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        |
|:-------------|
| 02 Sep 1747  |
| 22 Oct 1815  |
| 21 June 2059 |
| 26-Mar-1816  |
| Jul 08 2237  |
| 3 Nov 1904   |
| 29 Jan 2218  |
| 21-Jan-1818  |
| May 25 1817  |
| 06-Oct-2143  |
| 03302241     |
| 26 May 2155  |
| 02-Aug-2160  |
| 31 May 1963  |
| 05302131     |

Output Table:
| Input        |   Output |
|:-------------|---------:|
| 02 Sep 1747  | 09021747 |
| 22 Oct 1815  | 10221815 |
| 21 June 2059 | 06212059 |
| 26-Mar-1816  | 03261816 |
| Jul 08 2237  | 07082237 |
| 3 Nov 1904   | 11031904 |
| 29 Jan 2218  | 01292218 |
| 21-Jan-1818  | 01211818 |
| May 25 1817  | 05251817 |
| 06-Oct-2143  | 10062143 |
| 03302241     | 03302241 |
| 26 May 2155  | 05262155 |
| 02-Aug-2160  | 08022160 |
| 31 May 1963  | 05311963 |
| 05302131     | 05302131 |



filename: DateTime.000023
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       |
|:------------|
| 9:44:40 PM  |
| 11:48:20 PM |
| 6:25:57 PM  |
| 4:29:27 AM  |

Output Table:
| Input       | Output   |
|:------------|:---------|
| 9:44:40 PM  | 21:44    |
| 11:48:20 PM | 23:48    |
| 6:25:57 PM  | 18:25    |
| 4:29:27 AM  | 4:29     |



filename: DateTime.000031
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            |
|:-----------------|
| 22:06            |
| 09:53            |
| 5:27             |
| 1712-10-01 17:18 |

Output Table:
| Input            | Output   |
|:-----------------|:---------|
| 22:06            | 10:06 PM |
| 09:53            | 9:53 AM  |
| 5:27             | 5:27 AM  |
| 1712-10-01 17:18 | 5:18 PM  |



filename: DateTime.000032
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        |
|:-------------|
| 02 Sep 1747  |
| 22 Oct 1815  |
| 21 June 2059 |
| 26-Mar-1816  |
| Jul 08 2237  |
| 3 Nov 1904   |
| 29 Jan 2218  |
| 21-Jan-1818  |
| May 25 1817  |
| 06-Oct-2143  |
| 03302241     |
| 26 May 2155  |
| 02-Aug-2160  |
| 31 May 1963  |
| 05302131     |

Output Table:
| Input        | Output   |
|:-------------|:---------|
| 02 Sep 1747  | Sep 47   |
| 22 Oct 1815  | Oct 15   |
| 21 June 2059 | Jun 59   |
| 26-Mar-1816  | Mar 16   |
| Jul 08 2237  | Jul 37   |
| 3 Nov 1904   | Nov 04   |
| 29 Jan 2218  | Jan 18   |
| 21-Jan-1818  | Jan 18   |
| May 25 1817  | May 17   |
| 06-Oct-2143  | Oct 43   |
| 03302241     | Mar 41   |
| 26 May 2155  | May 55   |
| 02-Aug-2160  | Aug 60   |
| 31 May 1963  | May 63   |
| 05302131     | May 31   |



filename: DateTime.000025
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   |
|:--------|
| A       |
| Baz     |
|         |

Output Table:
| Input   | Output   |
|:--------|:---------|
| A       | A        |
| Baz     | B        |
|         | N/A      |



filename: City.000009
instruction: Conditional
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

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

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



filename: DateTime.000037
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 the 30th 2002   |
| 30/3/2241 | March the 30th 2241   |
| 31/1/1846 | January the 31th 1846 |



filename: DateTime.000044
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        |
|:-------------|
| 02 Sep 1747  |
| 22 Oct 1815  |
| 21 June 2059 |
| 26-Mar-1816  |
| Jul 08 2237  |
| 3 Nov 1904   |
| 29 Jan 2218  |
| 21-Jan-1818  |
| May 25 1817  |
| 06-Oct-2143  |
| 03302241     |
| 26 May 2155  |
| 02-Aug-2160  |
| 31 May 1963  |
| 05302131     |

Output Table:
| Input        | Output    |
|:-------------|:----------|
| 02 Sep 1747  | Saturday  |
| 22 Oct 1815  | Sunday    |
| 21 June 2059 | Saturday  |
| 26-Mar-1816  | Tuesday   |
| Jul 08 2237  | Saturday  |
| 3 Nov 1904   | Thursday  |
| 29 Jan 2218  | Thursday  |
| 21-Jan-1818  | Wednesday |
| May 25 1817  | Sunday    |
| 06-Oct-2143  | Sunday    |
| 03302241     | Tuesday   |
| 26 May 2155  | Monday    |
| 02-Aug-2160  | Saturday  |
| 31 May 1963  | Friday    |
| 05302131     | Wednesday |



filename: DateTime.000029
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    |
|:---------|
| 21:44:40 |
| 16:15:08 |
| 07:00:20 |

Output Table:
| Input    | Output      |
|:---------|:------------|
| 21:44:40 | 8PM to 10PM |
| 16:15:08 | 4PM to 6PM  |
| 07:00:20 | 6AM to 8AM  |



filename: DateTime.000049
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     |
|:----------|
| 3-30-2002 |
| 3-30-2241 |
| 1-31-1846 |

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



filename: DateTime.000036
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   |
|:--------|
| 43      |
| 20+     |
|         |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 43      |       43 |
| 20+     |       20 |
|         |        0 |



filename: City.000010
instruction: Conditional
Numeric
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                                                      |
|:-----------------------------------------------------------|
| 22ABDDK_3388.XY,9/12/2002 2:51:17 AM,8.111111,311,chicago  |
| 22ABDDK_3388.XY,9/12/2002 8:14:07 AM,8.111111,311,chicago  |
| 22ABDDK_3388.XY,9/12/2002 6:25:57 PM,8.111111,311,chicago  |
| 22ABDDK_3388.XY,9/12/2002 3:25:29 PM,8.111111,311,chicago  |
| 22ABDDK_3388.XY,9/12/2002 11:48:20 PM,8.111111,311,chicago |
| 22ABDDK_3388.XY,9/12/2002 7:00:20 AM,8.111111,311,chicago  |

Output Table:
| Input                                                      | Output              |
|:-----------------------------------------------------------|:--------------------|
| 22ABDDK_3388.XY,9/12/2002 2:51:17 AM,8.111111,311,chicago  | 2002-09-12 02:51:17 |
| 22ABDDK_3388.XY,9/12/2002 8:14:07 AM,8.111111,311,chicago  | 2002-09-12 08:14:07 |
| 22ABDDK_3388.XY,9/12/2002 6:25:57 PM,8.111111,311,chicago  | 2002-09-12 18:25:57 |
| 22ABDDK_3388.XY,9/12/2002 3:25:29 PM,8.111111,311,chicago  | 2002-09-12 15:25:29 |
| 22ABDDK_3388.XY,9/12/2002 11:48:20 PM,8.111111,311,chicago | 2002-09-12 23:48:20 |
| 22ABDDK_3388.XY,9/12/2002 7:00:20 AM,8.111111,311,chicago  | 2002-09-12 07:00:20 |



filename: DateTime.000034
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                  |
|:-----------------------|
| 2 September 1747 03:34 |
| 22 October 1815 21:29  |
| 21 Jun 2059 07:58      |
| 3261816                |
| Jul 8 2237 15:25       |
| 3 Nov 1904 8:14        |
| 29 January 2218 11:46  |
| 1211818                |
| 25 May 1817 09:50      |
| 06-Oct-2143 18:25      |
| 3302241                |
| 26 May 2155 4:05       |
| 8022160                |
| 31 May 1963 04:40      |
| 05302131               |

Output Table:
| Input                  | Output   |
|:-----------------------|:---------|
| 2 September 1747 03:34 | Q3 '1747 |
| 22 October 1815 21:29  | Q4 '1815 |
| 21 Jun 2059 07:58      | Q2 '2059 |
| 3261816                | Q1 '1816 |
| Jul 8 2237 15:25       | Q3 '2237 |
| 3 Nov 1904 8:14        | Q4 '1904 |
| 29 January 2218 11:46  | Q1 '2218 |
| 1211818                | Q1 '1818 |
| 25 May 1817 09:50      | Q2 '1817 |
| 06-Oct-2143 18:25      | Q4 '2143 |
| 3302241                | Q1 '2241 |
| 26 May 2155 4:05       | Q2 '2155 |
| 8022160                | Q3 '2160 |
| 31 May 1963 04:40      | Q2 '1963 |
| 05302131               | Q2 '2131 |



filename: DateTime.000027
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        |
|:-------------|
| 02 Sep 1747  |
| 22 Oct 1815  |
| 21 June 2059 |
| 26-Mar-1816  |
| Jul 08 2237  |
| 3 Nov 1904   |
| 29 Jan 2218  |
| 21-Jan-1818  |
| May 25 1817  |
| 06-Oct-2143  |
| 03302241     |
| 26 May 2155  |
| 02-Aug-2160  |
| 31 May 1963  |
| 05302131     |

Output Table:
| Input        | Output     |
|:-------------|:-----------|
| 02 Sep 1747  | 2/9/1747   |
| 22 Oct 1815  | 22/10/1815 |
| 21 June 2059 | 21/6/2059  |
| 26-Mar-1816  | 26/3/1816  |
| Jul 08 2237  | 8/7/2237   |
| 3 Nov 1904   | 3/11/1904  |
| 29 Jan 2218  | 29/1/2218  |
| 21-Jan-1818  | 21/1/1818  |
| May 25 1817  | 25/5/1817  |
| 06-Oct-2143  | 6/10/2143  |
| 03302241     | 30/3/2241  |
| 26 May 2155  | 26/5/2155  |
| 02-Aug-2160  | 2/8/2160   |
| 31 May 1963  | 31/5/1963  |
| 05302131     | 30/5/2131  |



filename: DateTime.000017
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    |
|:---------|
| 08:29:52 |
| 04:54:07 |
| 12:41:14 |

Output Table:
| Input    | Output      |
|:---------|:------------|
| 08:29:52 | 8:00-8:30   |
| 04:54:07 | 4:30-5:00   |
| 12:41:14 | 12:30-13:00 |



filename: DateTime.000050
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     |
|:----------|
| 30/3/2002 |
| 30/3/2241 |
| 31/1/1846 |

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



filename: DateTime.000043
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    |
|:---------|
| 08:29:52 |
| 04:54:07 |
| 12:41:14 |

Output Table:
| Input    | Output         |
|:---------|:---------------|
| 08:29:52 | 8:00AM-8:30AM  |
| 04:54:07 | 4:30AM-5:00AM  |
| 12:41:14 | 12:30PM-1:00PM |



filename: DateTime.000051
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                         |
|:------------------------------|
| Word 3 Word 30 Word 2002 Word |
| Word 3 Word 30 Word 2241 Word |
| Word 1 Word 31 Word 1846 Word |

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



filename: DateTime.000040
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     |
|:----------|
| 30/3/2002 |
| 30/3/2241 |
| 31/1/1846 |

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



filename: DateTime.000038
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       |
|:------------|
| 15.3.1902   |
| 12/24/2045  |
| 6/28/2137   |
| 73/193/2010 |

Output Table:
| Input       | Output      |
|:------------|:------------|
| 15.3.1902   | 3/15/1902   |
| 12/24/2045  | 12/24/2045  |
| 6/28/2137   | 6/28/2137   |
| 73/193/2010 | 73/193/2010 |



filename: DateTime.000041
instruction: Conditional
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        |
|:-------------|
| 02 Sep 1747  |
| 22 Oct 1815  |
| 21 June 2059 |
| 26-Mar-1816  |
| Jul 08 2237  |
| 3 Nov 1904   |
| 29 Jan 2218  |
| 21-Jan-1818  |
| May 25 1817  |
| 06-Oct-2143  |
| 03302241     |
| 26 May 2155  |
| 02-Aug-2160  |
| 31 May 1963  |
| 05302131     |

Output Table:
| Input        | Output                      |
|:-------------|:----------------------------|
| 02 Sep 1747  | Saturday, September 2, 1747 |
| 22 Oct 1815  | Sunday, October 22, 1815    |
| 21 June 2059 | Saturday, June 21, 2059     |
| 26-Mar-1816  | Tuesday, March 26, 1816     |
| Jul 08 2237  | Saturday, July 8, 2237      |
| 3 Nov 1904   | Thursday, November 3, 1904  |
| 29 Jan 2218  | Thursday, January 29, 2218  |
| 21-Jan-1818  | Wednesday, January 21, 1818 |
| May 25 1817  | Sunday, May 25, 1817        |
| 06-Oct-2143  | Sunday, October 6, 2143     |
| 03302241     | Tuesday, March 30, 2241     |
| 26 May 2155  | Monday, May 26, 2155        |
| 02-Aug-2160  | Saturday, August 2, 2160    |
| 31 May 1963  | Friday, May 31, 1963        |
| 05302131     | Wednesday, May 30, 2131     |



filename: DateTime.000030
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    |
|:---------|
| 21:44:40 |
| 16:15:08 |
| 07:00:20 |

Output Table:
| Input    | Output               |
|:---------|:---------------------|
| 21:44:40 | Between 8PM and 10PM |
| 16:15:08 | Between 4PM and 6PM  |
| 07:00:20 | Between 6AM and 8AM  |



filename: DateTime.000048
instruction: Concatenation
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           |
|:----------------|
| 3/11/1904       |
| 2192 100        |
| 26/6/2079       |
| Sunday, 2143W12 |
| 8/7/2237        |

Output Table:
| Input           | Output    |
|:----------------|:----------|
| 3/11/1904       | 1904 3 11 |
| 2192 100        | 2192 9 4  |
| 26/6/2079       | 2079 26 6 |
| Sunday, 2143W12 | 2143 24 3 |
| 8/7/2237        | 2237 8 7  |



filename: DateTime.000047
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    |
|:---------|
| 08:29:52 |
| 04:54:07 |
| 12:41:14 |

Output Table:
| Input    | Output         |
|:---------|:---------------|
| 08:29:52 | 8:00AM-9:50AM  |
| 04:54:07 | 4:00AM-5:50AM  |
| 12:41:14 | 12:00PM-1:50PM |



filename: DateTime.000061
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     |
|:----------|
| 2192 100  |
| 14/7/2143 |
| 7/2/1952  |

Output Table:
| Input     | Output    |
|:----------|:----------|
| 2192 100  | 2192 9 4  |
| 14/7/2143 | 2143 14 7 |
| 7/2/1952  | 1952 7 2  |



filename: DateTime.000046
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  | 3:00PM-3:29PM   |
| 9:44PM  | 9:30PM-9:59PM   |
| 11:48PM | 11:30PM-11:59PM |
| 6:25PM  | 6:00PM-6:29PM   |
| 4:29AM  | 4:00AM-4:29AM   |



filename: DateTime.000075
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 |
|--------:|
|     825 |
|     114 |

Output Table:
|   Input | Output        |
|--------:|:--------------|
|     825 | 8:00AM-8:30AM |
|     114 | 1:00AM-1:30AM |



filename: DateTime.000053
instruction: DateTimeRange
DateTimeRounding
DateTime
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv