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

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

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

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

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

Input Table:
| Input    |
|:---------|
| 67 euro  |
| 11 euro  |
| 360 euro |

Output Table:
| Input    | Output        |
|:---------|:--------------|
| 67 euro  | 74.20 dollar  |
| 11 euro  | 12.18 dollar  |
| 360 euro | 398.70 dollar |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_35.txt
instruction: //convert euros to dollars   google.com
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input    |
|:---------|
| 15 inch  |
| 16 inch  |
| 911 inch |

Output Table:
| Input    | Output     |
|:---------|:-----------|
| 15 inch  | 38.10 cm   |
| 16 inch  | 40.64 cm   |
| 911 inch | 2313.94 cm |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_36.txt
instruction: //convert inches to cm  https://www.google.com/search?q=square+feet+to+acres&ie=utf-8&oe=utf-8#q=convert+inches+to+cm
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 7 kg    |
| 1 kg    |
| 11 kg   |

Output Table:
| Input   | Output    |
|:--------|:----------|
| 7 kg    | 15.4324 b |
| 1 kg    | 2.20462 b |
| 11 kg   | 24.2508 b |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_4.txt
instruction: //convert kg to lbs  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=kg+to+b
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input               |
|:--------------------|
| 2008/02/26 17:37:58 |
| 2000/02/26 11:37:50 |
| 2000/02/26 00:37:50 |

Output Table:
| Input               | Output              |
|:--------------------|:--------------------|
| 2008/02/26 17:37:58 | 2008/02/26 16:37:58 |
| 2000/02/26 11:37:50 | 2000/02/26 10:37:50 |
| 2000/02/26 00:37:50 | 2000/02/25 23:37:50 |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/datetime_diff_transformation.txt
instruction: //diff
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input    |
|:---------|
| 17 inch  |
| 33 inch  |
| 100 inch |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 17 inch  | 431.8 mm |
| 33 inch  | 838.2 mm |
| 100 inch | 2540 mm  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_8.txt
instruction: //convert inches to mm  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=convert+inches+to+mm
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 790 mb  |
| 42 mb   |
| 49 mb   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 790 mb  | 0.790 gb |
| 42 mb   | 0.042 gb |
| 49 mb   | 0.049 gb |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_37.txt
instruction: //convert mb to gb https://www.google.com/search?q=convert+meters+to+miles&ie=utf-8&oe=utf-8#q=convert+mb+to+gb
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 1856 m  |
| 33 m    |
| 200 m   |

Output Table:
| Input   | Output          |
|:--------|:----------------|
| 1856 m  | 73070.8661 inch |
| 33 m    | 1299.2126 inch  |
| 200 m   | 7874.01575 inch |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_43.txt
instruction: //convert meters to inches http://www.bing.com/search?q=convert+meters+to+inches&qs=n&form=QBRE&pq=convert+meters+to+inches&sc=8-24&sp=-1&sk=&cvid=AF65D2FF77F64A84A8564E5D7CB50C42
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 862 oz  |
| 5 oz    |
| 51 oz   |

Output Table:
| Input   | Output    |
|:--------|:----------|
| 862 oz  | 53.875 lb |
| 5 oz    | 0.3125 lb |
| 51 oz   | 3.1875 lb |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_39.txt
instruction: //convert ounces to pounds   https://www.google.com/search?q=convert+ounces+to+pounds&ie=utf-8&oe=utf-8
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 502 lb  |
| 122 lb  |
| 76 lb   |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 502 lb  | 227703.37 g  |
| 122 lb  | 55338.2691 g |
| 76 lb   | 34473.0201 g |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_41.txt
instruction: //convert pounds to grams http://www.bing.com/search?q=convert+pounds+to+grams&qs=n&form=QBRE&pq=convert+pounds+to+grams&sc=8-23&sp=-1&sk=&cvid=7B02D68DA1784B898B6985B9ED9A89CC
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input     |
|:----------|
| 980 inch  |
| 111 inch  |
| 2000 inch |

Output Table:
| Input     | Output   |
|:----------|:---------|
| 980 inch  | 24.892 m |
| 111 inch  | 2.8194 m |
| 2000 inch | 50.8 m   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_47.txt
instruction: //convert inches to meters   https://www.google.com/search?q=convert+liters+to+ounces&ie=utf-8&oe=utf-8#q=convert+inches+to+meters
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input      |
|:-----------|
| 300 pound  |
| 444 pound  |
| 2100 pound |

Output Table:
| Input      | Output         |
|:-----------|:---------------|
| 300 pound  | 470.78 dollar  |
| 444 pound  | 696.75 dollar  |
| 2100 pound | 3295.45 dollar |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_44.txt
instruction: //convert pounds to dollars  https://www.google.com/search?q=convert+ounces+to+pounds&ie=utf-8&oe=utf-8#q=convert+pounds+to+dollars
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 5 cm    |
| 3 cm    |
| 6 cm    |

Output Table:
| Input   | Output          |
|:--------|:----------------|
| 5 cm    | 1.96850394 inch |
| 3 cm    | 1.18110236 inch |
| 6 cm    | 2.36220472 inch |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_5.txt
instruction: //convert cm to inches  www.bing.com/search?q=convert+cm+to+inches&qs=n&form=QBRE&pq=convert+cm+to+inches&sc=8-24&sp=-1&sk=&cvid=A5A14DEE028043618E89B4BC9FB24B89
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 1988 oz |
| 123 oz  |
| 37 oz   |

Output Table:
| Input   | Output     |
|:--------|:-----------|
| 1988 oz | 248.5 cup  |
| 123 oz  | 15.375 cup |
| 37 oz   | 4.625 cup  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_42.txt
instruction: //convert ounces to cups https://www.google.com/search?q=v&ie=utf-8&oe=utf-8#q=convert+ounces+to+cups
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 7 feet  |
| 22 feet |
| 18 feet |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 7 feet  | 2.1336 m |
| 22 feet | 6.7056 m |
| 18 feet | 5.4864 m |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_6.txt
instruction: //convert feet to meters  https://www.google.com/search?q=square+feet+to+acres&ie=utf-8&oe=utf-8#q=convert+feet+to+meters
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 5870 L  |
| 99 L    |
| 35 L    |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 5870 L  | 198488.313 oz |
| 99 L    | 3347.58825 oz |
| 35 L    | 1183.4908 oz  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_49.txt
instruction: //convert liters to ounces  http://www.bing.com/search?q=convert+liters+to+ounces&qs=n&form=QBRE&pq=convert+liters+to+ounces&sc=8-24&sp=-1&sk=&cvid=72C63370436C468BAF87898878785554
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input     |
|:----------|
| 0.5 acres |
| 4 acres   |
| 15 acres  |

Output Table:
| Input     | Output             |
|:----------|:-------------------|
| 0.5 acres | 21780 square feet  |
| 4 acres   | 174240 square feet |
| 15 acres  | 653400 square feet |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_48.txt
instruction: //convert acres to square feet https://www.google.com/search?q=convert+liters+to+ounces&ie=utf-8&oe=utf-8#q=convert+acres+to+square+feet
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
|   Input |
|--------:|
|    1350 |
|      11 |
|     981 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|    1350 | MCCCL    |
|      11 | XI       |
|     981 | CMLXXXI  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_50.txt
instruction: //convert to roman numerals  http://www.onlineconversion.com/roman_numerals_advanced.htm
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 43 mg   |
| 304 mg  |
| 211 mg  |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 43 mg   | 86 ml    |
| 304 mg  | 608 ml   |
| 211 mg  | 422 ml   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_50.txt
instruction: //convert mg to ml
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input              |
|:-------------------|
| 560 square meters  |
| 323 square meters  |
| 2000 square meters |

Output Table:
| Input              | Output                 |
|:-------------------|:-----------------------|
| 560 square meters  | 6027.78983 square feet |
| 323 square meters  | 3476.74306 square feet |
| 2000 square meters | 21527.8208 square feet |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_45.txt
instruction: //convert square meters to square feet  http://www.bing.com/search?q=convert+square+meters+to+square+feet&qs=n&form=QBRE&pq=convert+square+meters+to+square+feet&sc=6-36&sp=-1&sk=&cvid=8508968AFE4A4C1FAE568F78EEE6C5DE
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input         |
|:--------------|
| 21 US Dollar  |
| 12 US Dollar  |
| 111 US Dollar |

Output Table:
| Input         | Output       |
|:--------------|:-------------|
| 21 US Dollar  | 19.02 euros  |
| 12 US Dollar  | 10.87 euros  |
| 111 US Dollar | 100.56 euros |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_7.txt
instruction: //convert dollars to euros google.com
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 251 km  |
| 44 km   |
| 321 km  |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 251 km  | 155.964 mile |
| 44 km   | 27.3403 mile |
| 321 km  | 199.46 mile  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_10.txt
instruction: //convert km to miles  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=km+to+miles
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 10B     |
| 100 KB  |
| 1GB     |

Output Table:
| Input   | Output              |
|:--------|:--------------------|
| 10B     | 10 Bytes            |
| 100 KB  | 102,400 Bytes       |
| 1GB     | 1,073,741,824 Bytes |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_bytesize2.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input    |
|:---------|
| 19 mile  |
| 11 mile  |
| 240 mile |

Output Table:
| Input    | Output     |
|:---------|:-----------|
| 19 mile  | 30.5775 km |
| 11 mile  | 17.7028 km |
| 240 mile | 386.243 km |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_9.txt
instruction: //convert miles to kilometers  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=miles+to+kilometers
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 17 cm   |
| 2 cm    |
| 201 cm  |

Output Table:
| Input   | Output         |
|:--------|:---------------|
| 17 cm   | 0.557743 feet  |
| 2 cm    | 0.0656168 feet |
| 201 cm  | 6.59449 feet   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_24.txt
instruction: //convert cm to feet
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 10B     |
| 100 KB  |
| 1GB     |

Output Table:
| Input   | Output           |
|:--------|:-----------------|
| 10B     | 10 Bytes         |
| 100 KB  | 102400 Bytes     |
| 1GB     | 1073741824 Bytes |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_bytesize.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input                                               |
|:----------------------------------------------------|
| 1201 super commerce blvd Ste H, Richmond, VA, 23452 |
| 10 commerce way ste a, Richmond, VA, 23452          |
| 100 sunset blvd, Room 301, Sacramento California    |

Output Table:
| Input                                               | Output   |
|:----------------------------------------------------|:---------|
| 1201 super commerce blvd Ste H, Richmond, VA, 23452 | STE H    |
| 10 commerce way ste a, Richmond, VA, 23452          | STE A    |
| 100 sunset blvd, Room 301, Sacramento California    | RM 301   |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_comp_address_secondaryline.txt
instruction: //address
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input                  |
|:-----------------------|
| August 20, 2015, 16:11 |
| April 11, 2015, 15:14  |
|                        |

Output Table:
| Input                  | Output                 |
|:-----------------------|:-----------------------|
| August 20, 2015, 16:11 | August 21, 2015, 04:41 |
| April 11, 2015, 15:14  | April 12, 2015, 03:44  |
|                        |                        |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_comp_datetime_timezone.txt
instruction: //
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: TDE)

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

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

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

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

Input Table:
| Input   |
|:--------|
| 10B     |
| 100 KB  |
| 1GB     |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 10B     | 1.00E1 Bytes |
| 100 KB  | 1.02E5 Bytes |
| 1GB     | 1.07E9 Bytes |



filename: Transform-Data-by-Example/Benchmark/benchmark-headcase/funct_bytesize3.txt
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv