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          |
|:---------------|
| Aysu Polat     |
| Cecep Sutresna |
| Fatma Yilmaz   |

Output Table:
| Input          | Output          |
|:---------------|:----------------|
| Aysu Polat     | Polat, Aysu     |
| Cecep Sutresna | Sutresna, Cecep |
| Fatma Yilmaz   | Yilmaz, Fatma   |



filename: Name.000029
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     |
|:----------|
| 2,Fatma   |
| 4,Anna    |
| 4,Dilorom |

Output Table:
| Input     | Output   |
|:----------|:---------|
| 2,Fatma   | Fatma    |
| 4,Anna    | Anna     |
| 4,Dilorom | Dilorom  |



filename: Name.000032
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              |
|:-------------------|
| Ojerinde Adeniyi   |
| Aysu Polat         |
| Milica Zujovic     |
| Hone Albert        |
| Snezhana Bozhilova |

Output Table:
| Input              | Output   |
|:-------------------|:---------|
| Ojerinde Adeniyi   | o.a.     |
| Aysu Polat         | a.p.     |
| Milica Zujovic     | m.z.     |
| Hone Albert        | h.a.     |
| Snezhana Bozhilova | s.b.     |



filename: Name.000024
instruction: Casing
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                           |
|:--------------------------------|
| Dr. A. K. Haf Shchur II         |
| Dr. B. R. Mekdes Konecny-Mahile |

Output Table:
| Input                           | Output   |
|:--------------------------------|:---------|
| Dr. A. K. Haf Shchur II         | Shchur   |
| Dr. B. R. Mekdes Konecny-Mahile | Mahile   |



filename: Name.000036
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          |
|:---------------|
| Aysu Polat     |
| Cecep Sutresna |
| Fatma Yilmaz   |

Output Table:
| Input          | Output   |
|:---------------|:---------|
| Aysu Polat     | aysu.P@  |
| Cecep Sutresna | cecep.S@ |
| Fatma Yilmaz   | fatma.Y@ |



filename: Name.000039
instruction: Casing
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                      |
|:---------------------------|
| Hone T. Albert             |
| Robert??QfjgL7EjQJ Har-Paz |
| Yaron??eM Rees             |
| Kiriakos O. Sarlis         |
| Igor F. Konecny            |
| Nikolajs Serdar Pirc       |
| Maša??SYgTSh4 Buzov        |
| Snorre??cRG5QHflCs Evans   |
| Mukhtar J. Sarraf          |
| Shavkat F. Rustamov        |

Output Table:
| Input                      | Output   |
|:---------------------------|:---------|
| Hone T. Albert             | H A      |
| Robert??QfjgL7EjQJ Har-Paz | R P      |
| Yaron??eM Rees             | Y R      |
| Kiriakos O. Sarlis         | K S      |
| Igor F. Konecny            | I K      |
| Nikolajs Serdar Pirc       | N P      |
| Maša??SYgTSh4 Buzov        | M B      |
| Snorre??cRG5QHflCs Evans   | S E      |
| Mukhtar J. Sarraf          | M S      |
| Shavkat F. Rustamov        | S R      |



filename: Name.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 |
|----------:|
|  29721    |
|   3489.7  |
|  -1961.12 |
|  19039.5  |
| -28178    |

Output Table:
|     Input |    Output |
|----------:|----------:|
|  29721    |  297.21   |
|   3489.7  |   34.897  |
|  -1961.12 |  -19.6112 |
|  19039.5  |  190.395  |
| -28178    | -281.78   |



filename: Number.000008
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 |
|------------:|
| 55555555555 |
|      062957 |
|      304420 |
| 87525949224 |
| 88779256328 |
| 47641887866 |
| 80279935255 |
| 59650142967 |
| 49892181857 |
| 14403829059 |

Output Table:
|       Input |   Output |
|------------:|---------:|
| 55555555555 |     5555 |
|      062957 |     2957 |
|      304420 |     4420 |
| 87525949224 |     9224 |
| 88779256328 |     6328 |
| 47641887866 |     7866 |
| 80279935255 |     5255 |
| 59650142967 |     2967 |
| 49892181857 |     1857 |
| 14403829059 |     9059 |



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

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

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

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

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

Input Table:
|   Input |
|--------:|
|    0.28 |
|    0.02 |
|    0.41 |
|    0.16 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    0.28 |     0.3  |
|    0.02 |     0    |
|    0.41 |     0.4  |
|    0.16 |     0.15 |



filename: Number.000019
instruction: Numeric
NumericRounding
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            |
|:-----------------|
| Kiriakos Yilmaz  |
| Lisandro Zujovic |

Output Table:
| Input            | Output           |
|:-----------------|:-----------------|
| Kiriakos Yilmaz  | KIRIAKOS YILMAZ  |
| Lisandro Zujovic | LISANDRO ZUJOVIC |



filename: Name.000041
instruction: Casing
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 |
|--------:|
|   323.1 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|   323.1 |      323 |



filename: Number.000024
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                     |
|:--------------------------|
| Mrs. Filipe Oliveira, Jr. |
| Mrs. Crina Codreanu       |
| Maibritt Andreasen, Jr.   |
| Dr. Manjunath Naik, II    |
| Marina Robledo, Sr.       |
| Marcela Kubatova          |
| Mrs. Preben Bjerklund, II |
| Dr. Aswini Ragrez, II     |
| Mrs. Ulf Vestre, II       |
| Mrs. Mayang Damayanti, II |

Output Table:
| Input                     | Output   |
|:--------------------------|:---------|
| Mrs. Filipe Oliveira, Jr. | Jr.      |
| Mrs. Crina Codreanu       | NULL     |
| Maibritt Andreasen, Jr.   | Jr.      |
| Dr. Manjunath Naik, II    | II       |
| Marina Robledo, Sr.       | Sr.      |
| Marcela Kubatova          | NULL     |
| Mrs. Preben Bjerklund, II | II       |
| Dr. Aswini Ragrez, II     | II       |
| Mrs. Ulf Vestre, II       | II       |
| Mrs. Mayang Damayanti, II | II       |



filename: Name.000037
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 |
|--------:|
|    1652 |
|    1549 |
|     961 |
|    1160 |
|    1991 |
|     987 |
|     161 |
|     940 |
|     823 |
|    1978 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|    1652 | 1,652    |
|    1549 | 1,549    |
|     961 | 961      |
|    1160 | 1,160    |
|    1991 | 1,991    |
|     987 | 987      |
|     161 | 161      |
|     940 | 940      |
|     823 | 823      |
|    1978 | 1,978    |



filename: Number.000009
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 |
|--------:|
|    1394 |
|     913 |
|     814 |
|    1199 |
|     333 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    1394 |     1395 |
|     913 |      915 |
|     814 |      815 |
|    1199 |     1200 |
|     333 |      335 |



filename: Number.000016
instruction: Numeric
NumericRounding
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 |
|----------:|
|  -2032.94 |
| -16448.2  |
| -10324.6  |
|  12373.1  |

Output Table:
|     Input |   Output |
|----------:|---------:|
|  -2032.94 |  -2032.9 |
| -16448.2  | -16448.2 |
| -10324.6  | -10324.6 |
|  12373.1  |  12373.1 |



filename: Number.000012
instruction: Conditional
Numeric
NumericRounding
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 |
|--------:|
|    30   |
|     6.1 |
|    21   |
|    20   |
|    23   |
|     8   |
|     7   |
|    13.9 |
|    10   |
|    18   |
|     5.2 |
|     5.9 |
|    15   |
|    34   |
|    12.6 |
|    11   |
|    27   |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    30   |       30 |
|     6.1 |        6 |
|    21   |       21 |
|    20   |       20 |
|    23   |       23 |
|     8   |        8 |
|     7   |        7 |
|    13.9 |       13 |
|    10   |       10 |
|    18   |       18 |
|     5.2 |        5 |
|     5.9 |        5 |
|    15   |       15 |
|    34   |       34 |
|    12.6 |       12 |
|    11   |       11 |
|    27   |       27 |



filename: Number.000010
instruction: Numeric
NumericRounding
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   |
|:--------|
| 20;55   |
| 46;98   |
| 44;97   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 20;55   | 20-30    |
| 46;98   | 40-50    |
| 44;97   | 40-50    |



filename: Number.000013
instruction: Numeric
NumericRange
NumericRounding
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   |
|:--------|
| 20;55   |
| 46;98   |
| 44;97   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 20;55   | 50-60    |
| 46;98   | 90-100   |
| 44;97   | 90-100   |



filename: Number.000014
instruction: Numeric
NumericRange
NumericRounding
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 |
|--------:|
|   243   |
|    12.5 |
| 23224.1 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|   243   |    243   |
|    12.5 |     12.5 |
| 23224.1 |  23224.1 |



filename: Number.000025
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 |
|--------:|
|     440 |
|     137 |
|    1290 |
|    1040 |
|    1070 |
|     910 |
|      88 |
|      72 |
|     710 |
|     150 |
|     148 |
|     750 |
|     390 |
|      96 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     440 |      440 |
|     137 |      140 |
|    1290 |     1290 |
|    1040 |     1040 |
|    1070 |     1070 |
|     910 |      910 |
|      88 |       90 |
|      72 |       70 |
|     710 |      710 |
|     150 |      150 |
|     148 |      150 |
|     750 |      750 |
|     390 |      390 |
|      96 |      100 |



filename: Number.000011
instruction: Numeric
NumericRounding
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 |
|--------:|
|     112 |
|     117 |
|   23224 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     112 |      110 |
|     117 |      120 |
|   23224 |    23220 |



filename: Number.000029
instruction: Numeric
NumericRounding
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 |
|----------:|
| 000817680 |
| 000205588 |

Output Table:
|     Input | Output      |
|----------:|:------------|
| 000817680 | 000-81-7680 |
| 000205588 | 000-20-5588 |



filename: Number.000023
instruction: Concatenation
Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
|   Input |
|--------:|
|    1.14 |
|    1.45 |
|  102.11 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    1.14 |      1   |
|    1.45 |      1.5 |
|  102.11 |    102   |



filename: Number.000031
instruction: Numeric
NumericRounding
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      |
|:-----------|
| 2004/07/17 |
| 2006/07/09 |
| 2003/11/16 |
| 1999/11/08 |
| 2006/06/20 |
| 1996/07/25 |
| 2001/08/31 |
| 2007/03/17 |
| 2001/09/18 |
| 1996/08/22 |
| 2008/08/05 |

Output Table:
| Input      | Output     |
|:-----------|:-----------|
| 2004/07/17 | 17-7-2004  |
| 2006/07/09 | 9-7-2006   |
| 2003/11/16 | 16-11-2003 |
| 1999/11/08 | 8-11-1999  |
| 2006/06/20 | 20-6-2006  |
| 1996/07/25 | 25-7-1996  |
| 2001/08/31 | 31-8-2001  |
| 2007/03/17 | 17-3-2007  |
| 2001/09/18 | 18-9-2001  |
| 1996/08/22 | 22-8-1996  |
| 2008/08/05 | 5-8-2008   |



filename: Number.000041
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           |
|:----------------|
| Kiriakos Yilmaz |
| Radmilo Traver  |
| Artem Nabiyeva  |

Output Table:
| Input           | Output    |
|:----------------|:----------|
| Kiriakos Yilmaz | kiriakos@ |
| Radmilo Traver  | radmilo@  |
| Artem Nabiyeva  | artem@    |



filename: Name.000038
instruction: Casing
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    |
|:---------|
| $243     |
| $12.5    |
| $23224.1 |

Output Table:
| Input    |   Output |
|:---------|---------:|
| $243     |    243   |
| $12.5    |     12.5 |
| $23224.1 |  23224.1 |



filename: Number.000026
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 |
|--------:|
|   5.23  |
| 325.213 |
| 102     |

Output Table:
|   Input |   Output |
|--------:|---------:|
|   5.23  |    5.23  |
| 325.213 |  325.213 |
| 102     |  102     |



filename: Number.000039
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 |
|---------:|
|  2480.77 |
| 24572.6  |
|  1215.3  |

Output Table:
|    Input |   Output |
|---------:|---------:|
|  2480.77 |  24.8077 |
| 24572.6  | 245.726  |
|  1215.3  |  12.153  |



filename: Number.000020
instruction: Numeric
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv