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         |
|:--------------|
| [CPT-1153622] |
| [CPT-00350    |
| [CPT-115      |
| [CPT-1163     |
| [CPT-11536    |
| [CPT-00340    |

Output Table:
| Input         | Output         |
|:--------------|:---------------|
| [CPT-1153622] | [CPT-1153622]] |
| [CPT-00350    | [CPT-00350]    |
| [CPT-115      | [CPT-115]      |
| [CPT-1163     | [CPT-1163]     |
| [CPT-11536    | [CPT-11536]    |
| [CPT-00340    | [CPT-00340]    |



filename: BillingCode.000001
instruction: Concatenation
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input    |
|:---------|
| 15:25:29 |
| 21:44:40 |
| 23:48:20 |
| 18:25:57 |
| 04:29:27 |

Output Table:
| Input    |   Output |
|:---------|---------:|
| 15:25:29 |       15 |
| 21:44:40 |       21 |
| 23:48:20 |       23 |
| 18:25:57 |       18 |
| 04:29:27 |        4 |



filename: DateTime.000003
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         |
|:--------------|
|               |
|               |
| zzzzzz        |
| SAN FRANCISCO |
|               |
| ᠎              |
|               |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
|               | Empty         |
|               | Empty         |
| zzzzzz        | San Francisco |
| SAN FRANCISCO | San Francisco |
|               | San Francisco |
| ᠎              | San Francisco |
|               | San Francisco |



filename: City.000005
instruction: Conditional
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input         |
|:--------------|
| SCN FRANCISAO |
| SIN FRANCASCO |
| san fraccisno |
| S.F           |
| san francisco |
| S.F`          |
|               |
| SF.           |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
| SCN FRANCISAO | San Francisco |
| SIN FRANCASCO | San Francisco |
| san fraccisno | San Francisco |
| S.F           | San Francisco |
| san francisco | San Francisco |
| S.F`          | San Francisco |
|               |               |
| SF.           | San Francisco |



filename: City.000004
instruction: Conditional
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input         |
|:--------------|
|               |
| SAN FRANCISCO |
|               |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
|               |               |
| SAN FRANCISCO | San Francisco |
|               |               |



filename: City.000008
instruction: Conditional
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input                                     |
|:------------------------------------------|
| Aliquam - January 28, 1746 - with Gebhard |

Output Table:
| Input                                     |   Output |
|:------------------------------------------|---------:|
| Aliquam - January 28, 1746 - with Gebhard |     1746 |



filename: DateTime.000010
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            |
|:-----------------|
| 2130             |
| 12 March 1933    |
| Unknown          |
| 1756             |
| 4 September 2088 |

Output Table:
| Input            | Output   |
|:-----------------|:---------|
| 2130             | 2130     |
| 12 March 1933    | 1933     |
| Unknown          |          |
| 1756             | 1756     |
| 4 September 2088 | 2088     |



filename: DateTime.000012
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            |
|:-----------------|
| 2143-10-06 06:25 |
| 1816-03-26 11:59 |
| 1815-10-22 09:29 |
| 2002-03-30 09:44 |
| 2237-07-08 03:25 |
| 2160-08-02 11:48 |
| 2131-05-30 08:05 |
| 1953-04-22 11:34 |
| 2090-04-26 07:25 |
| 2155-05-26 04:05 |

Output Table:
| Input            | Output                 |
|:-----------------|:-----------------------|
| 2143-10-06 06:25 | Oct 06, 2143 6AM-8AM   |
| 1816-03-26 11:59 | Mar 26, 1816 10AM-12PM |
| 1815-10-22 09:29 | Oct 22, 1815 8AM-10AM  |
| 2002-03-30 09:44 | Mar 30, 2002 8AM-10AM  |
| 2237-07-08 03:25 | Jul 08, 2237 2AM-4AM   |
| 2160-08-02 11:48 | Aug 02, 2160 10AM-12PM |
| 2131-05-30 08:05 | May 30, 2131 8AM-10AM  |
| 1953-04-22 11:34 | Apr 22, 1953 10AM-12PM |
| 2090-04-26 07:25 | Apr 26, 2090 6AM-8AM   |
| 2155-05-26 04:05 | May 26, 2155 4AM-6AM   |



filename: DateTime.000008
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           |
|:----------------|
| 17-Jun-43       |
| 27-Nov-40       |
| 19-Oct-61       |
| 1887            |
| 5 December 1870 |
| 6-Nov-62        |
| 27 August 1635  |
| 2001            |
| 24-Dec-61       |
| Living          |
| 28-Nov-68       |
| 10-Mar-07       |
| 2-Dec-65        |
| 8 November 1240 |
| 1111            |
| 2003            |

Output Table:
| Input           |   Output |
|:----------------|---------:|
| 17-Jun-43       |     1943 |
| 27-Nov-40       |     1940 |
| 19-Oct-61       |     1961 |
| 1887            |     1887 |
| 5 December 1870 |     1870 |
| 6-Nov-62        |     1962 |
| 27 August 1635  |     1635 |
| 2001            |     2001 |
| 24-Dec-61       |     1961 |
| Living          |     2016 |
| 28-Nov-68       |     1968 |
| 10-Mar-07       |     2007 |
| 2-Dec-65        |     1965 |
| 8 November 1240 |     1240 |
| 1111            |     1111 |
| 2003            |     2003 |



filename: Author.000001
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                |
|:---------------------|
| 2025-07-28T11:43:30Z |
| 2025-04-11T11:26:36Z |
| 2026-12-30T06:25:57Z |
| 2027-02-01T01:29:43Z |
| 2026-06-20T05:14:30Z |
| 2024-11-03T09:08:38Z |
| 2027-01-08T05:15:37Z |
| 2026-04-15T09:44:40Z |
| &4%O6b7J(AaOGgJ0     |
| 2025-08-14T07:36:20Z |
|                      |
| 2024-12-29T08:07:33Z |
| 2026-10-03T05:14:34Z |

Output Table:
| Input                | Output      |
|:---------------------|:------------|
| 2025-07-28T11:43:30Z | Jul 2025    |
| 2025-04-11T11:26:36Z | Apr 2025    |
| 2026-12-30T06:25:57Z | Dec 2026    |
| 2027-02-01T01:29:43Z | Feb 2027    |
| 2026-06-20T05:14:30Z | Jun 2026    |
| 2024-11-03T09:08:38Z | Nov 2024    |
| 2027-01-08T05:15:37Z | Jan 2027    |
| 2026-04-15T09:44:40Z | Apr 2026    |
| &4%O6b7J(AaOGgJ0     | Not a date. |
| 2025-08-14T07:36:20Z | Aug 2025    |
|                      | Not a date. |
| 2024-12-29T08:07:33Z | Dec 2024    |
| 2026-10-03T05:14:34Z | Oct 2026    |



filename: DateTime.000005
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          |
|:---------------|
| 12171973       |
| Mar 1983 Tue 3 |
| 17 Dec 1973    |
| 30 Dec 1997    |
| 12 Dec 2002    |
| 02-Jan-1998    |
| Jan 10 1975    |
| 15 Aug 1947    |

Output Table:
| Input          | Output            |
|:---------------|:------------------|
| 12171973       | Monday, 1973W51   |
| Mar 1983 Tue 3 | Tuesday, 1983W11  |
| 17 Dec 1973    | Monday, 1973W51   |
| 30 Dec 1997    | Tuesday, 1998W01  |
| 12 Dec 2002    | Thursday, 2002W50 |
| 02-Jan-1998    | Friday, 1998W01   |
| Jan 10 1975    | Friday, 1975W02   |
| 15 Aug 1947    | Friday, 1947W33   |



filename: DateTime.000015
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       |
|:------------|
| [CPT-11523] |
| [CPT-2422]  |
| [CPT-23412] |

Output Table:
| Input       | Output      |
|:------------|:------------|
| [CPT-11523] | [CPT-11523] |
| [CPT-2422]  | [CPT-2422]  |
| [CPT-23412] | [CPT-23412] |



filename: BillingCode.000005
instruction: Concatenation
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input          |
|:---------------|
| 1 January 2015 |
| 8 January 2015 |
| 1 Feb 2013     |
| 30 Dec 1997    |
| 1 Jan 1999     |
| 1932 97        |
| 5 June 2016    |
| 02-Jan-1998    |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| 1 January 2015 | Thu W1   |
| 8 January 2015 | Thu W2   |
| 1 Feb 2013     | Fri W5   |
| 30 Dec 1997    | Tue W1   |
| 1 Jan 1999     | Fri W53  |
| 1932 97        | Wed W14  |
| 5 June 2016    | Sun W22  |
| 02-Jan-1998    | Fri W1   |



filename: DateTime.000016
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          |
|:---------------|
| Jul 08 2237    |
| Mar 30 2002    |
| 02-Aug-2160    |
| 06-Oct-2143    |
| 15 August 1740 |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Jul 08 2237    |          |
| Mar 30 2002    |          |
| 02-Aug-2160    |          |
| 06-Oct-2143    |          |
| 15 August 1740 |          |



filename: DateTime.000020
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          |
|:---------------|
| Mar 1983 Tue 3 |
| 12171973       |
| 17 Dec 1973    |
| 02-Jan-1998    |
| 12 Dec 2002    |
| Jan 10 1975    |
| 15 Aug 1947    |

Output Table:
| Input          | Output                    |
|:---------------|:--------------------------|
| Mar 1983 Tue 3 | Tuesday #3 March 1983     |
| 12171973       | Monday #3 December 1973   |
| 17 Dec 1973    | Monday #3 December 1973   |
| 02-Jan-1998    | Friday #1 January 1998    |
| 12 Dec 2002    | Thursday #2 December 2002 |
| Jan 10 1975    | Friday #2 January 1975    |
| 15 Aug 1947    | Friday #3 August 1947     |



filename: DateTime.000014
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 PM |
| 11:48:20 PM | 23:48 PM |
| 6:25:57 PM  | 18:25 PM |
| 4:29:27 AM  | 4:29 AM  |



filename: DateTime.000033
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                |
|:---------------------|
| 1961-09-21T12:44:55Z |
| 1978-11-30T11:17:23Z |
| 2041-11-23T01:17:33Z |
| 2017-03-09T02:00:55Z |
| 1932-03-07T02:24:22Z |
| 2041-06-16T05:46:33Z |
| 1961-10-22T11:57:18Z |
| 2023-11-11T04:17:30Z |
| 2021-03-02T07:15:34Z |
| 1953-12-14T08:35:28Z |
| 1974-12-16T02:32:49Z |
| 1925-01-21T12:52:40Z |
| 2027-01-16T01:14:49Z |

Output Table:
| Input                | Output   |
|:---------------------|:---------|
| 1961-09-21T12:44:55Z | Sep 1961 |
| 1978-11-30T11:17:23Z | Nov 1978 |
| 2041-11-23T01:17:33Z | Nov 2041 |
| 2017-03-09T02:00:55Z | Mar 2017 |
| 1932-03-07T02:24:22Z | Mar 1932 |
| 2041-06-16T05:46:33Z | Jun 2041 |
| 1961-10-22T11:57:18Z | Oct 1961 |
| 2023-11-11T04:17:30Z | Nov 2023 |
| 2021-03-02T07:15:34Z | Mar 2021 |
| 1953-12-14T08:35:28Z | Dec 1953 |
| 1974-12-16T02:32:49Z | Dec 1974 |
| 1925-01-21T12:52:40Z | Jan 1925 |
| 2027-01-16T01:14:49Z | Jan 2027 |



filename: DateTime.000007
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         |
|:--------------|
| New York City |
| n.y.c.        |
|               |
| New York City |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
| New York City | New York City |
| n.y.c.        | New York City |
|               |               |
| New York City | New York City |



filename: City.000011
instruction: Conditional
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

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



filename: DateTime.000018
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                   |
|:------------------------|
| 29 January 2218 11:46   |
| 26 May 2155 4:05        |
| 01211818                |
| 26-Apr-2090 19:25       |
| 11071712                |
| 1972 100                |
| 03-Apr-2008 5:24        |
| 16 September 2075 15:07 |
| 31 May 1963 04:40       |
| 06-Oct-2143 18:25       |
| 2067 309                |
| 03261816                |
| 1714 340                |
| 11242098                |
| 25 May 1817 09:50       |

Output Table:
| Input                   | Output   |
|:------------------------|:---------|
| 29 January 2218 11:46   | 29 2218  |
| 26 May 2155 4:05        | 146 2155 |
| 01211818                | 21 1818  |
| 26-Apr-2090 19:25       | 116 2090 |
| 11071712                | 312 1712 |
| 1972 100                | 100 1972 |
| 03-Apr-2008 5:24        | 94 2008  |
| 16 September 2075 15:07 | 259 2075 |
| 31 May 1963 04:40       | 151 1963 |
| 06-Oct-2143 18:25       | 279 2143 |
| 2067 309                | 309 2067 |
| 03261816                | 86 1816  |
| 1714 340                | 340 1714 |
| 11242098                | 328 2098 |
| 25 May 1817 09:50       | 145 1817 |



filename: DateTime.000021
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  | 9/2/1747   |
| 22 Oct 1815  | 10/22/1815 |
| 21 June 2059 | 6/21/2059  |
| 26-Mar-1816  | 3/26/1816  |
| Jul 08 2237  | 7/8/2237   |
| 3 Nov 1904   | 11/3/1904  |
| 29 Jan 2218  | 1/29/2218  |
| 21-Jan-1818  | 1/21/1818  |
| May 25 1817  | 5/25/1817  |
| 06-Oct-2143  | 10/6/2143  |
| 03302241     | 3/30/2241  |
| 26 May 2155  | 5/26/2155  |
| 02-Aug-2160  | 8/2/2160   |
| 31 May 1963  | 5/31/1963  |
| 05302131     | 5/30/2131  |



filename: DateTime.000022
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  | 9-02-1747  |
| 22 Oct 1815  | 10-22-1815 |
| 21 June 2059 | 6-21-2059  |
| 26-Mar-1816  | 3-26-1816  |
| Jul 08 2237  | 7-08-2237  |
| 3 Nov 1904   | 11-03-1904 |
| 29 Jan 2218  | 1-29-2218  |
| 21-Jan-1818  | 1-21-1818  |
| May 25 1817  | 5-25-1817  |
| 06-Oct-2143  | 10-06-2143 |
| 03302241     | 3-30-2241  |
| 26 May 2155  | 5-26-2155  |
| 02-Aug-2160  | 8-02-2160  |
| 31 May 1963  | 5-31-1963  |
| 05302131     | 5-30-2131  |



filename: DateTime.000024
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  | September 1747 |
| 22 Oct 1815  | October 1815   |
| 21 June 2059 | June 2059      |
| 26-Mar-1816  | March 1816     |
| Jul 08 2237  | July 2237      |
| 3 Nov 1904   | November 1904  |
| 29 Jan 2218  | January 2218   |
| 21-Jan-1818  | January 1818   |
| May 25 1817  | May 1817       |
| 06-Oct-2143  | October 2143   |
| 03302241     | March 2241     |
| 26 May 2155  | May 2155       |
| 02-Aug-2160  | August 2160    |
| 31 May 1963  | May 1963       |
| 05302131     | May 2131       |



filename: DateTime.000026
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            |
|:-----------------|
| 1877 358         |
| Apr 11 1699      |
| Aug 14 1944      |
| Jun 1779 Thu 2   |
| 26 Dec 2231      |
| Sat wk 8 in 2106 |
| 12 Dec 1718      |
| Jun 2135 Thu 2   |
| 06202214         |
| May 17 1835      |
| 11 May 2215      |
| 22 Oct 2124      |
| Oct 17 1892      |
| 03171719         |
| May 7 1791       |

Output Table:
| Input            | Output   |
|:-----------------|:---------|
| 1877 358         | W52      |
| Apr 11 1699      | W15      |
| Aug 14 1944      | W33      |
| Jun 1779 Thu 2   | W23      |
| 26 Dec 2231      | W52      |
| Sat wk 8 in 2106 | W08      |
| 12 Dec 1718      | W50      |
| Jun 2135 Thu 2   | W23      |
| 06202214         | W25      |
| May 17 1835      | W20      |
| 11 May 2215      | W19      |
| 22 Oct 2124      | W42      |
| Oct 17 1892      | W42      |
| 03171719         | W11      |
| May 7 1791       | W18      |



filename: DateTime.000028
instruction: DateTime
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv