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 |
|----------:|
|  29720.8  |
|   3489.67 |
|  -1961.12 |
|  19039.5  |
| -28178    |

Output Table:
|     Input |   Output |
|----------:|---------:|
|  29720.8  |  29720.8 |
|   3489.67 |   3489.7 |
|  -1961.12 |  -1961.1 |
|  19039.5  |  19039.5 |
| -28178    | -28178   |



filename: Number.000007
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 |
|--------:|
|   243.1 |
|    12.5 |
| 23224.1 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|   243.1 |    243.1 |
|    12.5 |     12.5 |
| 23224.1 |  23224.1 |



filename: Number.000027
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 |
|--------:|
|    9.66 |
|   15.78 |
|   16.99 |
|   16.77 |
|    6.55 |
|   19.83 |
|    9.3  |
|   12.15 |
|   16.33 |
|   17.66 |
|   12.11 |
|   12.88 |
|   20.33 |
|   17.55 |
|   19.36 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    9.66 |     9.65 |
|   15.78 |    15.8  |
|   16.99 |    17    |
|   16.77 |    16.75 |
|    6.55 |     6.55 |
|   19.83 |    19.85 |
|    9.3  |     9.3  |
|   12.15 |    12.15 |
|   16.33 |    16.35 |
|   17.66 |    17.65 |
|   12.11 |    12.1  |
|   12.88 |    12.9  |
|   20.33 |    20.35 |
|   17.55 |    17.55 |
|   19.36 |    19.35 |



filename: Number.000022
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 |
|--------:|
|    1918 |
|    1994 |
|    2024 |
|    1956 |
|    2020 |
|    1929 |
|    2009 |
|    2008 |
|    1919 |
|    1939 |
|    1954 |
|    1937 |
|    1982 |
|    2004 |
|    1951 |
|    1931 |
|    2014 |
|    1924 |
|    1974 |
|    2006 |
|    1965 |
|    1987 |
|    2012 |
|    1936 |
|    1935 |
|    1926 |
|    1968 |
|    1975 |
|    1966 |
|    1967 |
|    1985 |
|    2023 |
|    1941 |
|    1972 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    1918 |    19.18 |
|    1994 |    19.94 |
|    2024 |    20.24 |
|    1956 |    19.56 |
|    2020 |    20.2  |
|    1929 |    19.29 |
|    2009 |    20.09 |
|    2008 |    20.08 |
|    1919 |    19.19 |
|    1939 |    19.39 |
|    1954 |    19.54 |
|    1937 |    19.37 |
|    1982 |    19.82 |
|    2004 |    20.04 |
|    1951 |    19.51 |
|    1931 |    19.31 |
|    2014 |    20.14 |
|    1924 |    19.24 |
|    1974 |    19.74 |
|    2006 |    20.06 |
|    1965 |    19.65 |
|    1987 |    19.87 |
|    2012 |    20.12 |
|    1936 |    19.36 |
|    1935 |    19.35 |
|    1926 |    19.26 |
|    1968 |    19.68 |
|    1975 |    19.75 |
|    1966 |    19.66 |
|    1967 |    19.67 |
|    1985 |    19.85 |
|    2023 |    20.23 |
|    1941 |    19.41 |
|    1972 |    19.72 |



filename: Number.000018
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 |
|---------:|
|     3.89 |
| 23225    |
|     3.48 |
|    14.98 |

Output Table:
|    Input |   Output |
|---------:|---------:|
|     3.89 |      4   |
| 23225    |  23225   |
|     3.48 |      3.5 |
|    14.98 |     15   |



filename: Number.000045
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 |
|--------:|
| 123.457 |
|   3.4   |
| 102     |

Output Table:
|   Input |   Output |
|--------:|---------:|
| 123.457 |   123.46 |
|   3.4   |     3.4  |
| 102     |   102    |



filename: Number.000034
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 |
|--------:|
|    71.2 |
|    74.8 |
|    71.8 |
|    78.8 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    71.2 |       71 |
|    74.8 |       75 |
|    71.8 |       72 |
|    78.8 |       79 |



filename: Number.000048
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 |
|----------:|
|  119      |
|   14      |
|   -7.8445 |
|   76.2    |
| -113      |

Output Table:
|     Input |   Output |
|----------:|---------:|
|  119      |      119 |
|   14      |       14 |
|   -7.8445 |       -7 |
|   76.2    |       76 |
| -113      |     -113 |



filename: Number.000021
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 |
|---------:|
|   247.1  |
| 23225    |
|   249.6  |
|    14.98 |

Output Table:
|    Input |   Output |
|---------:|---------:|
|   247.1  |      245 |
| 23225    |    23225 |
|   249.6  |      250 |
|    14.98 |       15 |



filename: Number.000047
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 |
|--------:|
|     1.2 |
|    18   |
|   102   |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     1.2 |      1.2 |
|    18   |     18   |
|   102   |    102   |



filename: Number.000037
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 |
|--------:|
| 123.457 |
|   3.4   |
| 102     |

Output Table:
|   Input |   Output |
|--------:|---------:|
| 123.457 |   123.46 |
|   3.4   |     3.4  |
| 102     |   102    |



filename: Number.000033
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 |
|--------:|
|     1.2 |
|    18   |
|   102   |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     1.2 |      1.2 |
|    18   |     18   |
|   102   |    102   |



filename: Number.000036
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 |
|--------:|
|  1.0071 |
|  5      |
|  0.748  |
|  0.3348 |
|  1      |
|  0.326  |

Output Table:
|   Input |   Output |
|--------:|---------:|
|  1.0071 |   1007.1 |
|  5      |   5000   |
|  0.748  |    748   |
|  0.3348 |    334.8 |
|  1      |   1000   |
|  0.326  |    326   |



filename: Number.000017
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 |
|--------:|
| 123.457 |
|   3.4   |
| 102     |

Output Table:
|   Input |   Output |
|--------:|---------:|
| 123.457 |   123.46 |
|   3.4   |     3.4  |
| 102     |   102    |



filename: Number.000035
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 |
|--------:|
|     1.2 |
|    18   |
|   102   |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     1.2 |      1.2 |
|    18   |     18   |
|   102   |    102   |



filename: Number.000038
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 |
|---------:|
| 20040717 |
| 20060709 |
| 20031116 |
| 19991108 |
| 20060620 |
| 19960725 |
| 20010831 |
| 20070317 |
| 20010918 |
| 19960822 |
| 20080805 |

Output Table:
|    Input | Output     |
|---------:|:-----------|
| 20040717 | 2004/7/17  |
| 20060709 | 2006/7/9   |
| 20031116 | 2003/11/16 |
| 19991108 | 1999/11/8  |
| 20060620 | 2006/6/20  |
| 19960725 | 1996/7/25  |
| 20010831 | 2001/8/31  |
| 20070317 | 2007/3/17  |
| 20010918 | 2001/9/18  |
| 19960822 | 1996/8/22  |
| 20080805 | 2008/8/5   |



filename: Number.000040
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 |
|--------:|
| 123.457 |
|   3.4   |
| 102     |

Output Table:
|   Input |   Output |
|--------:|---------:|
| 123.457 |   123.46 |
|   3.4   |     3.4  |
| 102     |   102    |



filename: Number.000032
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 |
|--------:|
|   19.57 |
|   19.58 |
|  102.12 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|   19.57 |    19.55 |
|   19.58 |    19.6  |
|  102.12 |   102.1  |



filename: Number.000030
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 |
|---------:|
|  2345.23 |
|   243.1  |
| 23224.1  |

Output Table:
|    Input |   Output |
|---------:|---------:|
|  2345.23 |  2345.23 |
|   243.1  |   243.1  |
| 23224.1  | 23224.1  |



filename: Number.000028
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 | 17-07-2004 |
| 2006/07/09 | 9-07-2006  |
| 2003/11/16 | 16-11-2003 |
| 1999/11/08 | 8-11-1999  |
| 2006/06/20 | 20-06-2006 |
| 1996/07/25 | 25-07-1996 |
| 2001/08/31 | 31-08-2001 |
| 2007/03/17 | 17-03-2007 |
| 2001/09/18 | 18-09-2001 |
| 1996/08/22 | 22-08-1996 |
| 2008/08/05 | 5-08-2008  |



filename: Number.000042
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 |
|--------:|
|    1247 |
|    1036 |
|    0930 |
|    1308 |
|    2010 |
|    2345 |
|    1648 |
|    1520 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    1247 |     1247 |
|    1036 |     1036 |
|    0930 |      930 |
|    1308 |     1308 |
|    2010 |     2010 |
|    2345 |     2345 |
|    1648 |     1648 |
|    1520 |     1520 |



filename: Number.000044
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 |
|--------:|
|     954 |
|    6265 |
|     542 |
|     458 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     954 |     1000 |
|    6265 |     6300 |
|     542 |      500 |
|     458 |      500 |



filename: Number.000049
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   |
|:--------|
| 16/4    |
| 18/12   |
| 26/4    |
| 24/9    |
| 11/11   |
| 15/5    |
| 5/11    |
| 21/12   |
| 21/9    |
| 23/9    |
| 4/8     |
| 25/4    |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 16/4    | 04-16    |
| 18/12   | 12-18    |
| 26/4    | 04-26    |
| 24/9    | 09-24    |
| 11/11   | 11-11    |
| 15/5    | 05-15    |
| 5/11    | 11-05    |
| 21/12   | 12-21    |
| 21/9    | 09-21    |
| 23/9    | 09-23    |
| 4/8     | 08-04    |
| 25/4    | 04-25    |



filename: Number.000043
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 |
|---------:|
|     3.89 |
| 23225    |
|     3.48 |
|    14.98 |

Output Table:
|    Input |   Output |
|---------:|---------:|
|     3.89 |      4   |
| 23225    |  23225   |
|     3.48 |      3.5 |
|    14.98 |     15   |



filename: Number.000046
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 |
|--------:|
|  324223 |
|  234282 |
|  423531 |
|  832727 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|  324223 |   300000 |
|  234282 |   200000 |
|  423531 |   400000 |
|  832727 |   800000 |



filename: Number.000053
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 |
|--------:|
|    1954 |
|    6265 |
|     542 |
|     458 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|    1954 |     2000 |
|    6265 |     6000 |
|     542 |     1000 |
|     458 |        0 |



filename: Number.000050
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 |
|--------:|
|     158 |
|     605 |
|      64 |
|     964 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|     158 |      200 |
|     605 |      600 |
|      64 |      100 |
|     964 |     1000 |



filename: Number.000051
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 |
|---------:|
|     2321 |
|     5321 |
| 22666622 |
|     9828 |

Output Table:
|    Input |   Output |
|---------:|---------:|
|     2321 | 00002321 |
|     5321 | 00005321 |
| 22666622 | 22666622 |
|     9828 | 00009828 |



filename: Number.000054
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 |
|--------:|
| 1.23457 |
| 1.235   |
| 1.1     |
| 1.32256 |

Output Table:
|   Input |   Output |
|--------:|---------:|
| 1.23457 |  1.23457 |
| 1.235   |  1.235   |
| 1.1     |  1.1     |
| 1.32256 |  1.32256 |



filename: Number.000055
instruction: Numeric
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv