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   |
|:--------|
| (       |
| !       |
| %       |
| %20     |
| '       |

Output Table:
| Input   | Output   |
|:--------|:---------|
| (       | %28      |
| !       | %21      |
| %       | %25      |
| %20     |          |
| '       | %27      |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/2.txt
instruction: //http://www.w3schools.com/tags/ref_urlencode.asp
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: 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   |
|:--------|
| 3+6     |
| 40-9    |
| 1+4/2   |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 3+6     |        9 |
| 40-9    |       31 |
| 1+4/2   |        3 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/30.txt
instruction: //Math expressions evaluation
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: 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 |
|--------:|
|      11 |
|      10 |
|       1 |
|       9 |
|       6 |
|       3 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|      11 |     24.3 |
|      10 |     22   |
|       1 |      2.2 |
|       9 |     19.8 |
|       6 |     13.2 |
|       3 |      6.6 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/10.txt
instruction: //kgs to pounds, one digit after the decimal, rounding
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: 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           |
|:----------------|
| *9kk12jf7eYTk1  |
| 11PP))(*(&^%%&* |
| ()kjfdsk13^$$*A |

Output Table:
| Input           | Output        |
|:----------------|:--------------|
| *9kk12jf7eYTk1  | 9kk12jf7eYTk1 |
| 11PP))(*(&^%%&* | 11PP          |
| ()kjfdsk13^$$*A | kjfdsk13A     |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/28.txt
instruction: //Most efficient way to remove special characters from string, only allow A-Z a-Z 0-9
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: 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      |
|:-----------|
| 01/12/2012 |
| 12/12/2012 |
| 03/14/2013 |

Output Table:
| Input      | Output   |
|:-----------|:---------|
| 01/12/2012 | January  |
| 12/12/2012 | December |
| 03/14/2013 | March    |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/37.txt
instruction: //Get month name from Date
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: 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   |
|:--------|
| CGCAT   |
| CGCAA   |
| ACCAG   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| CGCAT   | GCGTA    |
| CGCAA   | GCGTT    |
| ACCAG   | TGGTC    |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/32.txt
instruction: //Bioinformatics http://stackoverflow.com/questions/8811335/algorithm-help-fast-algorithm-in-searching-for-a-string-with-its-partner
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: 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                        |
|:-----------------------------|
| 'James Smith'                |
| 'Alice C. Doe'               |
| 'Mr. Charles P. Wooten, III' |

Output Table:
| Input                        | Output    |
|:-----------------------------|:----------|
| 'James Smith'                | J. Smith  |
| 'Alice C. Doe'               | A. Doe    |
| 'Mr. Charles P. Wooten, III' | C. Wooten |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/input_splitter2.txt
instruction: //upper caseing
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: 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                                           |
|:------------------------------------------------|
| <html><body>bla bla</td><td>bla bla<body><html> |
| <b>test</b>                                     |

Output Table:
| Input                                           | Output          |
|:------------------------------------------------|:----------------|
| <html><body>bla bla</td><td>bla bla<body><html> | bla bla bla bla |
| <b>test</b>                                     | test            |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/3.txt
instruction: //Remove HTML tags from string
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: 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         |
|:--------------|
| 1,2,3,5,6,7,8 |
| 1,2,3,4,5     |
| 1,2,3         |

Output Table:
| Input         |   Output |
|:--------------|---------:|
| 1,2,3,5,6,7,8 |        5 |
| 1,2,3,4,5     |        3 |
| 1,2,3         |        2 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/34.txt
instruction: //median
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: 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      |
|:-----------|
| 2008-12-12 |
| 2015-12-31 |
| 2009-03-31 |

Output Table:
| Input      | Output     |
|:-----------|:-----------|
| 2008-12-12 | 2008-12-13 |
| 2015-12-31 | 2016-01-01 |
| 2009-03-31 | 2010-04-01 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/35.txt
instruction: //increment date by one day
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: 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          |
|:---------------|
| honest mistake |
| hour           |
| computer       |

Output Table:
| Input          | Output            |
|:---------------|:------------------|
| honest mistake | an honest mistake |
| hour           | an hour           |
| computer       | a computer        |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/33.txt
instruction: //prefix a word with a, an
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: 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 |
|--------:|
|    2008 |
|    2012 |
|    2007 |
|    2017 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|    2008 | leap     |
|    2012 | leap     |
|    2007 | common   |
|    2017 | common   |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/42.txt
instruction: //Year to common year or leap year
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: 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      |
|:-----------|
| 2001/12/12 |
| 2012/4/11  |
| 2014/3/20  |

Output Table:
| Input      | Output     |
|:-----------|:-----------|
| 2001/12/12 | 12/12/2001 |
| 2012/4/11  | 4/11/2012  |
| 2014/3/20  | 3/20/2014  |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/36.txt
instruction: //convert y-m-d to m/d/y
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: 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                    |
|:-------------------------|
| http://google.com        |
| www.google.com           |
| stackoverflow.com        |
| google.com               |
| www.facebook.com         |
| http://stackoverflow.com |

Output Table:
| Input                    | Output            |
|:-------------------------|:------------------|
| http://google.com        | google.com        |
| www.google.com           | google.com        |
| stackoverflow.com        | stackoverflow.com |
| google.com               | google.com        |
| www.facebook.com         | facebook.com      |
| http://stackoverflow.com | stackoverflow.com |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/5.txt
instruction: //http://stackoverflow.com/questions/13969214/how-to-strip-http-and-www-from-an-entered-domain-name-using-c-sharp
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: 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        |
|:-------------|
| 390-9292     |
| 499-999-2020 |
| 425-9990200  |

Output Table:
| Input        | Output       |
|:-------------|:-------------|
| 390-9292     | 425-390-9292 |
| 499-999-2020 | 499-999-2020 |
| 425-9990200  | 425-999-0200 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/45.txt
instruction: //phne number, default 425
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: 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          |
|:---------------|
| 131.107.160.83 |
| 103.235.46.39  |
| 77.67.27.8     |

Output Table:
| Input          | Output        |
|:---------------|:--------------|
| 131.107.160.83 | United States |
| 103.235.46.39  | Hong Kong     |
| 77.67.27.8     | Germany       |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/41.txt
instruction: //ip address to country
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: 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                                                              |
|:-------------------------------------------------------------------|
| <sample2 xns:id="d3450e86-7264-4512-9891-6c7183257741">b</sample2> |
| <sample6 xns:id="test">b</sample6>                                 |
| <sample4 xns:id="abc">xyz</sample3>                                |

Output Table:
| Input                                                              | Output                 |
|:-------------------------------------------------------------------|:-----------------------|
| <sample2 xns:id="d3450e86-7264-4512-9891-6c7183257741">b</sample2> | <sample2>b</sample2>   |
| <sample6 xns:id="test">b</sample6>                                 | <sample6>b</sample6>   |
| <sample4 xns:id="abc">xyz</sample3>                                | <sample3>xyz</sample3> |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/26.txt
instruction: //Filter out XML attributeshttp://stackoverflow.com/questions/7122109/codemirror-2-how-to-filter-out-xml-attributes
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: 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                     |
|:--------------------------|
| testing@invalid@email.com |
| walter@xyz.com            |
| wa@lter@xyz.com           |

Output Table:
| Input                     | Output         |
|:--------------------------|:---------------|
| testing@invalid@email.com | invalid        |
| walter@xyz.com            | walter@xyz.com |
| wa@lter@xyz.com           | invalid        |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/43.txt
instruction: //email validation
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: 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 |
|-------------:|
| 123412341234 |
|            1 |
|      1111222 |

Output Table:
|        Input | Output         |
|-------------:|:---------------|
| 123412341234 | 1234,1234,1234 |
|            1 | 1              |
|      1111222 | 1111,222       |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/39.txt
instruction: //Splitting string into sizes of 4
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: 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    |
|:---------|
| 999-2020 |
| 321-2020 |
| 390-9292 |

Output Table:
| Input    | Output       |
|:---------|:-------------|
| 999-2020 | 425-999-2020 |
| 321-2020 | 425-321-2020 |
| 390-9292 | 425-390-9292 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/44.txt
instruction: //phone number, default 425
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: 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            |
|:-----------------|
| www.facebook.com |
| www.abc.com      |
| www.sina.cn      |

Output Table:
| Input            | Output   |
|:-----------------|:---------|
| www.facebook.com | facebook |
| www.abc.com      | abc      |
| www.sina.cn      | sina     |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/6.txt
instruction: //Extract something between the first dot and the last dot, not so good an example, don't know what it tries to do
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: 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          |
|:---------------|
| Charles Wooten |
| Rachel Green   |
| Kim Lee        |

Output Table:
| Input          | Output    |
|:---------------|:----------|
| Charles Wooten | C. Wooten |
| Rachel Green   | R. Green  |
| Kim Lee        | K. Lee    |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/38.txt
instruction: //person name
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: 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 |
|--------:|
|      35 |
|      11 |
|      10 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|      35 |       55 |
|      11 |       31 |
|      10 |       30 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/49.txt
instruction: // plus 20
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: 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      |
|:-----------|
| 2001/12/12 |
| 2012/4/11  |
| 2014/3/2   |

Output Table:
| Input      | Output     |
|:-----------|:-----------|
| 2001/12/12 | 12/12/2001 |
| 2012/4/11  | 04/11/2012 |
| 2014/3/2   | 03/02/2014 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/48.txt
instruction: //convert y-m-d to m/d/y, add 0 if neccesary
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: 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                          |
|:-------------------------------|
| {{x, y}, {z, zz}}              |
| {{p, q}, {r, s, t}, {u, v, w}} |

Output Table:
| Input                          | Output   |
|:-------------------------------|:---------|
| {{x, y}, {z, zz}}              | z        |
| {{p, q}, {r, s, t}, {u, v, w}} | r        |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/46.txt
instruction: //first element in the second set
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: 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                                         |
|:----------------------------------------------|
| http://www.beta.microsoft.com/path/page.htm   |
| http://www.microsoft.com/                     |
| http://www.beta.microsoft.co.uk/path/page.htm |

Output Table:
| Input                                         | Output          |
|:----------------------------------------------|:----------------|
| http://www.beta.microsoft.com/path/page.htm   | microsoft.com   |
| http://www.microsoft.com/                     | microsoft.com   |
| http://www.beta.microsoft.co.uk/path/page.htm | microsoft.co.uk |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/7.txt
instruction: //Get a domain name from a URL
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: 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         |
|:--------------|
| 12,13,14,12,5 |
| 1,100,2,50    |
| 12,12         |

Output Table:
| Input         |   Output |
|:--------------|---------:|
| 12,13,14,12,5 |    3.564 |
| 1,100,2,50    |   47.091 |
| 12,12         |    0     |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/40.txt
instruction: //standard deviation calculation
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv