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

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output         |
|:---------------------|:---------------|
| 31-Jan-2031 05:54:18 | 01312031 5:54  |
| 27-Jan-2029 05:55:41 | 01272029 5:55  |
| 11-May-2028 15:31:52 | 05112028 15:31 |
| 17-Jan-1990 13:32:01 | 01171990 13:32 |
| 13-Jan-2038 10:59:36 | 01132038 10:59 |
| 16-Aug-1985 01:11:56 | 08161985 1:11  |
| 21-Oct-1997 08:42:58 | 10211997 8:42  |
| 14-Mar-2002 13:16:16 | 03142002 13:16 |
| 03-Mar-2024 10:17:49 | 03032024 10:17 |
| 15-Jul-1977 12:45:39 | 07151977 12:45 |



filename: DateTime.000110
instruction: DateTime
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: Transformation-text)

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      |
|:-----------|
| voluptatem |

Output Table:
| Input      | Output                  |
|:-----------|:------------------------|
| voluptatem | voluptatem@fabrikam.com |



filename: Email.000004
instruction: Concatenation
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: Transformation-text)

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   |
|:--------|
| 29:00   |
| 44:00   |
| 48:00   |
| 25:00   |
| 0:00    |

Output Table:
| Input   | Output      |
|:--------|:------------|
| 29:00   | 20:00-39:00 |
| 44:00   | 40:00-59:00 |
| 48:00   | 40:00-59:00 |
| 25:00   | 20:00-39:00 |
| 0:00    | 0:00-19:00  |



filename: DateTime.000077
instruction: DateTimeRange
DateTimeRounding
DateTime
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output                      |
|:---------------------|:----------------------------|
| 31-Jan-2031 05:54:18 | Friday, January 31, 2031    |
| 27-Jan-2029 05:55:41 | Saturday, January 27, 2029  |
| 11-May-2028 15:31:52 | Thursday, May 11, 2028      |
| 17-Jan-1990 13:32:01 | Wednesday, January 17, 1990 |
| 13-Jan-2038 10:59:36 | Wednesday, January 13, 2038 |
| 16-Aug-1985 01:11:56 | Friday, August 16, 1985     |
| 21-Oct-1997 08:42:58 | Tuesday, October 21, 1997   |
| 14-Mar-2002 13:16:16 | Thursday, March 14, 2002    |
| 03-Mar-2024 10:17:49 | Sunday, March 3, 2024       |
| 15-Jul-1977 12:45:39 | Friday, July 15, 1977       |



filename: DateTime.000109
instruction: DateTime
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output   |
|:---------------------|:---------|
| 31-Jan-2031 05:54:18 | 45-60    |
| 27-Jan-2029 05:55:41 | 45-60    |
| 11-May-2028 15:31:52 | 30-45    |
| 17-Jan-1990 13:32:01 | 30-45    |
| 13-Jan-2038 10:59:36 | 45-60    |
| 16-Aug-1985 01:11:56 | 0-15     |
| 21-Oct-1997 08:42:58 | 30-45    |
| 14-Mar-2002 13:16:16 | 15-30    |
| 03-Mar-2024 10:17:49 | 15-30    |
| 15-Jul-1977 12:45:39 | 45-60    |



filename: DateTime.000114
instruction: DateTimeRange
DateTimeRounding
DateTime
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: Transformation-text)

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                       |
|:----------------------------|
| Amet\Qui\omnis\A Quas - Sed |
| Repellat\Deleniti\aut\Et    |

Output Table:
| Input                       | Output       |
|:----------------------------|:-------------|
| Amet\Qui\omnis\A Quas - Sed | A Quas - Sed |
| Repellat\Deleniti\aut\Et    | Et           |



filename: FilePath.000005
instruction: Substring
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: Transformation-text)

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                                |
|:-------------------------------------|
| Company\Cumque\5QHflCs.zqt           |
| company\Dolorem\Id\Vero\Mn33fZkN.vfz |

Output Table:
| Input                                | Output                   |
|:-------------------------------------|:-------------------------|
| Company\Cumque\5QHflCs.zqt           | Company\Cumque\          |
| company\Dolorem\Id\Vero\Mn33fZkN.vfz | company\Dolorem\Id\Vero\ |



filename: FilePath.000003
instruction: Substring
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: Transformation-text)

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                                                                              |
|:-----------------------------------------------------------------------------------|
| amet quasi @ nodpublishers.com et molestiae modi non                               |
| eos @ woodgrovebank.com ipsam illum pariatur doloribus                             |
| dolorem id vero soluta fuga 33f@treyresearch.net facilis                           |
| assumenda iste quia natus et @ humongousinsurance.com ad nostrum totam harum totam |
| consectetur voluptatum ut voluptas tempore zyxv6@fabrikam.com nam sit quis         |

Output Table:
| Input                                                                              | Output               |
|:-----------------------------------------------------------------------------------|:---------------------|
| amet quasi @ nodpublishers.com et molestiae modi non                               |                      |
| eos @ woodgrovebank.com ipsam illum pariatur doloribus                             |                      |
| dolorem id vero soluta fuga 33f@treyresearch.net facilis                           | 33f@treyresearch.net |
| assumenda iste quia natus et @ humongousinsurance.com ad nostrum totam harum totam |                      |
| consectetur voluptatum ut voluptas tempore zyxv6@fabrikam.com nam sit quis         | zyxv6@fabrikam.com   |



filename: Email.000009
instruction: Substring
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: Transformation-text)

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   |
|:--------|
| F       |
| Male    |

Output Table:
| Input   | Output   |
|:--------|:---------|
| F       | 1        |
| Male    |          |



filename: Gender.000002
instruction: Conditional
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: Transformation-text)

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   |
|:--------|
| M       |
| F       |

Output Table:
| Input   |   Output |
|:--------|---------:|
| M       |        0 |
| F       |        1 |



filename: Gender.000001
instruction: Conditional
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: Transformation-text)

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                      |
|:---------------------------|
| bernardosagar@fabrikam.com |
| giftiantonio@fabrikam.com  |
| celtia@contoso.com         |
| joyikutty@fabrikam.com     |
| benito                     |

Output Table:
| Input                      | Output                     |
|:---------------------------|:---------------------------|
| bernardosagar@fabrikam.com | bernardosagar@fabrikam.com |
| giftiantonio@fabrikam.com  | giftiantonio@fabrikam.com  |
| celtia@contoso.com         | celtia@contoso.com         |
| joyikutty@fabrikam.com     | joyikutty@fabrikam.com     |
| benito                     | benito@proseware.com       |



filename: Email.000006
instruction: Concatenation
Conditional
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output   |
|:---------------------|:---------|
| 31-Jan-2031 05:54:18 | Q1 2031  |
| 27-Jan-2029 05:55:41 | Q1 2029  |
| 11-May-2028 15:31:52 | Q2 2028  |
| 17-Jan-1990 13:32:01 | Q1 1990  |
| 13-Jan-2038 10:59:36 | Q1 2038  |
| 16-Aug-1985 01:11:56 | Q3 1985  |
| 21-Oct-1997 08:42:58 | Q4 1997  |
| 14-Mar-2002 13:16:16 | Q1 2002  |
| 03-Mar-2024 10:17:49 | Q1 2024  |
| 15-Jul-1977 12:45:39 | Q3 1977  |



filename: DateTime.000112
instruction: DateTime
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: Transformation-text)

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   |
|:--------|
| nenad   |
| yuri    |

Output Table:
| Input   | Output             |
|:--------|:-------------------|
| nenad   | nenad@fabrikam.com |
| yuri    | yuri@fabrikam.com  |



filename: Email.000007
instruction: Concatenation
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output   |
|:---------------------|:---------|
| 31-Jan-2031 05:54:18 | 0-20     |
| 27-Jan-2029 05:55:41 | 40-60    |
| 11-May-2028 15:31:52 | 40-60    |
| 17-Jan-1990 13:32:01 | 0-20     |
| 13-Jan-2038 10:59:36 | 20-40    |
| 16-Aug-1985 01:11:56 | 40-60    |
| 21-Oct-1997 08:42:58 | 40-60    |
| 14-Mar-2002 13:16:16 | 0-20     |
| 03-Mar-2024 10:17:49 | 40-60    |
| 15-Jul-1977 12:45:39 | 20-40    |



filename: DateTime.000115
instruction: DateTimeRange
DateTimeRounding
DateTime
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: Transformation-text)

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                    |
|:-------------------------|
| company\Totam\index.html |
| company\Quas\index.html  |

Output Table:
| Input                    | Output         |
|:-------------------------|:---------------|
| company\Totam\index.html | company\Totam\ |
| company\Quas\index.html  | company\Quas\  |



filename: FilePath.000002
instruction: Conditional
Substring
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: Transformation-text)

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                                   |
|:----------------------------------------|
| Niklas.Bengtsson@wideworldimporters.com |
| Kiriakos.Sarlis@margiestravel.com       |
| Archana.Panigrahi@woodgrovebank.com     |
| Hone.Albert@southridgevideo.com         |
| Fatou.Ndiaye@adventure-works.com        |

Output Table:
| Input                                   | Output   |
|:----------------------------------------|:---------|
| Niklas.Bengtsson@wideworldimporters.com | Niklas   |
| Kiriakos.Sarlis@margiestravel.com       | Kiriakos |
| Archana.Panigrahi@woodgrovebank.com     | Archana  |
| Hone.Albert@southridgevideo.com         | Hone     |
| Fatou.Ndiaye@adventure-works.com        | Fatou    |



filename: Email.000011
instruction: Substring
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output      |
|:---------------------|:------------|
| 31-Jan-2031 05:54:18 | 5:30-6:00   |
| 27-Jan-2029 05:55:41 | 5:30-6:00   |
| 11-May-2028 15:31:52 | 15:30-16:00 |
| 17-Jan-1990 13:32:01 | 13:30-14:00 |
| 13-Jan-2038 10:59:36 | 10:30-11:00 |
| 16-Aug-1985 01:11:56 | 1:00-1:30   |
| 21-Oct-1997 08:42:58 | 8:30-9:00   |
| 14-Mar-2002 13:16:16 | 13:00-13:30 |
| 03-Mar-2024 10:17:49 | 10:00-10:30 |
| 15-Jul-1977 12:45:39 | 12:30-13:00 |



filename: DateTime.000113
instruction: DateTimeRange
DateTimeRounding
DateTime
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: Transformation-text)

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    |
|:---------|
| 15:25:29 |
| 21:44:40 |
| 23:48:20 |
| 18:25:57 |
| 4:29:27  |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 15:25:29 | 2PM      |
| 21:44:40 | 8PM      |
| 23:48:20 | 10PM     |
| 18:25:57 | 6PM      |
| 4:29:27  | 4AM      |



filename: DateTime.000089
instruction: DateTimeRounding
DateTime
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: Transformation-text)

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                                   |
|:----------------------------------------|
| Niklas.Bengtsson@wideworldimporters.com |
| Kiriakos.Sarlis@margiestravel.com       |
| Archana.Panigrahi@woodgrovebank.com     |
| Hone.Albert@southridgevideo.com         |
| Fatou.Ndiaye@adventure-works.com        |

Output Table:
| Input                                   | Output                 |
|:----------------------------------------|:-----------------------|
| Niklas.Bengtsson@wideworldimporters.com | wideworldimporters.com |
| Kiriakos.Sarlis@margiestravel.com       | margiestravel.com      |
| Archana.Panigrahi@woodgrovebank.com     | woodgrovebank.com      |
| Hone.Albert@southridgevideo.com         | southridgevideo.com    |
| Fatou.Ndiaye@adventure-works.com        | adventure-works.com    |



filename: Email.000010
instruction: Substring
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: Transformation-text)

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                |
|:---------------------|
| 31-Jan-2031 05:54:18 |
| 27-Jan-2029 05:55:41 |
| 11-May-2028 15:31:52 |
| 17-Jan-1990 13:32:01 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Mar-2002 13:16:16 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                | Output             |
|:---------------------|:-------------------|
| 31-Jan-2031 05:54:18 | 31/1/2031 5:54 AM  |
| 27-Jan-2029 05:55:41 | 27/1/2029 5:55 AM  |
| 11-May-2028 15:31:52 | 11/5/2028 3:31 PM  |
| 17-Jan-1990 13:32:01 | 17/1/1990 1:32 PM  |
| 13-Jan-2038 10:59:36 | 13/1/2038 10:59 AM |
| 16-Aug-1985 01:11:56 | 16/8/1985 1:11 AM  |
| 21-Oct-1997 08:42:58 | 21/10/1997 8:42 AM |
| 14-Mar-2002 13:16:16 | 14/3/2002 1:16 PM  |
| 03-Mar-2024 10:17:49 | 3/3/2024 10:17 AM  |
| 15-Jul-1977 12:45:39 | 15/7/1977 12:45 PM |



filename: DateTime.000111
instruction: DateTime
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: Transformation-text)

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                    |
|:-------------------------|
| company\Totam\index.html |
| company\Quas\index.html  |

Output Table:
| Input                    | Output         |
|:-------------------------|:---------------|
| company\Totam\index.html | company\Totam\ |
| company\Quas\index.html  | company\Quas\  |



filename: FilePath.000004
instruction: Substring
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: Transformation-text)

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   |
|:--------|
| Male    |
| Female  |

Output Table:
| Input   |   Output |
|:--------|---------:|
| Male    |        0 |
| Female  |        1 |



filename: Gender.000003
instruction: Conditional
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: Transformation-text)

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                                            |
|:-------------------------------------------------|
| Habiba.HaidariChaiprasit@alpineskihouse.com      |
| Kiriakos.Sarlis@margiestravel.com                |
| Ojerinde.AdeniyiLabrecque@fabrikamresidences.com |
| Hone.Albert@southridgevideo.com                  |
| Rodel.Torres@lucernepublishing.com               |

Output Table:
| Input                                            | Output            |
|:-------------------------------------------------|:------------------|
| Habiba.HaidariChaiprasit@alpineskihouse.com      | HaidariChaiprasit |
| Kiriakos.Sarlis@margiestravel.com                | Sarlis            |
| Ojerinde.AdeniyiLabrecque@fabrikamresidences.com | AdeniyiLabrecque  |
| Hone.Albert@southridgevideo.com                  | Albert            |
| Rodel.Torres@lucernepublishing.com               | Torres            |



filename: Email.000012
instruction: Substring
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: Transformation-text)

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                                                                             |
|:----------------------------------------------------------------------------------|
| RT202 PKWY & KNAPP RD; MONTGOMERY; 2015-12-10 @ 17:33:50;                         |
| BLUEROUTE & RAMP I476 NB TO CHEMICAL RD; PLYMOUTH; 2015-12-10 @ 17:35:41;         |
| REINDEER CT & DEAD END; NEW HANOVER; Station 332; 2015-12-10 @ 17:10:52;          |
| MAIN ST & OLD SUMNEYTOWN PIKE; LOWER SALFORD; Station 344; 2015-12-10 @ 16:51:42; |
| CANNON AVE & W 9TH ST; LANSDALE; Station 345; 2015-12-10 @ 15:39:04;              |
| AIRY ST & SWEDE ST; NORRISTOWN; Station 308A; 2015-12-10 @ 16:47:36;              |

Output Table:
| Input                                                                             | Output        |
|:----------------------------------------------------------------------------------|:--------------|
| RT202 PKWY & KNAPP RD; MONTGOMERY; 2015-12-10 @ 17:33:50;                         | Montgomery    |
| BLUEROUTE & RAMP I476 NB TO CHEMICAL RD; PLYMOUTH; 2015-12-10 @ 17:35:41;         | Plymouth      |
| REINDEER CT & DEAD END; NEW HANOVER; Station 332; 2015-12-10 @ 17:10:52;          | New Hanover   |
| MAIN ST & OLD SUMNEYTOWN PIKE; LOWER SALFORD; Station 344; 2015-12-10 @ 16:51:42; | Lower Salford |
| CANNON AVE & W 9TH ST; LANSDALE; Station 345; 2015-12-10 @ 15:39:04;              | Lansdale      |
| AIRY ST & SWEDE ST; NORRISTOWN; Station 308A; 2015-12-10 @ 16:47:36;              | Norristown    |



filename: EmergencyCall.000003
instruction: Casing
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv