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

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                                                              |
|:-------------------------------------------------------------------|
| <head> <title>A Simple HTML Example</title> </head>                |
| <h2>test1</h2> <p>test2</p> <p>test3</p> <p>test4</p>              |
| <h2>HTML is Easy To Learn</h2> <p>Welcome to the world of the </p> |

Output Table:
| Input                                                              | Output                                                            |
|:-------------------------------------------------------------------|:------------------------------------------------------------------|
| <head> <title>A Simple HTML Example</title> </head>                | <head><title>A Simple HTML Example</title></head>                 |
| <h2>test1</h2> <p>test2</p> <p>test3</p> <p>test4</p>              | <h2>test1</h2><p>test2</p><p>test3</p><p>test4</p>                |
| <h2>HTML is Easy To Learn</h2> <p>Welcome to the world of the </p> | <h2>HTML is Easy To Learn</h2><p>Welcome to the world of the </p> |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/8.txt
instruction: //Remove spaces in HTML source in between markups?
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                              |
|:-----------------------------------|
| 45 1ST WAY SE, Mandalay, MT 58712  |
| 2345 SW 5th Road, Eugene, CA 38891 |

Output Table:
| Input                              | Output   |
|:-----------------------------------|:---------|
| 45 1ST WAY SE, Mandalay, MT 58712  | Mandalay |
| 2345 SW 5th Road, Eugene, CA 38891 | Eugene   |



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

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

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

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

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

Input Table:
| Input                                                                          |
|:-------------------------------------------------------------------------------|
| Kotonexxxxx Janjic 576 09th Lane ,Surprise,TX,(055) 296-3466,000-47-2445,17293 |
| Fahmi Ibrahim 435 30th Lane ,Tangshan,IL,(471) 189-3968,000-69-9417,38891      |
| Sonia Rees 319 09th Lane ,Baltimore,NY,(012) 035-3987,000-22-1009,03719        |

Output Table:
| Input                                                                          | Output        |
|:-------------------------------------------------------------------------------|:--------------|
| Kotonexxxxx Janjic 576 09th Lane ,Surprise,TX,(055) 296-3466,000-47-2445,17293 | 576 09th Lane |
| Fahmi Ibrahim 435 30th Lane ,Tangshan,IL,(471) 189-3968,000-69-9417,38891      | 435 30th Lane |
| Sonia Rees 319 09th Lane ,Baltimore,NY,(012) 035-3987,000-22-1009,03719        | 319 09th Lane |



filename: Address.000003
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input             |
|:------------------|
| Abdoulaye Drubych |
| 123 First Ln.     |
| Vancouver         |
| MI                |
| 38891             |
| Rohini Che        |
| 90 Cherry Pkwy.   |
| Cleveland         |
| NE                |
| 38891             |

Output Table:
| Input             | Output          |
|:------------------|:----------------|
| Abdoulaye Drubych | 123 First Ln.   |
| 123 First Ln.     |                 |
| Vancouver         |                 |
| MI                |                 |
| 38891             |                 |
| Rohini Che        | 90 Cherry Pkwy. |
| 90 Cherry Pkwy.   |                 |
| Cleveland         |                 |
| NE                |                 |
| 38891             |                 |



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

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

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

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

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

Input Table:
| Input            |
|:-----------------|
| Hone Mukasine    |
| 456 Fifth Rd     |
| Elgin            |
| MI               |
| 58712            |
| Lisandro Zujovic |
| 678 Apple Rte    |
| Seoul            |
| TX               |
| 58712            |

Output Table:
| Input            | Output           |
|:-----------------|:-----------------|
| Hone Mukasine    | Hone Mukasine    |
| 456 Fifth Rd     |                  |
| Elgin            |                  |
| MI               |                  |
| 58712            |                  |
| Lisandro Zujovic | Lisandro Zujovic |
| 678 Apple Rte    |                  |
| Seoul            |                  |
| TX               |                  |
| 58712            |                  |



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

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

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

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

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

Input Table:
| Input         |
|:--------------|
| Consectetur   |
| Nihil Impedit |

Output Table:
| Input         | Output   |
|:--------------|:---------|
| Consectetur   | C        |
| Nihil Impedit | NI       |



filename: Abbreviation.000001
instruction: Concatenation
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                                  |
|:---------------------------------------|
| One Madeup Street, Lancaster, MD 58712 |
| 840 SW 126th Way, Oakland, CT 03719    |

Output Table:
| Input                                  | Output    |
|:---------------------------------------|:----------|
| One Madeup Street, Lancaster, MD 58712 | Lancaster |
| 840 SW 126th Way, Oakland, CT 03719    | Oakland   |



filename: Address.000010
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input          |
|:---------------|
| Cong Garayewa  |
| 3456 48th Pkwy |
| Reno           |
| MN             |
| 41230          |
| Yashita Vasala |
| 1234 Elm Ct    |
| Fullerton      |
| ME             |
| 41230          |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Cong Garayewa  | MN       |
| 3456 48th Pkwy |          |
| Reno           |          |
| MN             |          |
| 41230          |          |
| Yashita Vasala | ME       |
| 1234 Elm Ct    |          |
| Fullerton      |          |
| ME             |          |
| 41230          |          |



filename: Address.000007
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input             |
|:------------------|
| Abdoulaye Drubych |
| 123 First Ln      |
| Vancouver         |
| MI                |
| 38891             |
| Rohini Che        |
| 90 Cherry Pkwy    |
| Cleveland         |
| NE                |
| 38891             |

Output Table:
| Input             |   Output |
|:------------------|---------:|
| Abdoulaye Drubych |    38891 |
| 123 First Ln      |          |
| Vancouver         |          |
| MI                |          |
| 38891             |          |
| Rohini Che        |    38891 |
| 90 Cherry Pkwy    |          |
| Cleveland         |          |
| NE                |          |
| 38891             |          |



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

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

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

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

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

Input Table:
| Input          |
|:---------------|
| Sukma Meszaros |
| 678 Apple Rte  |
| Busan          |
| WV             |
| 38891          |
| Sanjar Ibrahim |
| 23 Second Blvd |
| Algiers        |
| WI             |
| 03719          |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Sukma Meszaros | Busan    |
| 678 Apple Rte  |          |
| Busan          |          |
| WV             |          |
| 38891          |          |
| Sanjar Ibrahim | Algiers  |
| 23 Second Blvd |          |
| Algiers        |          |
| WI             |          |
| 03719          |          |



filename: Address.000006
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input                                       |
|:--------------------------------------------|
| 345 Fifth Way SW, Lakewood, TX 41230        |
| 78 Seventh Boulevard SE, Denver, WY 03719   |
| One Madeup Street, Prague, KY 58712         |
| 2345 Redwood Road NE, Greensboro, VA 38891  |
| One Madeup Street, Belo Horizonte, MD 41230 |

Output Table:
| Input                                       | Output   |
|:--------------------------------------------|:---------|
| 345 Fifth Way SW, Lakewood, TX 41230        | 345      |
| 78 Seventh Boulevard SE, Denver, WY 03719   | 78       |
| One Madeup Street, Prague, KY 58712         |          |
| 2345 Redwood Road NE, Greensboro, VA 38891  | 2345     |
| One Madeup Street, Belo Horizonte, MD 41230 |          |



filename: Address.000012
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                                  |
|:---------------------------------------|
| 789 Oak Pkwy, Yangon, OR 41230         |
| 234 Maple Blvd, Edison, AK 58712       |
| 234 Fourth Ct, Omsk, OK 41230          |
| One Cherry Court, Greensboro, VA 38891 |
| One Second Way, Ekurhuleni, NY 58712   |

Output Table:
| Input                                  | Output   |
|:---------------------------------------|:---------|
| 789 Oak Pkwy, Yangon, OR 41230         | 789      |
| 234 Maple Blvd, Edison, AK 58712       | 234      |
| 234 Fourth Ct, Omsk, OK 41230          | 234      |
| One Cherry Court, Greensboro, VA 38891 |          |
| One Second Way, Ekurhuleni, NY 58712   |          |



filename: Address.000013
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                                                                                    |
|:-----------------------------------------------------------------------------------------|
| Fiamma Greco 967 03th Place SE,Long Beach,OK,(129) 734-1247,000-61-4879,03719            |
| Elise Vadeboncoeur 526 03th Place NE,Curitiba,OH,(993) 525-5024,000-84-6131,03719        |
| Muralixxxxx Hasmik Drakou 880 81th Place SE,Brasília,WY,(467) 817-7191,000-47-5407,17293 |

Output Table:
| Input                                                                                    | Output            |
|:-----------------------------------------------------------------------------------------|:------------------|
| Fiamma Greco 967 03th Place SE,Long Beach,OK,(129) 734-1247,000-61-4879,03719            | 967 03th Place SE |
| Elise Vadeboncoeur 526 03th Place NE,Curitiba,OH,(993) 525-5024,000-84-6131,03719        | 526 03th Place NE |
| Muralixxxxx Hasmik Drakou 880 81th Place SE,Brasília,WY,(467) 817-7191,000-47-5407,17293 | 880 81th Place    |



filename: Address.000002
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input      |
|:-----------|
| [CPT-11529 |
| 11529      |
| [AP-1089   |

Output Table:
| Input      | Output      |
|:-----------|:------------|
| [CPT-11529 | [CPT-11529] |
| 11529      | 11529]      |
| [AP-1089   | [AP-1089]   |



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

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

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

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

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

Input Table:
| Input        |
|:-------------|
| [CPT-00340   |
| [CPT-AB0     |
| [CPT-114563] |

Output Table:
| Input        | Output        |
|:-------------|:--------------|
| [CPT-00340   | [CPT-00340]   |
| [CPT-AB0     | [CPT-AB0]     |
| [CPT-114563] | [CPT-114563]] |



filename: BillingCode.000003
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                                                    |
|:---------------------------------------------------------|
| Canada 3000 folded, discount airline                     |
| Greyhound Air, winnow                                    |
| Canadian Pacific Airlines, acquired by Canadian Airlines |

Output Table:
| Input                                                    | Output                    |
|:---------------------------------------------------------|:--------------------------|
| Canada 3000 folded, discount airline                     | Canada 3000 folded        |
| Greyhound Air, winnow                                    | Greyhound Air             |
| Canadian Pacific Airlines, acquired by Canadian Airlines | Canadian Pacific Airlines |



filename: Airline.000002
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input   |
|:--------|
| CCC     |
| Column2 |
| AAA     |

Output Table:
| Input   | Output   |
|:--------|:---------|
| CCC     | ccc      |
| Column2 | Col_2    |
| AAA     | aaa      |



filename: Column.000002
instruction: Casing
Concatenation
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         |
|:--------------|
| [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.000002
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   |
|:--------|
| Coln8   |
| Coln5   |
| Coln4   |
| ID      |

Output Table:
| Input   | Output   |
|:--------|:---------|
| Coln8   | Col8     |
| Coln5   | Col5     |
| Coln4   | Col4     |
| ID      | Col1     |



filename: Column.000001
instruction: Concatenation
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    |
|:---------|
| [CPT-101 |
| [CPT-11] |

Output Table:
| Input    | Output    |
|:---------|:----------|
| [CPT-101 | [CPT-101] |
| [CPT-11] | [CPT-11]  |



filename: BillingCode.000006
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      |
|:-----------|
| [CPT-00350 |
| [CPT-00340 |

Output Table:
| Input      | Output      |
|:-----------|:------------|
| [CPT-00350 | [CPT-00350] |
| [CPT-00340 | [CPT-00340] |



filename: BillingCode.000008
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      |
|:-----------|
| [AP-1089   |
| [CPT-11529 |
| 11529      |

Output Table:
| Input      | Output      |
|:-----------|:------------|
| [AP-1089   | [AP-1089]   |
| [CPT-11529 | [CPT-11529] |
| 11529      | 11529]      |



filename: BillingCode.000007
instruction: Concatenation
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input         |
|:--------------|
|               |
| san francisco |
|               |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
|               | Empty         |
| san francisco | San Francisco |
|               |               |



filename: City.000007
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   |
|:--------|
| �9.92   |
| $34.90  |
| �42.58  |
| $47.36  |
| �44.65  |
| $4.03   |
| $25.21  |
| $45.20  |
| �33.30  |
| $31.56  |

Output Table:
| Input   | Output   |
|:--------|:---------|
| �9.92   | �        |
| $34.90  | $        |
| �42.58  | �        |
| $47.36  | $        |
| �44.65  | �        |
| $4.03   | $        |
| $25.21  | $        |
| $45.20  | $        |
| �33.30  | �        |
| $31.56  | $        |



filename: Currency.000004
instruction: Numeric
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         |
|:--------------|
|               |
| san francisco |
|               |

Output Table:
| Input         | Output        |
|:--------------|:--------------|
|               |               |
| san francisco | San Francisco |
|               |               |



filename: City.000006
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   |
|:--------|
| �9.92   |
| $34.90  |
| �42.58  |
| $47.36  |
| �44.65  |
| $4.03   |
| $25.21  |
| $45.20  |
| �33.30  |
| $31.56  |

Output Table:
| Input   |   Output |
|:--------|---------:|
| �9.92   |     9.92 |
| $34.90  |    34.9  |
| �42.58  |    42.58 |
| $47.36  |    47.36 |
| �44.65  |    44.65 |
| $4.03   |     4.03 |
| $25.21  |    25.21 |
| $45.20  |    45.2  |
| �33.30  |    33.3  |
| $31.56  |    31.56 |



filename: Currency.000003
instruction: Numeric
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv