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           |
|:----------------|
| 7/8/2237 15:25  |
| 3/30/2002 21:44 |
| 8/2/2160 23:48  |
| 10/6/2143 18:25 |
| 8/15/1740 4:29  |

Output Table:
| Input           | Output       |
|:----------------|:-------------|
| 7/8/2237 15:25  | Sat 3:00 PM  |
| 3/30/2002 21:44 | Sat 9:30 PM  |
| 8/2/2160 23:48  | Sat 11:30 PM |
| 10/6/2143 18:25 | Sun 6:00 PM  |
| 8/15/1740 4:29  | Mon 4:00 AM  |



filename: DateTime.000083
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    |
|:---------|
| 15:25:29 |
| 21:44:40 |
| 23:48:20 |
| 18:25:57 |
| 4:29:27  |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 15:25:29 | 2:00PM   |
| 21:44:40 | 8:00PM   |
| 23:48:20 | 10:00PM  |
| 18:25:57 | 6:00PM   |
| 4:29:27  | 4:00AM   |



filename: DateTime.000088
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        |
|:-------------|
| 1973-02-17   |
| 9 April 1987 |
| 09.12.97     |

Output Table:
| Input        | Output     |
|:-------------|:-----------|
| 1973-02-17   | 1973-02-17 |
| 9 April 1987 | 1987-04-09 |
| 09.12.97     | 1997-12-09 |



filename: DateTime.000093
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    |
|:---------|
| 15:25:29 |
| 21:44:40 |
| 23:48:20 |
| 18:25:57 |
| 4:29:27  |

Output Table:
| Input    |   Output |
|:---------|---------:|
| 15:25:29 |       14 |
| 21:44:40 |       20 |
| 23:48:20 |       22 |
| 18:25:57 |       18 |
| 4:29:27  |        4 |



filename: DateTime.000087
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   |
|:--------|
| 40:00   |
| 20:00   |

Output Table:
| Input   | Output      |
|:--------|:------------|
| 40:00   | 40:00-59:00 |
| 20:00   | 20:00-39:00 |



filename: DateTime.000078
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   |
|:--------|
| 14:39   |
| 16.26   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 14:39   | 14:39    |
| 16.26   | 16:26    |



filename: DateTime.000095
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    |
|:---------|
| 18:10:40 |
| 17:31:00 |
| 17:58:22 |
| 17:41:54 |
| 16:51:42 |
| 17:35:41 |
| 18:07:01 |
| 18:02:02 |
| 17:15:49 |
| 17:10:52 |
| 16:56:52 |
| 17:42:44 |
| 15:39:04 |
| 17:12:47 |
| 16:32:10 |
| 18:00:38 |

Output Table:
| Input    |   Output |
|:---------|---------:|
| 18:10:40 |       18 |
| 17:31:00 |       17 |
| 17:58:22 |       17 |
| 17:41:54 |       17 |
| 16:51:42 |       16 |
| 17:35:41 |       17 |
| 18:07:01 |       18 |
| 18:02:02 |       18 |
| 17:15:49 |       17 |
| 17:10:52 |       17 |
| 16:56:52 |       16 |
| 17:42:44 |       17 |
| 15:39:04 |       15 |
| 17:12:47 |       17 |
| 16:32:10 |       16 |
| 18:00:38 |       18 |



filename: DateTime.000092
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        |
|:-------------|
| 9 April 1987 |
| 19.12.97     |
| 1973-12-17   |
| 18/05/1967   |

Output Table:
| Input        | Output     |
|:-------------|:-----------|
| 9 April 1987 | 1987-04-09 |
| 19.12.97     | 1997-12-19 |
| 1973-12-17   | 1973-12-17 |
| 18/05/1967   | 1967-05-18 |



filename: DateTime.000094
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     |
|:----------|
| 30/3/2002 |
| 30/3/2241 |
| 31/1/1846 |

Output Table:
| Input     | Output          |
|:----------|:----------------|
| 30/3/2002 | March 30 2002   |
| 30/3/2241 | March 30 2241   |
| 31/1/1846 | January 31 1846 |



filename: DateTime.000100
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    |
|:---------|
| 18:10:40 |
| 17:31:00 |
| 17:58:22 |
| 17:41:54 |
| 16:51:42 |
| 17:35:41 |
| 18:07:01 |
| 18:02:02 |
| 17:15:49 |
| 17:10:52 |
| 16:56:52 |
| 17:42:44 |
| 15:39:04 |
| 17:12:47 |
| 16:32:10 |
| 18:00:38 |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 18:10:40 | 6PM      |
| 17:31:00 | 5PM      |
| 17:58:22 | 5PM      |
| 17:41:54 | 5PM      |
| 16:51:42 | 4PM      |
| 17:35:41 | 5PM      |
| 18:07:01 | 6PM      |
| 18:02:02 | 6PM      |
| 17:15:49 | 5PM      |
| 17:10:52 | 5PM      |
| 16:56:52 | 4PM      |
| 17:42:44 | 5PM      |
| 15:39:04 | 3PM      |
| 17:12:47 | 5PM      |
| 16:32:10 | 4PM      |
| 18:00:38 | 6PM      |



filename: DateTime.000091
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       |
|:------------|
| 2001--11--6 |
| 2/10/2204   |
| 6.21.2059   |
| 1846--1--31 |
| 10.8.2089   |
| 8/7/2237    |
| 1816--3--26 |
| 2160--8--2  |
| 14/10/1754  |
| 23/9/1733   |

Output Table:
| Input       |   Output |
|:------------|---------:|
| 2001--11--6 |       11 |
| 2/10/2204   |       10 |
| 6.21.2059   |        6 |
| 1846--1--31 |        1 |
| 10.8.2089   |       10 |
| 8/7/2237    |        7 |
| 1816--3--26 |        3 |
| 2160--8--2  |        8 |
| 14/10/1754  |       10 |
| 23/9/1733   |        9 |



filename: DateTime.000097
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    |
|:---------|
| 17:31:00 |
| 17:45:23 |
| 18:05:19 |
| 18:02:38 |
| 16:51:42 |
| 17:58:22 |
| 17:53:22 |
| 17:15:49 |
| 00:02:01 |
| 17:35:41 |
| 17:10:52 |
| 16:56:52 |
| 17:42:44 |
| 17:41:54 |
| 15:39:04 |
| 17:12:47 |
| 16:32:10 |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 17:31:00 | 5pm      |
| 17:45:23 | 5pm      |
| 18:05:19 | 6pm      |
| 18:02:38 | 6pm      |
| 16:51:42 | 4pm      |
| 17:58:22 | 5pm      |
| 17:53:22 | 5pm      |
| 17:15:49 | 5pm      |
| 00:02:01 | 12am     |
| 17:35:41 | 5pm      |
| 17:10:52 | 5pm      |
| 16:56:52 | 4pm      |
| 17:42:44 | 5pm      |
| 17:41:54 | 5pm      |
| 15:39:04 | 3pm      |
| 17:12:47 | 5pm      |
| 16:32:10 | 4pm      |



filename: DateTime.000090
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 |       31 |
| 27-Jan-2029 05:55:41 |       27 |
| 11-May-2028 15:31:52 |       11 |
| 17-Jan-1990 13:32:01 |       17 |
| 13-Jan-2038 10:59:36 |       13 |
| 16-Aug-1985 01:11:56 |       16 |
| 21-Oct-1997 08:42:58 |       21 |
| 14-Mar-2002 13:16:16 |       14 |
| 03-Mar-2024 10:17:49 |        3 |
| 15-Jul-1977 12:45:39 |       15 |



filename: DateTime.000101
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       |
|:------------|
| 2001--11--6 |
| 2/10/2204   |
| 6.21.2059   |
| 1846--1--31 |
| 10.8.2089   |
| 8/7/2237    |
| 1816--3--26 |
| 2160--8--2  |
| 14/10/1754  |
| 23/9/1733   |

Output Table:
| Input       | Output    |
|:------------|:----------|
| 2001--11--6 | November  |
| 2/10/2204   | October   |
| 6.21.2059   | June      |
| 1846--1--31 | January   |
| 10.8.2089   | October   |
| 8/7/2237    | July      |
| 1816--3--26 | March     |
| 2160--8--2  | August    |
| 14/10/1754  | October   |
| 23/9/1733   | September |



filename: DateTime.000098
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       |
|:------------|
| 2001--11--6 |
| 2/10/2204   |
| 6.21.2059   |
| 1846--1--31 |
| 10.8.2089   |
| 8/7/2237    |
| 1816--3--26 |
| 2160--8--2  |
| 14/10/1754  |
| 23/9/1733   |

Output Table:
| Input       |   Output |
|:------------|---------:|
| 2001--11--6 |     2001 |
| 2/10/2204   |     2204 |
| 6.21.2059   |     2059 |
| 1846--1--31 |     1846 |
| 10.8.2089   |     2089 |
| 8/7/2237    |     2237 |
| 1816--3--26 |     1816 |
| 2160--8--2  |     2160 |
| 14/10/1754  |     1754 |
| 23/9/1733   |     1733 |



filename: DateTime.000099
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 |       54 |
| 27-Jan-2029 05:55:41 |       55 |
| 11-May-2028 15:31:52 |       31 |
| 17-Jan-1990 13:32:01 |       32 |
| 13-Jan-2038 10:59:36 |       59 |
| 16-Aug-1985 01:11:56 |       11 |
| 21-Oct-1997 08:42:58 |       42 |
| 14-Mar-2002 13:16:16 |       16 |
| 03-Mar-2024 10:17:49 |       17 |
| 15-Jul-1977 12:45:39 |       45 |



filename: DateTime.000103
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 |        5 |
| 27-Jan-2029 05:55:41 |        5 |
| 11-May-2028 15:31:52 |       15 |
| 17-Jan-1990 13:32:01 |       13 |
| 13-Jan-2038 10:59:36 |       10 |
| 16-Aug-1985 01:11:56 |        1 |
| 21-Oct-1997 08:42:58 |        8 |
| 14-Mar-2002 13:16:16 |       13 |
| 03-Mar-2024 10:17:49 |       10 |
| 15-Jul-1977 12:45:39 |       12 |



filename: DateTime.000102
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       |
|:------------|
| 2001--11--6 |
| 2/10/2204   |
| 6.21.2059   |
| 1846--1--31 |
| 10.8.2089   |
| 8/7/2237    |
| 1816--3--26 |
| 2160--8--2  |
| 14/10/1754  |
| 23/9/1733   |

Output Table:
| Input       |   Output |
|:------------|---------:|
| 2001--11--6 |        6 |
| 2/10/2204   |        2 |
| 6.21.2059   |       21 |
| 1846--1--31 |       31 |
| 10.8.2089   |        8 |
| 8/7/2237    |        8 |
| 1816--3--26 |       26 |
| 2160--8--2  |        2 |
| 14/10/1754  |       14 |
| 23/9/1733   |       23 |



filename: DateTime.000096
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 | Fri      |
| 27-Jan-2029 05:55:41 | Sat      |
| 11-May-2028 15:31:52 | Thu      |
| 17-Jan-1990 13:32:01 | Wed      |
| 13-Jan-2038 10:59:36 | Wed      |
| 16-Aug-1985 01:11:56 | Fri      |
| 21-Oct-1997 08:42:58 | Tue      |
| 14-Mar-2002 13:16:16 | Thu      |
| 03-Mar-2024 10:17:49 | Sun      |
| 15-Jul-1977 12:45:39 | Fri      |



filename: DateTime.000106
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 | Jan      |
| 27-Jan-2029 05:55:41 | Jan      |
| 11-May-2028 15:31:52 | May      |
| 17-Jan-1990 13:32:01 | Jan      |
| 13-Jan-2038 10:59:36 | Jan      |
| 16-Aug-1985 01:11:56 | Aug      |
| 21-Oct-1997 08:42:58 | Oct      |
| 14-Mar-2002 13:16:16 | Mar      |
| 03-Mar-2024 10:17:49 | Mar      |
| 15-Jul-1977 12:45:39 | Jul      |



filename: DateTime.000104
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 |     2031 |
| 27-Jan-2029 05:55:41 |     2029 |
| 11-May-2028 15:31:52 |     2028 |
| 17-Jan-1990 13:32:01 |     1990 |
| 13-Jan-2038 10:59:36 |     2038 |
| 16-Aug-1985 01:11:56 |     1985 |
| 21-Oct-1997 08:42:58 |     1997 |
| 14-Mar-2002 13:16:16 |     2002 |
| 03-Mar-2024 10:17:49 |     2024 |
| 15-Jul-1977 12:45:39 |     1977 |



filename: DateTime.000107
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 |
| 21-Jan-1985 05:44:43 |
| 13-Jan-2038 10:59:36 |
| 16-Aug-1985 01:11:56 |
| 21-Oct-1997 08:42:58 |
| 14-Feb-2034 05:36:07 |
| 03-Mar-2024 10:17:49 |
| 15-Jul-1977 12:45:39 |

Output Table:
| Input                |   Output |
|:---------------------|---------:|
| 31-Jan-2031 05:54:18 |       18 |
| 27-Jan-2029 05:55:41 |       41 |
| 11-May-2028 15:31:52 |       52 |
| 21-Jan-1985 05:44:43 |       43 |
| 13-Jan-2038 10:59:36 |       36 |
| 16-Aug-1985 01:11:56 |       56 |
| 21-Oct-1997 08:42:58 |       58 |
| 14-Feb-2034 05:36:07 |       07 |
| 03-Mar-2024 10:17:49 |       49 |
| 15-Jul-1977 12:45:39 |       39 |



filename: DateTime.000105
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   |
|:--------|
| 3:25PM  |
| 9:44PM  |
| 11:48PM |
| 6:25PM  |
| 4:29AM  |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 3:25PM  | 3PM-5PM  |
| 9:44PM  | 9PM-11PM |
| 11:48PM | 11PM-1AM |
| 6:25PM  | 5PM-7PM  |
| 4:29AM  | 3AM-5AM  |



filename: DateTime.000079
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 | 1/31/2031  |
| 27-Jan-2029 05:55:41 | 1/27/2029  |
| 11-May-2028 15:31:52 | 5/11/2028  |
| 17-Jan-1990 13:32:01 | 1/17/1990  |
| 13-Jan-2038 10:59:36 | 1/13/2038  |
| 16-Aug-1985 01:11:56 | 8/16/1985  |
| 21-Oct-1997 08:42:58 | 10/21/1997 |
| 14-Mar-2002 13:16:16 | 3/14/2002  |
| 03-Mar-2024 10:17:49 | 3/3/2024   |
| 15-Jul-1977 12:45:39 | 7/15/1977  |



filename: DateTime.000108
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                  |
|:-----------------------|
| /                      |
| /libero/iste/enim3.png |
| /libero/quas/enim5.png |
| /libero/enim9.png      |
| /libero/quas/enim3.png |
| /libero/quas/enim2.png |
| /libero/enim5.png      |

Output Table:
| Input                  | Output   |
|:-----------------------|:---------|
| /                      | root     |
| /libero/iste/enim3.png | enim3    |
| /libero/quas/enim5.png | enim5    |
| /libero/enim9.png      | enim9    |
| /libero/quas/enim3.png | enim3    |
| /libero/quas/enim2.png | enim2    |
| /libero/enim5.png      | enim5    |



filename: FilePath.000001
instruction: Conditional
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv