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 | 5AM-7AM  |
| 27-Jan-2029 05:55:41 | 5AM-7AM  |
| 11-May-2028 15:31:52 | 3PM-5PM  |
| 17-Jan-1990 13:32:01 | 1PM-3PM  |
| 13-Jan-2038 10:59:36 | 9AM-11AM |
| 16-Aug-1985 01:11:56 | 1AM-3AM  |
| 21-Oct-1997 08:42:58 | 7AM-9AM  |
| 14-Mar-2002 13:16:16 | 1PM-3PM  |
| 03-Mar-2024 10:17:49 | 9AM-11AM |
| 15-Jul-1977 12:45:39 | 11AM-1PM |



filename: DateTime.000116
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   |
|:--------|
| ggg     |
| malat   |

Output Table:
| Input   | Output              |
|:--------|:--------------------|
| ggg     | ggg@proseware.com   |
| malat   | malat@proseware.com |



filename: Email.000005
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                   |
|:------------------------|
| {y} Haniya Mehraban {x} |

Output Table:
| Input                   | Output             |
|:------------------------|:-------------------|
| {y} Haniya Mehraban {x} | {y} H Mehraban {x} |



filename: Name.000011
instruction: Concatenation
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   |
|:--------|
| Man     |

Output Table:
| Input   | Output   |
|:--------|:---------|
| Man     | Mr       |



filename: Honorific.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                                                                                                                                                     |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2008-06-17 02:25:20 186.183.32.231 GET /foobar/ - 14 - 138.83.171.119 VVUTYHN/6.3+(HVFEBQ+SQ+9.2;+YBX60;+DC:69.4)+DROFQ/03802799+KNHNNBH/48.6 174 9 1 668 |

Output Table:
| Input                                                                                                                                                     | Output   |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------|:---------|
| 2008-06-17 02:25:20 186.183.32.231 GET /foobar/ - 14 - 138.83.171.119 VVUTYHN/6.3+(HVFEBQ+SQ+9.2;+YBX60;+DC:69.4)+DROFQ/03802799+KNHNNBH/48.6 174 9 1 668 | 02:25:20 |



filename: Log.000022
instruction: DateTime
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                                                                            |
|:---------------------------------------------------------------------------------|
| DREYCOTT LN & W LANCASTER AVE; LOWER MERION; Station 313; 2015-12-11 @ 01:29:52; |
| W MONTGOMERY AVE & PENNSWOOD RD; LOWER MERION; 2015-12-10 @ 19:25:22;            |
| MONTGOMERY AVE & WOODSIDE RD; LOWER MERION; Station 313; 2015-12-11 @ 04:11:35;  |
| AVIGNON DR & DEAD END; LOWER MERION; 2015-12-10 @ 20:01:29-Station:STA24;        |

Output Table:
| Input                                                                            | Output          |
|:---------------------------------------------------------------------------------|:----------------|
| DREYCOTT LN & W LANCASTER AVE; LOWER MERION; Station 313; 2015-12-11 @ 01:29:52; | 12 Nov 2015 1AM |
| W MONTGOMERY AVE & PENNSWOOD RD; LOWER MERION; 2015-12-10 @ 19:25:22;            | 12 Oct 2015 7PM |
| MONTGOMERY AVE & WOODSIDE RD; LOWER MERION; Station 313; 2015-12-11 @ 04:11:35;  | 12 Nov 2015 4AM |
| AVIGNON DR & DEAD END; LOWER MERION; 2015-12-10 @ 20:01:29-Station:STA24;        | 12 Oct 2015 8PM |



filename: EmergencyCall.000004
instruction: DateTime
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                  |
|:-----------------------|
| <n>Haniya Mehraban</n> |

Output Table:
| Input                  | Output            |
|:-----------------------|:------------------|
| <n>Haniya Mehraban</n> | <n>H Mehraban</n> |



filename: Name.000012
instruction: Concatenation
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | oa       |
| Aysu Polat         | ap       |
| Milica Zujovic     | mz       |
| Hone Albert        | ha       |
| Snezhana Bozhilova | sb       |



filename: Name.000026
instruction: Casing
Concatenation
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output     |
|:-------------------|:-----------|
| Ojerinde Adeniyi   | Ojerinde A |
| Aysu Polat         | Aysu P     |
| Milica Zujovic     | Milica Z   |
| Hone Albert        | Hone A     |
| Snezhana Bozhilova | Snezhana B |



filename: Name.000018
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Oj       |
| Aysu Polat         | Ay       |
| Milica Zujovic     | Mi       |
| Hone Albert        | Ho       |
| Snezhana Bozhilova | Sn       |



filename: Name.000015
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | o a      |
| Aysu Polat         | a p      |
| Milica Zujovic     | m z      |
| Hone Albert        | h a      |
| Snezhana Bozhilova | s b      |



filename: Name.000025
instruction: Casing
Concatenation
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output      |
|:-------------------|:------------|
| Ojerinde Adeniyi   | Ojerinde Ad |
| Aysu Polat         | Aysu Po     |
| Milica Zujovic     | Milica Zu   |
| Hone Albert        | Hone Al     |
| Snezhana Bozhilova | Snezhana Bo |



filename: Name.000019
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Ojer     |
| Aysu Polat         | Aysu     |
| Milica Zujovic     | Mili     |
| Hone Albert        | Hone     |
| Snezhana Bozhilova | Snez     |



filename: Name.000017
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Ad       |
| Aysu Polat         | Po       |
| Milica Zujovic     | Zu       |
| Hone Albert        | Al       |
| Snezhana Bozhilova | Bo       |



filename: Name.000021
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Ade      |
| Aysu Polat         | Pol      |
| Milica Zujovic     | Zuj      |
| Hone Albert        | Alb      |
| Snezhana Bozhilova | Boz      |



filename: Name.000022
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Aden     |
| Aysu Polat         | Pola     |
| Milica Zujovic     | Zujo     |
| Hone Albert        | Albe     |
| Snezhana Bozhilova | Bozh     |



filename: Name.000023
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | A        |
| Aysu Polat         | P        |
| Milica Zujovic     | Z        |
| Hone Albert        | A        |
| Snezhana Bozhilova | B        |



filename: Name.000020
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Oje      |
| Aysu Polat         | Ays      |
| Milica Zujovic     | Mil      |
| Hone Albert        | Hon      |
| Snezhana Bozhilova | Sne      |



filename: Name.000016
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               |
|:--------------------|
| Hone Albert         |
| Antonia Guachiac    |
| Svetlana Konovalova |
| Aysu Polat          |
| Ojerinde Adeniyi    |
| Milica Zujovic      |
| Pere Castellsegui   |
| Artem Kuznetsov     |
| Klemmet Reiersen    |
| Haf Evans           |

Output Table:
| Input               | Output   |
|:--------------------|:---------|
| Hone Albert         | H        |
| Antonia Guachiac    | A        |
| Svetlana Konovalova | S        |
| Aysu Polat          | A        |
| Ojerinde Adeniyi    | O        |
| Milica Zujovic      | M        |
| Pere Castellsegui   | P        |
| Artem Kuznetsov     | A        |
| Klemmet Reiersen    | K        |
| Haf Evans           | H        |



filename: Name.000013
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          |
|:---------------|
| Aysu Polat     |
| Cecep Sutresna |
| Fatma Yilmaz   |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Aysu Polat     | Polat    |
| Cecep Sutresna | Sutresna |
| Fatma Yilmaz   | Yilmaz   |



filename: Name.000034
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      |
|:-----------|
| 2, Fatma   |
| 4, Anna    |
| 4, Dilorom |

Output Table:
| Input      | Output   |
|:-----------|:---------|
| 2, Fatma   | Fatma    |
| 4, Anna    | Anna     |
| 4, Dilorom | Dilorom  |



filename: Name.000027
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | Ojerinde |
| Aysu Polat         | Aysu     |
| Milica Zujovic     | Milica   |
| Hone Albert        | Hone     |
| Snezhana Bozhilova | Snezhana |



filename: Name.000014
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                      |
|:---------------------------|
| Mr. Milica Z. Bozhilova    |
| Ihar Reiersen              |
| Daniyar Fatou Castellsegui |

Output Table:
| Input                      | Output   |
|:---------------------------|:---------|
| Mr. Milica Z. Bozhilova    | Mr.      |
| Ihar Reiersen              | Ihar     |
| Daniyar Fatou Castellsegui | Daniyar  |



filename: Name.000040
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               |
|:--------------------|
| Asemelash, Svitlana |
| Hennum, Alena       |

Output Table:
| Input               | Output   |
|:--------------------|:---------|
| Asemelash, Svitlana | Svitlana |
| Hennum, Alena       | Alena    |



filename: Name.000031
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          |
|:---------------|
| Aysu Polat     |
| Cecep Sutresna |
| Fatma Yilmaz   |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Aysu Polat     | Aysu     |
| Cecep Sutresna | Cecep    |
| Fatma Yilmaz   | Fatma    |



filename: Name.000035
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                |
|:---------------------|
| Meena Orav           |
| Artem Milanesi       |
| Jan Evans            |
| Hone-Kiriakos Yilmaz |
| Barbara Susila       |

Output Table:
| Input                | Output   |
|:---------------------|:---------|
| Meena Orav           | Meena    |
| Artem Milanesi       | Artem    |
| Jan Evans            | Jan      |
| Hone-Kiriakos Yilmaz | Hone     |
| Barbara Susila       | Barbara  |



filename: Name.000028
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         |
|:--------------|
| Albert, Anne  |
| Sarlis, Fatma |

Output Table:
| Input         | Output   |
|:--------------|:---------|
| Albert, Anne  | Albert   |
| Sarlis, Fatma | Sarlis   |



filename: Name.000030
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv