



row_idx: 0
col_idx: 2
col_name: Country of assembly
label: Japan
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
|   Year | Award                                   | Work/Artist                  | Result    |
|-------:|:----------------------------------------|:-----------------------------|:----------|
|   1995 | Songwriter of the Year                  | Daniel Johns and Ben Gillies | Won       |
|   1996 | Most Performed Australian Work Overseas | "Tomorrow"                   | Won       |
|   2003 | Songwriter of the Year                  | Daniel Johns                 | Won       |
|   2003 | Song of the Year                        | "The Greatest View"          | Nominated |
|   2003 | Song of the Year                        | "Without You"                | Nominated |
|   2003 | Most Performed Australian Work          | "The Greatest View"          | Nominated |
|   2004 | Song of the Year                        | "Across the Night"           | Nominated |
|   2008 | Song of the Year                        | "Straight Lines"             | Won       |
|   2008 | Most Played Australian Work             | "Straight Lines"             | [MISSING] |
|   2008 | Songwriter of the Year                  | Daniel Johns                 | Won       |



row_idx: 8
col_idx: 3
col_name: Result
label: Won
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Artist             | Album / Single                                                                                                 |   Year |
|:-------------------|:---------------------------------------------------------------------------------------------------------------|-------:|
| Montserrat Caballe | Friends for Life                                                                                               |   1998 |
| Phoenix Down       | Under a Wild Sky                                                                                               |   1999 |
| Various Artists    | Familiar Faces: A Tribute to Rod Stewart & the Faces                                                           |   2000 |
| The Streets        | A Grand Don't Come for Free                                                                                    |   2004 |
| Billy Currie       | Pieces of the Puzzle                                                                                           |   2004 |
| Phat Beach         | "I'll Be Ready" (Baywatch Theme)                                                                               |   2006 |
| Young Punx         | [MISSING]                                                                                                      |   2007 |
| Phonat             | "Set Me Free", "Get Down My Dirty Street", "Warm Welcome" and "Learn to Recycle"                               |   2009 |
| Dizzee Rascal      | Tongue n' Cheek - Deluxe Edition (live from the BBC Electric Proms) on "Fix Up Look Sharp" and "Brand New Day" |   2010 |
| Lauren Simeca      | Roxanne re-mix 2010                                                                                            |    nan |
| Urban Myth         | Africa 2010/11                                                                                                 |    nan |



row_idx: 6
col_idx: 1
col_name: Album / Single
label: "Your Music is Killing Me"
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| n         | Pular                                                                 | English                                                                    |
|:----------|:----------------------------------------------------------------------|:---------------------------------------------------------------------------|
| 1         | Midho jogii bhibbe dhidho hodhu bhe Pari.                             | I have two children who live in Paris.                                     |
| 2         | Suka an hodhu dho Niw york on no nawni.                               | My son who lives in New York is sick.                                      |
| 3         | Midho andi mawdho mo suka mun jogii otooje tati.                      | I know an old man whose son has three vehicles.                            |
| 4         | Ko hombo jeyi dhii vellooji dhi pineeji mun haajitoraa hendu.         | Who owns these bicycles whose tires don't need air?                        |
| [MISSING] | Medhen yewtude fii worbhe bhe bheynguuli mun dogi sabu angal kaalisi. | We are talking about men whose wives left because of a lack of money.      |
| 6         | Mi faalaaka gorko mo maraa jawdi.                                     | I don't want a man who does not have money.                                |
| 7         | Mi yidhaa gnaamugol maafe ko wadhaaka gnamaku.                        | I don't like any sauce that doesn't have pepper.                           |
| 8         | Midho andi hidha seytini.                                             | I know that you are upset. Note that the relative pronoun is omitted here. |
| 9         | Mi nanii wondema hanki hari hibhe nawni.                              | I heard that they were sick yesterday.                                     |
| 10        | Mi jangii e deftere wondema leydi ndin no murlidhiri wa balonre.      | I read in a book that the earth is round like a soccer ball.               |
| 11        | Bhe hoolaaki wondema wakkilaare bheyday arsike gorko.                 | They don't believe that hardwork can increase a man's luck.                |
| 12        | Midho sikki tun o alaa e yeetaade en ngoonga on.                      | I just think that he/she is not telling us the truth.                      |
| 13        | Mi nanuno ka radioo hanki woo (wondema) gere on bhuttii.              | I heard on the radio yesterday that the war has ended.                     |



row_idx: 4
col_idx: 0
col_name: n
label: 5
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Unnamed: 0         | plant part   | ppm              |
|:-------------------|:-------------|:-----------------|
| [MISSING]          | seed         | nan              |
| Ash                | seed         | < 14,200         |
| Caffeine           | plant        | < 25,000         |
| Caffeine           | seed         | 9,100 - 76,000   |
| Catechutannic-acid | seed         | nan              |
| Choline            | seed         | nan              |
| D-catechin         | seed         | nan              |
| Fat                | seed         | < 30,000         |
| Guanine            | seed         | nan              |
| Guaranine          | seed         | nan              |
| Hypoxanthine       | seed         | nan              |
| Mucilage           | seed         | nan              |
| Protein            | seed         | < 98,600         |
| Resin              | seed         | < 70,000         |
| Saponin            | seed         | nan              |
| Starch             | seed         | 50,000 - 60,000  |
| Tannin             | seed         | 50,000 - 120,000 |
| Theobromine        | seed         | 200 - 400        |
| Theophylline       | seed         | 0 - 2500         |
| Timbonine          | seed         | nan              |
| Xanthine           | seed         | nan              |



row_idx: 0
col_idx: 0
col_name: Unnamed: 0
label: Adenine
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Sport              | Coach                | Season    | Record   |
|:-------------------|:---------------------|:----------|:---------|
| Boys Football      | Coach Travis Nolting | Fall      | 42-21    |
| Girls Golf         | nan                  | Fall      | Unknown  |
| Boys Tennis        | nan                  | Fall      | Unknown  |
| Girls Volleyball   | nan                  | [MISSING] | nan      |
| Coed Cross Country | Jeff Ransford        | Fall      | Unknown  |
| Boys Basketball    | Mr. Pherson          | Winter    | nan      |
| Girls Basketball   | Joey Davidson        | Winter    | nan      |
| Boys Golf          | Aaron Greve          | Spring    | Unknown  |
| Boys Baseball      | Joe Kutch            | Spring    | 3-8      |
| Girls Softball     | Erica Arnold         | Spring    | Unknown  |
| Coed Track         | Jamie Huff           | Spring    | Unknown  |



row_idx: 3
col_idx: 2
col_name: Season
label: Fall
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Times Hosting   | Park                             | Years            |
|:----------------|:---------------------------------|:-----------------|
| 3               | Holiday World & Splashin' Safari | 2001, 2006, 2011 |
| 2               | Cedar Point                      | 2004, 2016       |
| 2               | Dollywood                        | 2007, 2012       |
| 1               | Busch Gardens Williamsburg       | 2010             |
| 1               | Kings Island                     | 2002             |
| 1               | Legoland California              | 2009             |
| [MISSING]       | Luna Park, Coney Island          | 2015             |
| 1               | Santa Cruz Beach Boardwalk       | 2013             |
| 1               | Schlitterbahn                    | 2003             |
| 1               | SeaWorld San Diego               | 2014             |
| 1               | Six Flags Fiesta Texas           | 2005             |



row_idx: 6
col_idx: 0
col_name: Times Hosting
label: 1
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Nationality           |   Trips |   Fliers | Notes                                                                                                                 |
|:----------------------|--------:|---------:|:----------------------------------------------------------------------------------------------------------------------|
| Soviet Union / Russia |      68 |       42 | one woman, 35 Mir crew members, 13 double and three triple flights, one quadruple and one quintuple flight            |
| United States         |      49 |       44 | eight women, seven Mir crew members, three double flights and one triple flight                                       |
| France                |       8 |        6 | one woman, one Mir crew member, two double flights                                                                    |
| Germany               |       4 |        4 | [MISSING]                                                                                                             |
| Afghanistan           |       1 |        1 | nan                                                                                                                   |
| Austria               |       1 |        1 | nan                                                                                                                   |
| Bulgaria              |       1 |        1 | nan                                                                                                                   |
| Canada                |       1 |        1 | nan                                                                                                                   |
| Japan                 |       1 |        1 | one tourist                                                                                                           |
| Slovakia              |       1 |        1 | nan                                                                                                                   |
| Syria                 |       1 |        1 | nan                                                                                                                   |
| United Kingdom        |       1 |        1 | one woman                                                                                                             |
| Total                 |     137 |      104 | 11 women, 44 Mir crew members, one tourist, 18 double and four triple flights, one quadruple and one quintuple flight |



row_idx: 3
col_idx: 3
col_name: Notes
label: one Mir crew member
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Name                         | Area         | Population   |
|:-----------------------------|:-------------|:-------------|
| Center City                  | 0.381 sq. mi | 5,374        |
| Callowhill                   | 0.751 sq. mi | 7,289        |
| Centre Park                  | 0.615 sq. mi | 10,781       |
| College Heights              | 1.295 sq. mi | 14,903       |
| East Reading                 | 2.230 sq. mi | 34,572       |
| Eastside                     | 1.849 sq. mi | 29,198       |
| Glenside                     | 2.303 sq. mi | 11,837       |
| Hampden Heights              | 3.144 sq. mi | 44,101       |
| Millmont                     | 1.024 sq. mi | 5,298        |
| North Riverside              | 0.955 sq. mi | 12,674       |
| Northmont                    | 0.035 sq. mi | 697          |
| Northside                    | [MISSING]    | 1,822        |
| Oakbrook/Wyomissing Park     | 1.197 sq. mi | 5,947        |
| Outlet District              | 0.554 sq. mi | 14,295       |
| Penn's Commons               | 0.796 sq. mi | 15,891       |
| Prince Historic District     | 0.123 sq. mi | 2,002        |
| Queen Anne Historic District | 0.330 sq. mi | 6,359        |
| Southside                    | 1.486 sq. mi | 10,317       |
| South of Penn                | 1.122 sq. mi | 8,483        |



row_idx: 11
col_idx: 1
col_name: Area
label: 0.187 sq. mi
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
|   Calendar year | US sales(Ford Escape)           | US sales(Mercury Mariner)   |
|----------------:|:--------------------------------|:----------------------------|
|            2000 | 42,635                          | nan                         |
|            2001 | 164,184                         | nan                         |
|            2002 | 145,471                         | nan                         |
|            2003 | [MISSING]                       | nan                         |
|            2004 | 183,430 - (inc. 2,993 hybrids)  | 7,171                       |
|            2005 | 165,122 - (inc. 18,797 hybrids) | 34,099                      |
|            2006 | 157,395 - (inc. 20,149 hybrids) | 33,941                      |
|            2007 | 165,596 - (inc. 21,386 hybrids) | 34,844                      |
|            2008 | 156,544 - (inc. 17,173 hybrids) | 32,306                      |
|            2009 | 173,044 - (inc. 14,787 hybrids) | 28,688                      |
|            2010 | 191,026 - (inc. 11,182 hybrids) | 29,912                      |
|            2011 | 254,293 - (inc. 10,089 hybrids) | nan                         |
|            2012 | 261,008 - (inc. 1,441 hybrids)  | nan                         |
|            2013 | 295,993                         | nan                         |
|            2014 | 306,212                         | nan                         |
|            2015 | 306,492                         | nan                         |



row_idx: 3
col_idx: 1
col_name: US sales(Ford Escape)
label: 167,678
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Political party                          |   Votes in % | Seats     |
|:-----------------------------------------|-------------:|:----------|
| Christian Democratic Appeal              |        22.44 | 11        |
| People's Party for Freedom and Democracy |        12.43 | 6         |
| Socialist Party (Netherlands)            |        10.7  | 5         |
| Dutch Labour Party                       |        10.59 | 5         |
| Democrats 66                             |        10.47 | 5         |
| Party for Freedom                        |         9.43 | 5         |
| ChristianUnion                           |         8.37 | 4         |
| Reformed Political Party                 |         4.31 | 2         |
| GreenLeft                                |         4.3  | 2         |
| 50PLUS                                   |         2.91 | 1         |
| Party for the Animals                    |         2.24 | [MISSING] |
| Other parties                            |         0.66 | 0         |
| Total                                    |       nan    | 47        |



row_idx: 10
col_idx: 2
col_name: Seats
label: 1
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Game                                                | PAL                 | North America   |
|:----------------------------------------------------|:--------------------|:----------------|
| Disney's Aladdin                                    | Released            | Unreleased      |
| Disney's Magical Quest                              | Released            | [MISSING]       |
| Disney's Magical Quest 2: The Great Circus Mystery  | Released            | Unreleased      |
| Donkey Kong Country                                 | 1998                | 1997            |
| Donkey Kong Country 2: Diddy's Kong Quest           | 1998                | 1998            |
| Donkey Kong Country 3: Dixie Kong's Double Trouble! | 1998                | 1998            |
| F-Zero                                              | Unknown if released | 1996            |
| The Jungle Book                                     | Released            | Unreleased      |
| The Legend of Zelda: A Link to the Past             | 1998                | 1996            |
| The Lion King                                       | Released            | Unreleased      |
| Mario Paint                                         | Released            | 1996            |
| SimCity                                             | 1998                | 1996            |
| Super Bomberman 2                                   | 1998                | Unreleased      |
| Super Mario All-Stars                               | 1997                | 1996            |
| Super Mario Kart                                    | 1997                | May 20, 1996    |
| Super Mario World                                   | 1997                | 1996            |
| Super Mario World 2: Yoshi's Island                 | Released            | Unreleased      |
| Super Metroid                                       | 1998                | 1996            |
| Super Star Wars                                     | Unknown if released | 1996            |
| Super Street Fighter II                             | 1997                | Unreleased      |
| Tetris & Dr. Mario                                  | 1997                | 1996            |
| Tetris 2                                            | Unknown if released | 1996            |
| Timon & Pumbaa's Jungle Games                       | Released            | Unreleased      |



row_idx: 1
col_idx: 2
col_name: North America
label: Unreleased
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
|   No | Player           | Team        |   Mat |   Ovs |   Runs |   Wkts |   Avg |
|-----:|:-----------------|:------------|------:|------:|-------:|-------:|------:|
|    1 | Mashrafe Mortaza | Bangladesh  |     7 |  64   |    250 |     17 | 14.7  |
|    2 | [MISSING]        | India       |     6 |  47   |    163 |      9 | 18.11 |
|    3 | Tawanda Mupariwa | Zimbabwe    |    10 |  93   |    438 |     22 | 19.9  |
|    4 | Dwayne Bravo     | West Indies |     9 |  62.5 |    283 |     14 | 20.21 |
|    5 | Jerome Taylor    | West Indies |     8 |  67   |    286 |     14 | 20.42 |
|    8 | Lasith Malinga   | Sri Lanka   |     7 |  56.2 |    283 |     13 | 21.76 |
|    9 | Peter Ongondo    | Kenya       |     5 |  42   |    154 |      7 | 22    |
|   10 | John Davison     | Canada      |     6 |  51   |    208 |      9 | 23.11 |
|   11 | Mohammad Asif    | Pakistan    |     6 |  57   |    209 |      9 | 23.22 |
|   14 | Steve Harmison   | England     |     6 |  60   |    344 |     11 | 31.27 |
|   29 | Saleem Mukuddem  | Bermuda     |     5 |  46   |    196 |      3 | 65.33 |



row_idx: 1
col_idx: 1
col_name: Player
label: Ajit Agarkar
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
|   Wins | Driver          | Vehicle                     |
|-------:|:----------------|:----------------------------|
|   1975 | Rod Millen      | Mazda RX-3                  |
|   1976 | Rod Millen      | Mazda RX-3                  |
|   1977 | Rod Millen      | Mazda RX-3                  |
|   1978 | Blair Robson    | Ford Escort RS1800          |
|   1979 | Paul Adams      | Ford Escort RS1800          |
|   1980 | Jim Donald      | Ford Escort RS1800          |
|   1981 | Jim Donald      | Ford Escort RS1800          |
|   1982 | Tony Teesdale   | Ford Escort RS1800          |
|   1983 | Malcolm Stewart | Ford Escort RS1800          |
|   1984 | Tony Teesdale   | Nissan 240RS                |
|   1985 | Brian Stokes    | Ford Escort RS              |
|   1986 | Neil Allport    | Mazda RX-7                  |
|   1987 | Tony Teesdale   | MG Metro 6R4                |
|   1988 | Brian Stokes    | Ford Sierra 4x4             |
|   1989 | Neil Allport    | Mazda 323 4WD               |
|   1990 | David Ayling    | Mazda 323 4WD               |
|   1991 | Possum Bourne   | Subaru Legacy               |
|   1992 | Neil Allport    | Mazda 323 GTX               |
|   1993 | Joe McAndrew    | Subaru Legacy               |
|   1994 | Joe McAndrew    | Subaru Legacy               |
|   1995 | Reece Jones     | Mitsubishi Lancer Evolution |
|   1996 | Joe McAndrew    | Subaru Legacy               |
|   1997 | Reece Jones     | Mitsubishi Lancer Evolution |
|   1998 | Geoff Argyle    | Mitsubishi Lancer Evolution |
|   1999 | Geoff Argyle    | Mitsubishi Lancer Evolution |
|   2000 | Bruce Herbert   | Mitsubishi Lancer Evolution |
|   2001 | Bruce Herbert   | Subaru Impreza WRX          |
|   2002 | Bruce Herbert   | Subaru Impreza WRX          |
|   2003 | Bruce Herbert   | Subaru Impreza WRX          |
|   2004 | Chris West      | Subaru Impreza WRX          |
|   2005 | Richard Mason   | Subaru Impreza WRX          |
|   2006 | Richard Mason   | Subaru Impreza WRX          |
|   2007 | Sam Murray      | Subaru Impreza WRX          |
|   2008 | Hayden Paddon   | Mitsubishi Lancer Evolution |
|   2009 | Hayden Paddon   | Mitsubishi Lancer Evolution |
|   2010 | Dean Sumner     | Mitsubishi Lancer Evolution |
|   2011 | Richard Mason   | Subaru Impreza WRX          |
|   2012 | Richard Mason   | Subaru Impreza WRX          |
|   2013 | [MISSING]       | Mitsubishi Lancer Evo IX    |
|   2014 | Richard Mason   | Subaru Impreza WRX          |
|   2015 | Ben Hunt        | Subaru Impreza WRX          |
|   2016 | David Holder    | Mitsubishi Lancer Evo IX    |



row_idx: 38
col_idx: 1
col_name: Driver
label: Hayden Paddon
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
| Country       | Date              | Format               | Label             | Catalog       |
|:--------------|:------------------|:---------------------|:------------------|:--------------|
| Australia     | December 18, 2009 | CD, digital download | Universal Music   | 2725654       |
| Germany       | December 18, 2009 | CD, digital download | Universal Music   | 0602527256542 |
| Italy         | December 18, 2009 | CD, digital download | Universal Music   | 060252725654  |
| Greece        | December 18, 2009 | CD, digital download | Universal Music   | 060252725654  |
| Sweden        | December 18, 2009 | CD, digital download | Universal Music   | 060252725654  |
| Netherlands   | December 18, 2009 | CD, digital download | Universal Music   | 060252725654  |
| Switzerland   | December 18, 2009 | [MISSING]            | Universal Music   | 060252725654  |
| France        | December 21, 2009 | CD, digital download | Barclay Records   | 060252725654  |
| United States | December 21, 2009 | CD, digital download | Matriarch Records | 602527256542  |
| Japan         | December 23, 2009 | CD                   | Universal Music   | UICF9065      |
| Korea         | February 2, 2010  | CD                   | Universal Music   | DF6424        |
| Brazil        | February 2, 2010  | Digital download     | Universal Music   | nan           |



row_idx: 6
col_idx: 2
col_name: Format
label: CD, digital download
Task: Data-Imputation (source dataset: WebTable)

Please fill in the missing value in the input table. The missing value is denoted by '[MISSING]'. Please only return the value filled in. Do not return the whole table.

No explanation, return the value filled in JSON format: {"value": "filled_value"}. 

Input Table:
|   Year | Recipient                  | Area of study   |
|-------:|:---------------------------|:----------------|
|   2000 | Hans Dieter Betz           | New Testament   |
|   2001 | Rudolf Smend               | Hebrew Bible    |
|   2002 | Gerd Theissen              | New Testament   |
|   2003 | Bertil Albrektson          | [MISSING]       |
|   2004 | Morna Hooker               | New Testament   |
|   2005 | Pierre-Maurice Bogaert OSB | Hebrew Bible    |
|   2006 | Graham Stanton             | New Testament   |
|   2007 | Alberto Soggin             | Hebrew Bible    |
|   2008 | Richard Bauckham           | New Testament   |
|   2009 | Ernest Nicholson           | Hebrew Bible    |



row_idx: 3
col_idx: 2
col_name: Area of study
label: Hebrew Bible