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

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| 144-038-2905 x9062 |
| 317-901-9724 x2152 |
| 578-240-2436       |
| 052-360-2625       |
| 418-463-3296       |
| 298-506-0529       |
| 729-589-2431       |
| 343-466-9926       |
| 367-458-4948 x7295 |
| 984-374-6960 x9851 |
| 655-693-0442       |
| 786-628-8081 x8294 |
| 086-266-7905 x2010 |
| 941-652-6038       |
| 712-973-4124 x6418 |
| 488-779-2563       |
| 918-486-9931 x0673 |
| 917-630-7178       |
| 967-476-7158       |
| 575-390-6114 x2327 |
| 799-352-5502       |
| 656-300-8786       |
| 885-929-8848       |
| 608-738-9515       |
| 111-573-7405       |

Output Table:
| Input              |   Output |
|:-------------------|---------:|
| 144-038-2905 x9062 |      144 |
| 317-901-9724 x2152 |      317 |
| 578-240-2436       |      578 |
| 052-360-2625       |      052 |
| 418-463-3296       |      418 |
| 298-506-0529       |      298 |
| 729-589-2431       |      729 |
| 343-466-9926       |      343 |
| 367-458-4948 x7295 |      367 |
| 984-374-6960 x9851 |      984 |
| 655-693-0442       |      655 |
| 786-628-8081 x8294 |      786 |
| 086-266-7905 x2010 |      086 |
| 941-652-6038       |      941 |
| 712-973-4124 x6418 |      712 |
| 488-779-2563       |      488 |
| 918-486-9931 x0673 |      918 |
| 917-630-7178       |      917 |
| 967-476-7158       |      967 |
| 575-390-6114 x2327 |      575 |
| 799-352-5502       |      799 |
| 656-300-8786       |      656 |
| 885-929-8848       |      885 |
| 608-738-9515       |      608 |
| 111-573-7405       |      111 |



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

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

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

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

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

Input Table:
| Input      |
|:-----------|
| 9:05 12/5  |
| 22:48 8/16 |
| 9:30 5/14  |
| 7:30 4/18  |

Output Table:
| Input      | Output      |
|:-----------|:------------|
| 9:05 12/5  | 09 05 12 05 |
| 22:48 8/16 | 22 48 08 16 |
| 9:30 5/14  | 09 30 05 14 |
| 7:30 4/18  | 07 30 04 18 |



filename: Number.000059
instruction: DateTime
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input        |
|:-------------|
| 875-259-4922 |
| 906-295-7349 |
| 556-930-4420 |
| 649-089-6388 |
| 792-563-2821 |
| 878-662-8808 |
| 802-799-3525 |
| 670-339-5712 |
| 921-818-5753 |
| 144-038-2905 |

Output Table:
| Input        | Output       |
|:-------------|:-------------|
| 875-259-4922 | 875 259 4922 |
| 906-295-7349 | 906 295 7349 |
| 556-930-4420 | 556 930 4420 |
| 649-089-6388 | 649 089 6388 |
| 792-563-2821 | 792 563 2821 |
| 878-662-8808 | 878 662 8808 |
| 802-799-3525 | 802 799 3525 |
| 670-339-5712 | 670 339 5712 |
| 921-818-5753 | 921 818 5753 |
| 144-038-2905 | 144 038 2905 |



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

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

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

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

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

Input Table:
| Input        |
|:-------------|
| 875-259-4922 |
| 649-089-6388 |
| 878-662-8808 |

Output Table:
| Input        | Output       |
|:-------------|:-------------|
| 875-259-4922 | 875 259 4922 |
| 649-089-6388 | 649 089 6388 |
| 878-662-8808 | 878 662 8808 |



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

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| 875-259-4922       |
| 846-332-9609 x917  |
| 753-906-1145 x2327 |
| 649-089-6388 x9065 |
| 694-165-2603 x802  |
| 829-467-8177 x1914 |
| 116-295-0285 x6590 |
| 395-712-9734 x1247 |
| 101-718-6528 x849  |
| 656-300-8786 x224  |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| 875-259-4922       | 259-4922 |
| 846-332-9609 x917  | 332-9609 |
| 753-906-1145 x2327 | 906-1145 |
| 649-089-6388 x9065 | 089-6388 |
| 694-165-2603 x802  | 165-2603 |
| 829-467-8177 x1914 | 467-8177 |
| 116-295-0285 x6590 | 295-0285 |
| 395-712-9734 x1247 | 712-9734 |
| 101-718-6528 x849  | 718-6528 |
| 656-300-8786 x224  | 300-8786 |



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

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

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

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

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

Input Table:
|           Input |
|----------------:|
|    23.54        |
|    12.31        |
| 34534.2         |
|     3.63456e+07 |
|     3.21452e+06 |

Output Table:
|           Input | Output       |
|----------------:|:-------------|
|    23.54        | ₹24          |
|    12.31        | ₹12          |
| 34534.2         | ₹34,534      |
|     3.63456e+07 | ₹3,63,45,645 |
|     3.21452e+06 | ₹32,14,524   |



filename: Number.000094
instruction: Concatenation
Numeric
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| +31.49.279.89.63   |
| +49.96.70.33.95.71 |

Output Table:
| Input              | Output      |
|:-------------------|:------------|
| +31.49.279.89.63   | Netherlands |
| +49.96.70.33.95.71 | Germany     |



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

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

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

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

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

Input Table:
| Input          |
|:---------------|
| (525) 949 2246 |
| 896-388-9065   |
| 880-816-8294   |
| 014.2967       |

Output Table:
| Input          | Output       |
|:---------------|:-------------|
| (525) 949 2246 | 525-949-2246 |
| 896-388-9065   | 896-388-9065 |
| 880-816-8294   | 880-816-8294 |
| 014.2967       | 425-014-2967 |



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

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

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

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

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

Input Table:
| Input                        |
|:-----------------------------|
| eaque placeat ullam ut       |
| error delectus expedita odit |

Output Table:
| Input                        | Output   |
|:-----------------------------|:---------|
| eaque placeat ullam ut       | placeat  |
| error delectus expedita odit | delectus |



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

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

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

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

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

Input Table:
| Input          |
|:---------------|
| 279-935-2550   |
| 169-416-5260   |
| (525) 949 2246 |
| 669.2909       |
| 880-816-8294   |
| 014.2967       |

Output Table:
| Input          | Output       |
|:---------------|:-------------|
| 279-935-2550   | 425-935-2550 |
| 169-416-5260   | 425-416-5260 |
| (525) 949 2246 | 525-949-2246 |
| 669.2909       | 425-669-2909 |
| 880-816-8294   | 425-816-8294 |
| 014.2967       | 425-014-2967 |



filename: Phone.000012
instruction: Concatenation
Conditional
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input          |
|:---------------|
| 896-388-9065   |
| 887-866-2880   |
| (014) 296 7033 |

Output Table:
| Input          | Output       |
|:---------------|:-------------|
| 896-388-9065   | 896-388-9065 |
| 887-866-2880   | 887-866-2880 |
| (014) 296 7033 | 014-296-7033 |



filename: Phone.000013
instruction: Concatenation
Conditional
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
|   Input |
|--------:|
| 525.949 |
| 490.896 |
| 418.879 |

Output Table:
|   Input | Output       |
|--------:|:-------------|
| 525.949 | 425-525-9492 |
| 490.896 | 425-490-8963 |
| 418.879 | 425-418-8786 |



filename: Phone.000014
instruction: Concatenation
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| 144-038-2905 x9062 |
| 317-901-9724 x2152 |
| 578-240-2436       |
| 052-360-2625       |
| 418-463-3296       |
| 298-506-0529       |
| 729-589-2431       |
| 343-466-9926       |
| 367-458-4948 x7295 |
| 984-374-6960 x9851 |
| 655-693-0442       |
| 786-628-8081 x8294 |
| 086-266-7905 x2010 |
| 941-652-6038       |
| 712-973-4124 x6418 |
| 488-779-2563       |
| 918-486-9931 x0673 |
| 917-630-7178       |
| 967-476-7158       |
| 575-390-6114 x2327 |
| 799-352-5502       |
| 656-300-8786       |
| 885-929-8848       |
| 608-738-9515       |
| 111-573-7405       |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| 144-038-2905 x9062 | x9062    |
| 317-901-9724 x2152 | x2152    |
| 578-240-2436       | NULL     |
| 052-360-2625       | NULL     |
| 418-463-3296       | NULL     |
| 298-506-0529       | NULL     |
| 729-589-2431       | NULL     |
| 343-466-9926       | NULL     |
| 367-458-4948 x7295 | x7295    |
| 984-374-6960 x9851 | x9851    |
| 655-693-0442       | NULL     |
| 786-628-8081 x8294 | x8294    |
| 086-266-7905 x2010 | x2010    |
| 941-652-6038       | NULL     |
| 712-973-4124 x6418 | x6418    |
| 488-779-2563       | NULL     |
| 918-486-9931 x0673 | x0673    |
| 917-630-7178       | NULL     |
| 967-476-7158       | NULL     |
| 575-390-6114 x2327 | x2327    |
| 799-352-5502       | NULL     |
| 656-300-8786       | NULL     |
| 885-929-8848       | NULL     |
| 608-738-9515       | NULL     |
| 111-573-7405       | NULL     |



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

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

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

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

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

Input Table:
|     Input |
|----------:|
| 464908963 |
| 712973412 |
| 890659650 |

Output Table:
|     Input | Output   |
|----------:|:---------|
| 464908963 | 0x46     |
| 712973412 | 0x71     |
| 890659650 | 0x89     |



filename: Phone.000016
instruction: Concatenation
Numeric
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| 875-259-4922 x4649 |
| 101-718-6528 x849  |
| 656-300-8786 x224  |
| 896-388-9065 x965  |
| 260-380-2799 x3525 |
| 781-771-9145 x3648 |
| 186-556-9304 x4202 |
| 973-412-4764 x188  |
| 437-469-6069 x8510 |
| 846-332-9609 x917  |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| 875-259-4922 x4649 | x4649    |
| 101-718-6528 x849  | x849     |
| 656-300-8786 x224  | x224     |
| 896-388-9065 x965  | x965     |
| 260-380-2799 x3525 | x3525    |
| 781-771-9145 x3648 | x3648    |
| 186-556-9304 x4202 | x4202    |
| 973-412-4764 x188  | x188     |
| 437-469-6069 x8510 | x8510    |
| 846-332-9609 x917  | x917     |



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

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

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

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

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

Input Table:
| Input                        |
|:-----------------------------|
| QUI VOLUPTATEM VENIAM 6.2 PK |
| UT NOSTRUM 4.6Z              |
| EXCEPTURI DOLOR 7Z           |

Output Table:
| Input                        | Output   |
|:-----------------------------|:---------|
| QUI VOLUPTATEM VENIAM 6.2 PK | 6.2 PK   |
| UT NOSTRUM 4.6Z              | 4.6Z     |
| EXCEPTURI DOLOR 7Z           | 7Z       |



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

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

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

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

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

Input Table:
| Input                                                                               |
|:------------------------------------------------------------------------------------|
| MOLESTIAS EST ATQUE AUT ODIO 60'S 760-549-1126 - - 220428-85 04-08-2217             |
| ET AUTEM SUSCIPIT 3 BLANDITIIS [KS] 641-887-8662 - - 808168-29 10-19-1959           |
| Sit Et Minima Delectus Officia Est 97MG XZG 181-857-5390 - - 114523-27 01-25-1769   |
| ET DIGNISSIMOS REICIENDIS AD NOSTRUM 52MG ZAW 963-889-0659 - - 501429-67 11-07-1712 |
| Laboriosam Omnis Molestiae Est 18MG QZW 510-563-8375 - - 551653-28 03-03-1948       |

Output Table:
| Input                                                                               | Output       |
|:------------------------------------------------------------------------------------|:-------------|
| MOLESTIAS EST ATQUE AUT ODIO 60'S 760-549-1126 - - 220428-85 04-08-2217             | 760-549-1126 |
| ET AUTEM SUSCIPIT 3 BLANDITIIS [KS] 641-887-8662 - - 808168-29 10-19-1959           | 641-887-8662 |
| Sit Et Minima Delectus Officia Est 97MG XZG 181-857-5390 - - 114523-27 01-25-1769   | 181-857-5390 |
| ET DIGNISSIMOS REICIENDIS AD NOSTRUM 52MG ZAW 963-889-0659 - - 501429-67 11-07-1712 | 963-889-0659 |
| Laboriosam Omnis Molestiae Est 18MG QZW 510-563-8375 - - 551653-28 03-03-1948       | 510-563-8375 |



filename: Phone.000017
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
|       Input |
|------------:|
|      0.0001 |
|     -0.1    |
|   -243      |
|      0.1    |
| -23224.1    |
|  -2345.23   |

Output Table:
|       Input |            Output |
|------------:|------------------:|
|      0.0001 |       0.1         |
|     -0.1    |    -100           |
|   -243      | -243000           |
|      0.1    |     100           |
| -23224.1    |      -2.32241e+07 |
|  -2345.23   |      -2.34523e+06 |



filename: Number.000074
instruction: Numeric
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input                    |
|:-------------------------|
| harum.totam.voluptatibus |
| 890.65.9650              |

Output Table:
| Input                    | Output   |
|:-------------------------|:---------|
| harum.totam.voluptatibus | totam    |
| 890.65.9650              | 65       |



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

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

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

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

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

Input Table:
| Input          |
|:---------------|
| (779) 256 3282 |
| 896-388-9065   |
| 880-816-8294   |
| (525) 949 2246 |
| 669-2909       |

Output Table:
| Input          | Output       |
|:---------------|:-------------|
| (779) 256 3282 | 425-256-3282 |
| 896-388-9065   | 425-388-9065 |
| 880-816-8294   | 425-816-8294 |
| (525) 949 2246 | 425-949-2246 |
| 669-2909       | 425-669-2909 |



filename: Phone.000015
instruction: Concatenation
Conditional
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input   |
|:--------|
| (8)     |
| (6)     |
| (8)     |

Output Table:
| Input   |   Output |
|:--------|---------:|
| (8)     |        8 |
| (6)     |        6 |
| (8)     |        8 |



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

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

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

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

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

Input Table:
| Input                                                                                                      |
|:-----------------------------------------------------------------------------------------------------------|
| Handmade Soft Salad & Awesome Plastic+Mouse - Licensed Granite Sausages - 586g                             |
| Incredible Concrete Keyboard Small Rubber Mouse & In+elligent Rubber Fish - Unbranded Granite Mouse - 502g |
| Intell+gent Fresh Towels Intelligent Concrete Fi?h 235 Handcrafted Metal Chair - 146Ml                     |
| 692 Practi+al Concrete Chips & 275 186, 531gm                                                              |
| 742 & Sleek Steel Ball - Handmade Frozen Chair - 515 g                                                     |

Output Table:
| Input                                                                                                      | Output   |
|:-----------------------------------------------------------------------------------------------------------|:---------|
| Handmade Soft Salad & Awesome Plastic+Mouse - Licensed Granite Sausages - 586g                             | 586g     |
| Incredible Concrete Keyboard Small Rubber Mouse & In+elligent Rubber Fish - Unbranded Granite Mouse - 502g | 502g     |
| Intell+gent Fresh Towels Intelligent Concrete Fi?h 235 Handcrafted Metal Chair - 146Ml                     | 146Ml    |
| 692 Practi+al Concrete Chips & 275 186, 531gm                                                              | 531gm    |
| 742 & Sleek Steel Ball - Handmade Frozen Chair - 515 g                                                     | 515 g    |



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

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

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

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

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

Input Table:
| Input        |
|:-------------|
| ( 	            |
| 	             |
|            |
|            |
| No Rating) |
| (6)        |
| (5)        |
| (          |
| No Rating) |
| (10)       |
| (7)        |
| (0)        |
| (2)        |
| (          |
|            |
| 	          |
| 	            |
| No Rating)   |

Output Table:
| Input        |   Output |
|:-------------|---------:|
| ( 	            |        0 |
| 	             |          |
|            |          |
|            |          |
| No Rating) |          |
| (6)        |        6 |
| (5)        |        5 |
| (          |        0 |
| No Rating) |          |
| (10)       |       10 |
| (7)        |        7 |
| (0)        |        0 |
| (2)        |        2 |
| (          |        0 |
|            |          |
| 	          |          |
| 	            |          |
| No Rating)   |          |



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

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

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

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

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

Input Table:
| Input                    |
|:-------------------------|
| 1Z AI7 S7L 39 2136 908 9 |
| 1Z 1KN 3G8 94 1261 323 9 |
| 1Z GU2 T24 89 4205 353 5 |

Output Table:
| Input                    | Output   |
|:-------------------------|:---------|
| 1Z AI7 S7L 39 2136 908 9 | AI7 2136 |
| 1Z 1KN 3G8 94 1261 323 9 | 1KN 1261 |
| 1Z GU2 T24 89 4205 353 5 | GU2 89   |



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

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

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

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

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

Input Table:
| Input                    |
|:-------------------------|
| 1Z AI7 S7L 39 2136 908 9 |
| 1Z 1KN 3G8 94 1261 323 9 |
| 1Z GU2 T24 89 4205 353 5 |

Output Table:
| Input                    | Output   |
|:-------------------------|:---------|
| 1Z AI7 S7L 39 2136 908 9 | AI7      |
| 1Z 1KN 3G8 94 1261 323 9 | 1KN      |
| 1Z GU2 T24 89 4205 353 5 | GU2      |



filename: ShippingCode.000009
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv