MMTU-benchmark/MMTU huggingface dataset:
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                                                                                                     |
|:--------------------------------------------------------------------------------------------------------------------------|
| Mar 18 1. Warm sunsets, Lake Washington, Downtown Seattle Skyline. Act fast! $2829 / 3br - 1535ft2 - (Renton, WA) pic map |
| Mar 23 Downtown Seattle Skyline. Act fast! $2829 / 3br - 355ft2 - (Seattle, WA) pic map                                   |
| Mar 18 New AVAILABLE NOW FURNISHED $1650 / 1br - 750ft2 - (Bellevue) pic                                                  |

Output Table:
| Input                                                                                                                     |   Output |
|:--------------------------------------------------------------------------------------------------------------------------|---------:|
| Mar 18 1. Warm sunsets, Lake Washington, Downtown Seattle Skyline. Act fast! $2829 / 3br - 1535ft2 - (Renton, WA) pic map |     1535 |
| Mar 23 Downtown Seattle Skyline. Act fast! $2829 / 3br - 355ft2 - (Seattle, WA) pic map                                   |      355 |
| Mar 18 New AVAILABLE NOW FURNISHED $1650 / 1br - 750ft2 - (Bellevue) pic                                                  |      750 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_craigslist1.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| bb      |
| eeeeeee |
| ccc     |

Output Table:
| Input   | Output   |
|:--------|:---------|
| bb      | bb       |
| eeeeeee | eee      |
| ccc     | ccc      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_firstkchar_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                                              |
|:-------------------------------------------------------------------|
| | style="text-align:  center;"| {{Sort|02|[[Super Bowl II|II]]}}   |
| | style="text-align:  center;"| {{Sort|03|[[Super Bowl V|III]]}}   |
| | style="text-align:  center;"| {{Sort|03|[[Super Bowl III|III]]}} |

Output Table:
| Input                                                              | Output         |
|:-------------------------------------------------------------------|:---------------|
| | style="text-align:  center;"| {{Sort|02|[[Super Bowl II|II]]}}   | Super Bowl II  |
| | style="text-align:  center;"| {{Sort|03|[[Super Bowl V|III]]}}   | Super Bowl V   |
| | style="text-align:  center;"| {{Sort|03|[[Super Bowl III|III]]}} | Super Bowl III |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_duke_ds_1.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                               |
|:------------------------------------|
| {{x, aaay}, {aaaz, d}}              |
| {{a}, {b}, {c}}                     |
| {{a}, {ffeaa}, {aa, aaa, aaa, aaa}} |

Output Table:
| Input                               | Output   |
|:------------------------------------|:---------|
| {{x, aaay}, {aaaz, d}}              | aaaz     |
| {{a}, {b}, {c}}                     | b        |
| {{a}, {ffeaa}, {aa, aaa, aaa, aaa}} | ffeaa    |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_hierarchical1_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                        |
|:-----------------------------|
| x, aaay; aaaz, d             |
| ccc; aaa, bbb, ccc, ddd; xyz |
| a; ffeaa; aa, aaa, aaa, aaa  |

Output Table:
| Input                        | Output   |
|:-----------------------------|:---------|
| x, aaay; aaaz, d             | aaaz     |
| ccc; aaa, bbb, ccc, ddd; xyz | aaa      |
| a; ffeaa; aa, aaa, aaa, aaa  | ffeaa    |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_hierarchical2_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         |

Output Table:
| Input   | Output                      |
|:--------|:----------------------------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         | Meena Khosla                |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | Madhumati / Madhavi / Radha |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | Gauri                       |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | Leela                       |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | Gauri                       |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | Biraj Chakravorty           |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | Lalita                      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_googlerefine_step3_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| adam	williams         |
| charlie	denis         |
| james	johnson         |

Output Table:
| Input   | Output    |
|:--------|:----------|
| adam	williams         | awilliams |
| charlie	denis         | cdenis    |
| james	johnson         | jjohnson  |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_name.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         |

Output Table:
| Input   | Output         |
|:--------|:---------------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         | Mala Sinha     |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | Vyjayanthimala |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | Meena Kumari   |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | Meena Kumari   |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | Nutan          |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | Kamini Kaushal |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | Meena Kumari   |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_googlerefine_step1.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| Cencini	A	Andrew         |
| Charlie	Bob	Alice         |
| Kotas	Bollen	Jan         |

Output Table:
| Input   | Output             |
|:--------|:-------------------|
| Cencini	A	Andrew         | Cencini, Andrew A. |
| Charlie	Bob	Alice         | Charlie, Alice B.  |
| Kotas	Bollen	Jan         | Kotas, Jan B.      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_name3.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input          |
|:---------------|
| williams, adam |
| smith, bob     |
| johnson, james |

Output Table:
| Input          | Output    |
|:---------------|:----------|
| williams, adam | awilliams |
| smith, bob     | bsmith    |
| johnson, james | jjohnson  |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_name2.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input              |
|:-------------------|
| a b  c   d    e    |
| aaa  bbb  ccc  ddd |
| a b     e          |

Output Table:
| Input              | Output          |
|:-------------------|:----------------|
| a b  c   d    e    | a b c d e       |
| aaa  bbb  ccc  ddd | aaa bbb ccc ddd |
| a b     e          | a b e           |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_removespace_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                 |
|:----------------------|
| "--cool--",           |
| "test if this works"? |
| "...ok!",             |

Output Table:
| Input                 | Output             |
|:----------------------|:-------------------|
| "--cool--",           | cool               |
| "test if this works"? | test if this works |
| "...ok!",             | ok                 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_removepunctuation.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| azzz	ttt         |
| abcdefg	uvw         |
| xyz	ttttt         |

Output Table:
| Input   | Output   |
|:--------|:---------|
| azzz	ttt         | AZZZ     |
| abcdefg	uvw         | ABCDEFG  |
| xyz	ttttt         | XYZ      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_upper.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                                        |
|:---------------------------------------------|
| Principle Of Programming Lanaguages          |
| Table Extraction by Global Record Alignment  |
| Special Interest Group on Management Of Data |

Output Table:
| Input                                        | Output   |
|:---------------------------------------------|:---------|
| Principle Of Programming Lanaguages          | POPL     |
| Table Extraction by Global Record Alignment  | TEGRA    |
| Special Interest Group on Management Of Data | SIGMOD   |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_regexjoin_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input          |
|:---------------|
| 323-708-7700   |
| (425)-706-7709 |
| 111-222-3333   |
| 510.220.5586   |

Output Table:
| Input          | Output       |
|:---------------|:-------------|
| 323-708-7700   | 323-708-7700 |
| (425)-706-7709 | 425-706-7709 |
| 111-222-3333   | 111-222-3333 |
| 510.220.5586   | 510-220-5586 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_phonebranch.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| 30%     |
| 12%     |
| 45%     |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 30%     | 16.70 Degrees |
| 12%     | 6.84 Degrees  |
| 45%     | 24.23 Degrees |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_12.txt
instruction: //convert slope to degrees http://www.calcunation.com/calculators/general%20math/geometry/slope-percent-conversion.php?perc=24&calc=yes&Calculate=Convert+to+Degrees
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input    |
|:---------|
| CCXXXIII |
| MCCXXXIV |
| C        |

Output Table:
| Input    |   Output |
|:---------|---------:|
| CCXXXIII |      233 |
| MCCXXXIV |     1234 |
| C        |      100 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_11.txt
instruction: //convert roman numerals to normal numbers   http://www.onlineconversion.com/roman_numerals_advanced.htm
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input         |
|:--------------|
| 36.11, 80.22  |
| 10.22, -39.33 |
| 10.82, 20.21  |

Output Table:
| Input         | Output              |
|:--------------|:--------------------|
| 36.11, 80.22  | 429797mE, 3996430mN |
| 10.22, -39.33 | 463857mE, 1129755mN |
| 10.82, 20.21  | 413642mE, 1196188mN |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_14.txt
instruction: //convert lat long to utm  http://www.earthpoint.us/Convert.aspx
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                  |
|:-----------------------|
| all_i23saii_jjk_fhdkaj |
| axd_effe_xzz_ttesfes   |
| ipo_2839a_zif_fjei     |

Output Table:
| Input                  | Output   |
|:-----------------------|:---------|
| all_i23saii_jjk_fhdkaj | aljk     |
| axd_effe_xzz_ttesfes   | axzz     |
| ipo_2839a_zif_fjei     | ipif     |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_split_combine_partial_match_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input                               |
|:------------------------------------|
| {{x, aaay}, {aaaz, d}}              |
| {{a}, {b}, {c}}                     |
| {{a}, {ffeaa}, {aa, aaa, aaa, aaa}} |

Output Table:
| Input                               | Output   |
|:------------------------------------|:---------|
| {{x, aaay}, {aaaz, d}}              | aaay     |
| {{a}, {b}, {c}}                     | b        |
| {{a}, {ffeaa}, {aa, aaa, aaa, aaa}} | ffeaa    |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_reverse2.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input     |
|:----------|
| aeiou     |
| aeíouíóúz |
| aeíouxyz  |

Output Table:
| Input     | Output    |
|:----------|:----------|
| aeiou     | AEIOU     |
| aeíouíóúz | AEIOUIOUZ |
| aeíouxyz  | AEIOUXYZ  |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_upperaccent.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
|      Input |
|-----------:|
| 9822135882 |
| 1234567890 |
| 8332213215 |

Output Table:
|      Input | Output       |
|-----------:|:-------------|
| 9822135882 | 982-213-5882 |
| 1234567890 | 123-456-7890 |
| 8332213215 | 833-221-3215 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_ssn.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
| Input   |
|:--------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         |

Output Table:
| Input   | Output                      |
|:--------|:----------------------------|
| ** [[Mala Sinha]]  ''[[Dhool Ka Phool]]'' as Meena Khosla         | Meena Khosla                |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | Madhumati / Madhavi / Radha |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | Gauri                       |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | Leela                       |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | Gauri                       |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | Biraj Chakravorty           |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | Lalita                      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_wikilist3_ff_fail.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

You are given a input table where each row is an input record, and an output table that contains one additional column, called "Output", whose values in each row can be produced by transforming values of the same row in the input table. Your task is to write the corresponding code that can perform such transformations, based on the given input-output examples. Make sure that the transformations you write can generalize to additional input examples not shown in the input table.

Implementation Details:
1. Generate Python code using the Pandas library to perform the transformation.
2. The code should read the input table from "input.csv".
3. Apply the transformation based on the observed pattern.
4. Save the transformed output to "output.csv".
5. Ensure that the output structure matches the expected format, including column names and data types.

Your task is to ensure that the Python script is correctly formatted, efficient, and ready for execution on any dataset following the same transformation pattern.

No explanation and return the Python code in a markdown code block: ```python
<PYTHON-CODE>
```.

Input Table:
|     Input |
|----------:|
|  10101001 |
|    101010 |
| 100001101 |

Output Table:
|     Input |   Output |
|----------:|---------:|
|  10101001 |      169 |
|    101010 |       42 |
| 100001101 |      269 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_18.txt
instruction: //convert 1111two to base ten  http://www.unitconversion.org/numbers/base-2-to-base-10-conversion.html
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv