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 |
|--------:|
| 372.32  |
|   8.238 |
|   1.243 |
|  19.252 |

Output Table:
|   Input | Output    |
|--------:|:----------|
| 372.32  | 372.3 pts |
|   8.238 | 8.2 pts   |
|   1.243 | 1.2 pts   |
|  19.252 | 19.3 pts  |



filename: Number.000057
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 |
|--------:|
|     232 |
|     605 |
|     234 |
|     964 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     232 |      230 |
|     605 |      605 |
|     234 |      235 |
|     964 |      965 |



filename: Number.000052
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 |
|----------:|
|  30.4     |
| 200       |
|  29.3     |
|   1.32256 |

Output Table:
|     Input |    Output |
|----------:|----------:|
|  30.4     |  30.4     |
| 200       | 200       |
|  29.3     |  29.3     |
|   1.32256 |   1.32256 |



filename: Number.000056
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 |
|---------:|
|  2345.23 |
|   243    |
| 23224.1  |

Output Table:
|    Input | Output      |
|---------:|:------------|
|  2345.23 | 2.345,23292 |
|   243    | 243         |
| 23224.1  | 23.224,1    |



filename: Number.000067
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    |
|:---------|
| 11:5:25  |
| 12:8:29  |
| 3:14:23  |
| 11:23:45 |

Output Table:
| Input    | Output      |
|:---------|:------------|
| 11:5:25  | 11h 05m 25s |
| 12:8:29  | 12h 08m 29s |
| 3:14:23  | 03h 14m 23s |
| 11:23:45 | 11h 23m 45s |



filename: Number.000066
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 |
|---------:|
|   -12.5  |
|  1202.34 |
| -2345.23 |

Output Table:
|    Input |           Output |
|---------:|-----------------:|
|   -12.5  | -12500           |
|  1202.34 |      1.20234e+06 |
| -2345.23 |     -2.34523e+06 |



filename: Number.000079
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 |
|--------:|
|    +617 |
|    +125 |
|     +91 |
|      +9 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    +617 |    +0617 |
|    +125 |    +0125 |
|     +91 |    +0091 |
|      +9 |    +0009 |



filename: Number.000060
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 |
|---------:|
|   100    |
|    -0.1  |
|   243    |
|     0.1  |
| 23224.1  |
|  2345.23 |

Output Table:
|    Input | Output   |
|---------:|:---------|
|   100    | $0.1B    |
|    -0.1  | $0.0B    |
|   243    | $0.2B    |
|     0.1  | $0.0B    |
| 23224.1  | $23.2B   |
|  2345.23 | $2.3B    |



filename: Number.000075
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 |
|----------:|
| 23224.1   |
|   243.425 |
|     0.9   |
|     0.6   |
|  2345.23  |
|    12.5   |

Output Table:
|     Input |   Output |
|----------:|---------:|
| 23224.1   |  23224   |
|   243.425 |    243.5 |
|     0.9   |      1   |
|     0.6   |      0.5 |
|  2345.23  |   2345   |
|    12.5   |     12.5 |



filename: Number.000071
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     |
|:----------|
| 2,ranjita |
| 2,iuliana |

Output Table:
| Input     |   Output |
|:----------|---------:|
| 2,ranjita |        2 |
| 2,iuliana |        2 |



filename: Number.000089
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 |
|--------:|
|    -0.1 |
|    12.5 |
| 23224.1 |
|   243   |
|     0.1 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    -0.1 |        0 |
|    12.5 |       10 |
| 23224.1 |    23220 |
|   243   |      240 |
|     0.1 |        0 |



filename: Number.000068
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 |
|----------:|
|    100    |
|     -0.1  |
|   -243    |
|      0.1  |
| -23224.1  |
|  -2345.23 |

Output Table:
|     Input |    Output |
|----------:|----------:|
|    100    |   0.1     |
|     -0.1  |  -0.0001  |
|   -243    |  -0.243   |
|      0.1  |   0.0001  |
| -23224.1  | -23.2241  |
|  -2345.23 |  -2.34523 |



filename: Number.000073
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       |
|:------------|
| 23224.1     |
| 243.425112  |
| 0.9@!       |
| *%0.6       |
| =2345.23292 |
| 12.5        |

Output Table:
| Input       |   Output |
|:------------|---------:|
| 23224.1     |  23224   |
| 243.425112  |    243.5 |
| 0.9@!       |      1   |
| *%0.6       |      0.5 |
| =2345.23292 |   2345   |
| 12.5        |     12.5 |



filename: Number.000070
instruction: Conditional
Numeric
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 |
|--------:|
|       5 |
|      51 |
|      46 |
|     100 |
|      44 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|       5 | 1-25     |
|      51 | 51-75    |
|      46 | 26-50    |
|     100 | 76-100   |
|      44 | 26-50    |



filename: Number.000081
instruction: Numeric
NumericRange
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 |
|--------:|
|    -0.1 |
|    12.5 |
| 23224.1 |
|   243   |
|     0.1 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    -0.1 |        0 |
|    12.5 |       15 |
| 23224.1 |    23225 |
|   243   |      245 |
|     0.1 |        0 |



filename: Number.000069
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 |
|--------:|
|       0 |
|       0 |
|       0 |
|       0 |
|       0 |
|       0 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|       0 |        0 |
|       0 |        0 |
|       0 |        0 |
|       0 |        0 |
|       0 |        0 |
|       0 |        0 |



filename: Number.000083
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 |
|--------:|
|     117 |
|     548 |
|   11112 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     117 |      120 |
|     548 |      550 |
|   11112 |    11110 |



filename: Number.000078
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 |
|--------:|
|     117 |
|     548 |
|   11112 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     117 |      110 |
|     548 |      540 |
|   11112 |    11110 |



filename: Number.000077
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 |
|--------:|
|      82 |
|      59 |
|      12 |
|      43 |
|      17 |
|      77 |
|      85 |
|      97 |
|      49 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|      82 | 80-89    |
|      59 | 50-69    |
|      12 | 10-19    |
|      43 | 40-49    |
|      17 | 10-19    |
|      77 | 70-79    |
|      85 | 80-89    |
|      97 | 90-99    |
|      49 | 40-59    |



filename: Number.000087
instruction: Numeric
NumericRange
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         |
|:--------------|
| 886 906 (99)  |
| (24) 950 4346 |

Output Table:
| Input         |   Output |
|:--------------|---------:|
| 886 906 (99)  |       99 |
| (24) 950 4346 |       24 |



filename: Number.000091
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   |
|:--------|
| 25 8.1  |
| 26 33   |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 25 8.1  |      8.1 |
| 26 33   |     33   |



filename: Number.000092
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 |
|---------:|
| 23224.1  |
|   243.5  |
|     0.99 |
|     0.6  |
|  2345.23 |
|    12.5  |

Output Table:
|    Input |   Output |
|---------:|---------:|
| 23224.1  |  23224.1 |
|   243.5  |    243.5 |
|     0.99 |      1   |
|     0.6  |      0.6 |
|  2345.23 |   2345.2 |
|    12.5  |     12.5 |



filename: Number.000072
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        |
|:-------------|
| 8,355,928.24 |
| 7,866,672.29 |
| 5,079,944.67 |
| 6,023,450.72 |
| 9,958,123.73 |
| 5,205,832.30 |
| 1,293,626.79 |
| 4,979,600.86 |
| 9,999,999.99 |
| 9,896,894.01 |

Output Table:
| Input        | Output                    |
|:-------------|:--------------------------|
| 8,355,928.24 | 8,355,928.2 - 8,355,928.3 |
| 7,866,672.29 | 7,866,672.2 - 7,866,672.3 |
| 5,079,944.67 | 5,079,944.6 - 5,079,944.7 |
| 6,023,450.72 | 6,023,450.7 - 6,023,450.8 |
| 9,958,123.73 | 9,958,123.7 - 9,958,123.8 |
| 5,205,832.30 | 5,205,832.3 - 5,205,832.4 |
| 1,293,626.79 | 1,293,626.7 - 1,293,626.8 |
| 4,979,600.86 | 4,979,600.8 - 4,979,600.9 |
| 9,999,999.99 | 9,999,999.9 - 10,000,000  |
| 9,896,894.01 | 9,896,894 - 9,896,894.1   |



filename: Number.000085
instruction: Numeric
NumericRange
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 |
|--------:|
|    5828 |
|   44130 |
|   10029 |
|   49007 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    5828 |    05828 |
|   44130 |    44130 |
|   10029 |    10029 |
|   49007 |    49007 |



filename: Number.000093
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 |
|----------:|
| 0.558161  |
| 0.481366  |
| 0.89644   |
| 0.442178  |
| 0.762964  |
| 0.0326253 |
| 0.46      |
| 0.0303671 |
| 0.100935  |
| 0.64      |

Output Table:
|     Input |    Output |
|----------:|----------:|
| 0.558161  | 0.558161  |
| 0.481366  | 0.481366  |
| 0.89644   | 0.89644   |
| 0.442178  | 0.442178  |
| 0.762964  | 0.762964  |
| 0.0326253 | 0.0326253 |
| 0.46      | 0.46      |
| 0.0303671 | 0.0303671 |
| 0.100935  | 0.100935  |
| 0.64      | 0.64      |



filename: Number.000082
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    |
|:---------|
| 10,lilly |

Output Table:
| Input    |   Output |
|:---------|---------:|
| 10,lilly |       10 |



filename: Number.000090
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 |
|----------:|
| 875259492 |
| 044202144 |
| 295028565 |
| 464908963 |
| 453648877 |
| 764188786 |
| 169416526 |
| 429670339 |
| 382905990 |
| 051865569 |

Output Table:
|     Input | Output      |
|----------:|:------------|
| 875259492 | 875-25-9492 |
| 044202144 | 044-20-2144 |
| 295028565 | 295-02-8565 |
| 464908963 | 464-90-8963 |
| 453648877 | 453-64-8877 |
| 764188786 | 764-18-8786 |
| 169416526 | 169-41-6526 |
| 429670339 | 429-67-0339 |
| 382905990 | 382-90-5990 |
| 051865569 | 051-86-5569 |



filename: Number.000080
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 |
|----------:|
|    100    |
|     -0.1  |
|     50    |
|      0.1  |
| -23224.1  |
|  -2345.23 |

Output Table:
|     Input |   Output |
|----------:|---------:|
|    100    |      0.1 |
|     -0.1  |      0   |
|     50    |      0.1 |
|      0.1  |      0   |
| -23224.1  |    -23.2 |
|  -2345.23 |     -2.3 |



filename: Number.000076
instruction: Numeric
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv