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   |
|:--------|
| ** [[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_wikilist1.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    |
|:---------|
| 1:30:25  |
| 22:22:22 |
| 12:10:02 |

Output Table:
| Input    | Output        |
|:---------|:--------------|
| 1:30:25  | 1.5069 hours  |
| 22:22:22 | 22.3728 hours |
| 12:10:02 | 12.1672 hours |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_1.txt
instruction: //convert time to decimal  http://calendar.google.com/
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 |
|--------:|
|    0100 |
|    1800 |
|    0200 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|    0100 | 1:00 AM  |
|    1800 | 6:00 PM  |
|    0200 | 2:00 AM  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_16.txt
instruction: //convert military time to standard time
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         | Dhool Ka Phool |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | Madhumati      |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | Baiju Bawra    |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | Sahara         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | Seema          |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | Biraj Bahu     |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | Parineeta      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_googlerefine_step2_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, y, z             |
| 1, 2, 3, 4, 5, 6, 7 |
| 1, 2, 3             |

Output Table:
| Input               | Output   |
|:--------------------|:---------|
| x, y, z             | z        |
| 1, 2, 3, 4, 5, 6, 7 | 7        |
| 1, 2, 3             | 3        |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_reverse.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    |
|:---------|
| 6:00 PM  |
| 11:00 AM |
| 11:00 PM |

Output Table:
| Input    |   Output |
|:---------|---------:|
| 6:00 PM  |     1800 |
| 11:00 AM |     1100 |
| 11:00 PM |     2300 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_13.txt
instruction: //convert regular time to military time  http://www.calculatehours.com/Military_Time_Converter.html
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               |
|:--------------------|
| 0.16,0.67,0.61,0.73 |
| 0.53,0.42,0.38,0.55 |
| 0,1,1,0             |

Output Table:
| Input               | Output   |
|:--------------------|:---------|
| 0.16,0.67,0.61,0.73 | 58,23,27 |
| 0.53,0.42,0.38,0.55 | 54,67,71 |
| 0,1,1,0             | 255,0,0  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_17.txt
instruction: //convert cmyk to rgb   http://www.rapidtables.com/convert/color/cmyk-to-rgb.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   |
|:--------|
| jane	johnson         |
| charlie	denis         |
| bob	white         |

Output Table:
| Input   | Output   |
|:--------|:---------|
| jane	johnson         | JJohnson |
| charlie	denis         | CDenis   |
| bob	white         | BWhite   |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_stringcase1.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                               |
|:------------------------------------|
| University Ave Middleton, WI 53562  |
| 14820 36th ST NE, Redmond, WA 98052 |
| Farwell St Mc Farland, WI 53558     |

Output Table:
| Input                               |   Output |
|:------------------------------------|---------:|
| University Ave Middleton, WI 53562  |    53562 |
| 14820 36th ST NE, Redmond, WA 98052 |    98052 |
| Farwell St Mc Farland, WI 53558     |    53558 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_trifacta_step2.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         | Dhool Ka Phool |
| ** [[Vyjayanthimala]]  ''[[Madhumati]]'' as Madhumati / Madhavi / Radha         | Madhumati      |
| * '''1953 [[Meena Kumari]]  ''[[Baiju Bawra (film)|Baiju Bawra]]''''' as '''Gauri'''         | Baiju Bawra    |
| ** [[Meena Kumari]]  ''[[Sahara (1958 film)|Sahara]]'' as Leela         | Sahara         |
| * '''1956 [[Nutan]]  ''[[Seema (1955 film)|Seema]]''''' as '''Gauri'''         | Seema          |
| * '''1955 [[Kamini Kaushal]]  ''[[Biraj Bahu]]''''' as '''Biraj Chakravorty'''         | Biraj Bahu     |
| * '''1954 [[Meena Kumari]]  ''[[Parineeta (1953 film)|Parineeta]]''''' as '''Lalita'''         | Parineeta      |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_wikilist2_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    |
|:---------|
| 0,0,0    |
| 0,255,0  |
| 33,44,55 |

Output Table:
| Input    | Output          |
|:---------|:----------------|
| 0,0,0    | 0,0,0,1         |
| 0,255,0  | 1,0,1,0         |
| 33,44,55 | 0.4,0.2,0,0.784 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_15.txt
instruction: //convert rgb to cmyk  http://www.rapidtables.com/convert/color/rgb-to-cmyk.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 |
|---------:|
| 11111111 |
|      111 |
|  0000101 |

Output Table:
|    Input | Output   |
|---------:|:---------|
| 11111111 | FF       |
|      111 | 7        |
|  0000101 | 5        |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_10.txt
instruction: //convert binary to hexadecimal
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                                                                                                                     |
|:--------------------------------------------------------------------------------------------------------------------------|
| 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 |     2829 |
| Mar 23 Downtown Seattle Skyline. Act fast! $2829 / 3br - 355ft2 - (Seattle, WA) pic map                                   |     2829 |
| Mar 18 New AVAILABLE NOW FURNISHED $1650 / 1br - 750ft2 - (Bellevue) pic                                                  |     1650 |



filename: Transform-Data-by-Example/Benchmark/benchmark-FF-Trifacta-GoogleRefine/example_file_craigslist2.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            |
|:-----------------|
| 11:12:13.7458750 |
| 23:31:21.4322211 |
| 23:01:09.0988712 |

Output Table:
| Input            | Output                   |
|:-----------------|:-------------------------|
| 11:12:13.7458750 | 11 hrs, 12 mins, 14 secs |
| 23:31:21.4322211 | 23 hrs, 31 mins, 21 secs |
| 23:01:09.0988712 | 23 hrs, 1 mins, 9 secs   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_19.txt
instruction: //convert timespan to string
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 |
|---------:|
| 23912000 |
|    12345 |
|    98100 |

Output Table:
|    Input |         Output |
|---------:|---------------:|
| 23912000 |     2.3912e+07 |
|    12345 | 12345          |
|    98100 | 98100          |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_2.txt
instruction: //convert to scientific notation
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                                      |
|:-------------------------------------------|
| <b>hello world!</b><br /><i>it is me</i>   |
| <b>this</b><b> is</b><b> a</b><b> test</b> |
| <b>hello, </b><b>hi.</b><b>welcome</b>     |

Output Table:
| Input                                      | Output               |
|:-------------------------------------------|:---------------------|
| <b>hello world!</b><br /><i>it is me</i>   | hello world!it is me |
| <b>this</b><b> is</b><b> a</b><b> test</b> | this is a test       |
| <b>hello, </b><b>hi.</b><b>welcome</b>     | hello, hi.welcome    |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_20.txt
instruction: //convert html to plain text
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   |
|:--------|
| #00FF00 |
| #223344 |
| #FF00FF |

Output Table:
| Input   | Output    |
|:--------|:----------|
| #00FF00 | 0,255,0   |
| #223344 | 34,51,68  |
| #FF00FF | 255,0,255 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_24.txt
instruction: //convert hex to rgb http://www.javascripter.net/faq/hextorgb.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   |
|:--------|
| RS      |
| "       |
| #       |

Output Table:
| Input   | Output   |
|:--------|:---------|
| RS      | 1E       |
| "       | 22       |
| #       | 23       |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_26.txt
instruction: //convert ascii to hex   http://www.rapidtables.com/convert/number/hex-to-ascii.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 |
|--------:|
|  111101 |
|     111 |
| 1011010 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|  111101 |       61 |
|     111 |        7 |
| 1011010 |       90 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_40.txt
instruction: //convert 11010two to base ten
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 |
|--------:|
|   11.35 |
|   21    |
|  129.45 |

Output Table:
|   Input | Output      |
|--------:|:------------|
|   11.35 | 11 20' 60"  |
|   21    | 21 26' 24"  |
|  129.45 | 129 26' 60" |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_29.txt
instruction: //convert decimal degrees to degrees minutes seconds  https://www.fcc.gov/encyclopedia/degrees-minutes-seconds-tofrom-decimal-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                   |
|:------------------------|
| 11:00 PM,Sun,Aug 2,2015 |
| 2:30 AM,Mon,Feb 2,2015  |
| 3:30 AM,Mon,Feb 2,2015  |

Output Table:
| Input                   | Output                 |
|:------------------------|:-----------------------|
| 11:00 PM,Sun,Aug 2,2015 | 2:00 AM,Mon,Aug 3,2015 |
| 2:30 AM,Mon,Feb 2,2015  | 5:30 AM,Mon,Feb 2,2015 |
| 3:30 AM,Mon,Feb 2,2015  | 6:30 AM,Mon,Feb 2,2015 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_22.txt
instruction: //convert pacific time to eastern time
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 |
|--------:|
|      14 |
|       9 |
|       8 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|      14 |     12.5 |
|       9 |      7.5 |
|       8 |      6.5 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_31.txt
instruction: //convert women_s shoe size to mens  http://www.shoesize.com/women-to-men-converter/
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 |
|--------:|
|      55 |
|      22 |
|      74 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|      55 | U        |
|      22 | "        |
|      74 | t        |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_4.txt
instruction: //convert hex to ascii   http://www.rapidtables.com/convert/number/hex-to-ascii.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      |
|:-----------|
| 244,237,71 |
| 198,160,12 |
| 249,232,20 |

Output Table:
| Input      |   Output |
|:-----------|---------:|
| 244,237,71 |      101 |
| 198,160,12 |      117 |
| 249,232,20 |      102 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_34.txt
instruction: //convert rgb to pantone  http://euro-bags.eu/pantone
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   |
|:--------|
| 2 1/2   |
| 51 3/4  |
| 1/8     |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 2 1/2   |    2.5   |
| 51 3/4  |   51.75  |
| 1/8     |    1.125 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_39.txt
instruction: //convert mixed number to decimal
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv