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                      |
|:---------------------------|
| Wednesday, 2nd March, 1980 |
| Sunday, 3rd March, 2013    |
| Monday, 5th May, 1999      |

Output Table:
| Input                      | Output   |
|:---------------------------|:---------|
| Wednesday, 2nd March, 1980 | March    |
| Sunday, 3rd March, 2013    | March    |
| Monday, 5th May, 1999      | May      |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/16.txt
instruction: //Extract month from datetime
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: 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                                                                                                   |
|:--------------------------------------------------------------------------------------------------------|
| Mozilla/5.0 (Windows; U; MSIE 9.0; WIndows NT 9.0; en-US))                                              |
| Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10; rv:33.0) Gecko/20100101 Firefox/33.0                      |
| Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36 |

Output Table:
| Input                                                                                                   | Output    |
|:--------------------------------------------------------------------------------------------------------|:----------|
| Mozilla/5.0 (Windows; U; MSIE 9.0; WIndows NT 9.0; en-US))                                              | Windows   |
| Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10; rv:33.0) Gecko/20100101 Firefox/33.0                      | Macintosh |
| Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2227.0 Safari/537.36 | X11       |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_user_agent.txt
instruction: //http://www.useragentstring.com/index.php
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   |
|:--------|
| $10     |
| $3      |
| $55     |

Output Table:
| Input   | Output    |
|:--------|:----------|
| $10     | CAD$12.98 |
| $3      | CAD$3.89  |
| $55     | CAD$71.39 |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/numeric_currency.txt
instruction: //
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            |
|:-----------------|
| good_spirit      |
| a_bc_de_fg_hi_jk |
| a_string_var     |

Output Table:
| Input            | Output      |
|:-----------------|:------------|
| good_spirit      | goodSpirit  |
| a_bc_de_fg_hi_jk | aBcDeFgHiJk |
| a_string_var     | aStringVar  |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/string_camelize_transformation.txt
instruction: //camelize 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                                                |
|:-----------------------------------------------------|
| 1201 super commerce blvd Ste H, Richmond, VA, 23452  |
| 100 turing way, Redmond, wa, 98052                   |
| 100 sunset blvd, room 301, Beverley Hills California |

Output Table:
| Input                                                | Output         |
|:-----------------------------------------------------|:---------------|
| 1201 super commerce blvd Ste H, Richmond, VA, 23452  | Richmond       |
| 100 turing way, Redmond, wa, 98052                   | Redmond        |
| 100 sunset blvd, room 301, Beverley Hills California | Beverley Hills |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_comp_address_city.txt
instruction: //address
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              |
|:-------------------|
| photos.twitter.com |
| cnbc.com           |
| cnn.com.au         |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| photos.twitter.com | com      |
| cnbc.com           | com      |
| cnn.com.au         | com.au   |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_domain_tld.txt
instruction: //
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 |
|--------:|
|     144 |
|      99 |
|   12345 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     144 |    00144 |
|      99 |    00099 |
|   12345 |    12345 |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/numeric_padding.txt
instruction: //
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 |
|---------:|
|  0.00367 |
| 11       |
| 22.3     |

Output Table:
|    Input |   Output |
|---------:|---------:|
|  0.00367 |  0.00367 |
| 11       | 11       |
| 22.3     | 22.3     |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/1.txt
instruction: //http://stackoverflow.com/questions/15072552/use-scientific-notation-only-if-needed
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                             |
|:----------------------------------|
| this is well                      |
| Test Is Good                      |
| In my backpack, There is a monkey |

Output Table:
| Input                             | Output                            |
|:----------------------------------|:----------------------------------|
| this is well                      | THIS IS WELL                      |
| Test Is Good                      | TEST IS GOOD                      |
| In my backpack, There is a monkey | IN MY BACKPACK, THERE IS A MONKEY |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/string_uppercaseing_transformation.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   |
|:--------|
| 2134    |
| 1234    |
| 1234.123	1,234.123         |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 2134    | 2,134    |
| 1234    | 1,234    |
| 1234.123	1,234.123         |          |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/11.txt
instruction: //Add , to number
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         |
|:--------------|
| fe80::/10     |
| 2001:db8::/48 |

Output Table:
| Input         | Output                                  |
|:--------------|:----------------------------------------|
| fe80::/10     | febf:ffff:ffff:ffff:ffff:ffff:ffff:ffff |
| 2001:db8::/48 | 2001:db8:0:ffff:ffff:ffff:ffff:ffff     |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_iprange_end.txt
instruction: // https://en.wikipedia.org/wiki/Classless_Inter-Domain_Routing
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                   |
|:------------------------|
| Pb                      |
| H                       |
| Pb(H2)2                 |
| PbH2                    |
| H2                      |
| Na2(CH3(CH2)2CH3)2      |
| (Tc(H2O)3(Fe3(SO4)2)2)2 |
| Tc(H2O)3(Fe3(SO4)2)2    |
| (Ag(Pb(H2)2)2)2SO4      |

Output Table:
| Input                   | Output                 |
|:------------------------|:-----------------------|
| Pb                      | Pb                     |
| H                       | H                      |
| Pb(H2)2                 | Pb 4H                  |
| PbH2                    | Pb 2H                  |
| H2                      | 2H                     |
| Na2(CH3(CH2)2CH3)2      | 2Na 2C 6H 4C 8H 2C 6H  |
| (Tc(H2O)3(Fe3(SO4)2)2)2 | 2Tc 12H 6O 12Fe 8S 32O |
| Tc(H2O)3(Fe3(SO4)2)2    | Tc 6H 3O 6Fe 4S 16O    |
| (Ag(Pb(H2)2)2)2SO4      | 2Ag 4Pb 16H S 4O       |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/29.txt
instruction: //Chemistry: Parsing a chemical formula
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 |
|--------:|
| 12334.1 |
| 12334.1 |

Output Table:
|   Input | Output    |
|--------:|:----------|
| 12334.1 | 12,334.13 |
| 12334.1 | 12,334.13 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/13.txt
instruction: //Number formatting, use , before . , and keep two digits after .
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 |
|--------:|
|   10183 |
|      90 |
| 1000001 |
|    5888 |

Output Table:
|   Input | Output                                   |
|--------:|:-----------------------------------------|
|   10183 | ten thousand one hundred eighty three    |
|      90 | ninety                                   |
| 1000001 | one million one                          |
|    5888 | five thousand eight hundred eighty eight |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/31.txt
instruction: //number to words  http://stackoverflow.com/questions/3911966/how-to-convert-number-to-words-in-java
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                     |
|:--------------------------|
| What is wrong             |
| Test is good for everyone |
| Wait for a while          |

Output Table:
| Input                     | Output                |
|:--------------------------|:----------------------|
| What is wrong             | WhatIsWrong           |
| Test is good for everyone | TestIsGoodForEveryone |
| Wait for a while          | WaitForAWhile         |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/18.txt
instruction: //Concateneate words with Camel Case
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 |
|       5 |
|      12 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|       3 |       03 |
|       5 |       05 |
|      12 |       12 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/14.txt
instruction: //Pad numbers to the left
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       |
| 42      |
| 21 asg  |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 1       |        1 |
| 42      |       42 |
| 21 asg  |       21 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/15.txt
instruction: //http://stackoverflow.com/questions/1561273/parse-an-integer-from-a-string-with-trailing-garbage
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                     |
|:--------------------------|
| DoesNotMatterHowManyWords |
| TestIsGood                |
| WhatIf                    |

Output Table:
| Input                     | Output                         |
|:--------------------------|:-------------------------------|
| DoesNotMatterHowManyWords | Does Not Matter How Many Words |
| TestIsGood                | Test Is Good                   |
| WhatIf                    | What If                        |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/17.txt
instruction: //Leave proper space
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   |
|:--------|
| yy      |
| c       |
| zzzzz   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| yy      | yy       |
| c       | c        |
| zzzzz   | zzzzz    |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/string_padleft_transformation.txt
instruction: //pad left
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          |
|:---------------|
| (913)-444-5555 |
| 4654764768     |
| 509-791-8816   |

Output Table:
| Input          |     Output |
|:---------------|-----------:|
| (913)-444-5555 | 9134445555 |
| 4654764768     | 4654764768 |
| 509-791-8816   | 5097918816 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/22.txt
instruction: //Phone number processing
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      |
|:-----------|
| E7C3B2A1   |
| E12HY5     |
| ABCDE12345 |

Output Table:
| Input      | Output     |
|:-----------|:-----------|
| E7C3B2A1   | A1B2C3E7   |
| E12HY5     | E1H2Y5     |
| ABCDE12345 | A1B2C3D4E5 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/24.txt
instruction: //sort alphanumeric string, then alternate numeric/alphabet char output
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                                            |
|:-------------------------------------------------|
| Built-In Character Casing functions in .Net      |
| .NET method to convert a string to sentence case |
| What's the difference between String and string  |

Output Table:
| Input                                            | Output                                          |
|:-------------------------------------------------|:------------------------------------------------|
| Built-In Character Casing functions in .Net      | built-in-character-casing-functions-in-net      |
| .NET method to convert a string to sentence case | net-method-to-convert-a-string-to-sentence-case |
| What's the difference between String and string  | whats-the-difference-between-string-and-string  |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/21.txt
instruction: //http://stackoverflow.com/questions/25259/how-does-stack-overflow-generate-its-seo-friendly-urls
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                  |
|:-----------------------|
| How are    you         |
| How   fun              |
| What   is the   matter |

Output Table:
| Input                  | Output             |
|:-----------------------|:-------------------|
| How are    you         | How are you        |
| How   fun              | How fun            |
| What   is the   matter | What is the matter |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/20.txt
instruction: //replace multiple spaces with a single space
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     |
|:----------|
| December  |
| October   |
| January   |
| September |
| June      |
| March     |
| February  |

Output Table:
| Input     |   Output |
|:----------|---------:|
| December  |       12 |
| October   |       10 |
| January   |        1 |
| September |        9 |
| June      |        6 |
| March     |        3 |
| February  |        2 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/25.txt
instruction: //month to number
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 |
|--------:|
|      45 |
|      60 |
|      50 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|      45 |        7 |
|      60 |       16 |
|      50 |       10 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/27.txt
instruction: //F -> C, need calculation + 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 |
|-----------:|
| 9134445555 |
| 4654764768 |
| 5097918816 |

Output Table:
|      Input | Output       |
|-----------:|:-------------|
| 9134445555 | 913-444-5555 |
| 4654764768 | 465-476-4768 |
| 5097918816 | 509-791-8816 |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/23.txt
instruction: //Phone number processing
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    |
|:---------|
| KLKLHHMO |
| 9090     |
| XUCHU    |

Output Table:
| Input    | Output   |
|:---------|:---------|
| KLKLHHMO | KLHMO    |
| 9090     | 90       |
| XUCHU    | XUCH     |



filename: Transform-Data-by-Example/Benchmark/benchmark-stackoverflow/4.txt
instruction: //Get Distinct chars of a string
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv