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   |
|:--------|
| 8 mm    |
| 22 mm   |
| 17 mm   |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 8 mm    | 0.314961 inch |
| 22 mm   | 0.866142 inch |
| 17 mm   | 0.669291 inch |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_1.txt
instruction: //convert mm to inches  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=mm+to+inches
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   |
|:--------|
| 39 b    |
| 12 b    |
| 198 b   |

Output Table:
| Input   | Output     |
|:--------|:-----------|
| 39 b    | 17.6901 kg |
| 12 b    | 5.44311 kg |
| 198 b   | 89.8113 kg |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_13.txt
instruction: //convert pounds to kilograms https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+pounds+to+kilograms
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          |
|:---------------|
| 100 fahrenheit |
| 123 fahrenheit |
| 350 fahrenheit |

Output Table:
| Input          | Output          |
|:---------------|:----------------|
| 100 fahrenheit | 37.7778 celsius |
| 123 fahrenheit | 50.5556 celsius |
| 350 fahrenheit | 176.667 celsius |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_25.txt
instruction: //convert fahrenheit to celsius  https://www.google.com/search?q=convert+fahrenheit+to+celsius&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                   |
|:------------------------|
| 7:00 AM,Tue,Jul 7,2015  |
| 1:11 AM,Thu,May 21,2015 |
| 1:30 AM,Thu,May 14,2015 |

Output Table:
| Input                   | Output                  |
|:------------------------|:------------------------|
| 7:00 AM,Tue,Jul 7,2015  | 8:00 AM,Tue,Jul 7,2015  |
| 1:11 AM,Thu,May 21,2015 | 2:11 AM,Thu,May 21,2015 |
| 1:30 AM,Thu,May 14,2015 | 2:30 AM,Thu,May 14,2015 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_47.txt
instruction: //convert central time to eastern time
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    |
|:---------|
| 50 inch  |
| 40 inch  |
| 429 inch |

Output Table:
| Input    | Output     |
|:---------|:-----------|
| 50 inch  | 1270 mm    |
| 40 inch  | 1016 mm    |
| 429 inch | 10896.6 mm |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_33.txt
instruction: //convert inches to millimeters  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+inches+to+millimeters
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      |
|:-----------|
| 3 gallons  |
| 15 gallons |

Output Table:
| Input      | Output   |
|:-----------|:---------|
| 3 gallons  | 48 cups  |
| 15 gallons | 240 cups |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_34.txt
instruction: // convert gallons 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               |
|:--------------------|
| 429797mE, 3996430mN |
| 715740mE, 2581625mN |
| 413642mE, 1196188mN |

Output Table:
| Input               | Output         |
|:--------------------|:---------------|
| 429797mE, 3996430mN | 36.11N, 80.22E |
| 715740mE, 2581625mN | 23.33N, 23.11E |
| 413642mE, 1196188mN | 10.82N, 20.21E |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_8.txt
instruction: //convert utm to lat long    http://www.earthpoint.us/Convert.aspx
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 meter |
| 2 meter  |
| 20 meter |

Output Table:
| Input    | Output       |
|:---------|:-------------|
| 19 meter | 62.336 feet  |
| 2 meter  | 6.56168 feet |
| 20 meter | 65.6168 feet |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_2.txt
instruction: //convert meters to feet   https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+meters+to+fee
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 L    |
| 17 L    |
| 98 L    |

Output Table:
| Input   | Output      |
|:--------|:------------|
| 15 L    | 3.96258 gal |
| 17 L    | 4.49092 gal |
| 98 L    | 25.8889 gal |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_17.txt
instruction: //convert liters to gallons https://www.google.com/search?q=square+feet+to+acres&ie=utf-8&oe=utf-8#q=convert+liters+to+gallons
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 oz   |
| 23 oz   |
| 320 oz  |

Output Table:
| Input   | Output    |
|:--------|:----------|
| 19 oz   | 538.641 g |
| 23 oz   | 652.039 g |
| 320 oz  | 9071.85 g |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_32.txt
instruction: //convert ounces to grams https://www.google.com/search?q=square+feet+to+acres&ie=utf-8&oe=utf-8#q=convert+ounces+to+grams
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   |
|:--------|
| 59 kb   |
| 123 kb  |
| 140 kb  |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 59 kb   | 0.059 mb |
| 123 kb  | 0.123 mb |
| 140 kb  | 0.14 mb  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_31.txt
instruction: //convert kb to mb
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   |
|:--------|
| 30 g    |
| 23 g    |
| 1500 g  |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 30 g    | 1.05821886 oz |
| 23 g    | 0.81130113 oz |
| 1500 g  | 52.910943 oz  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_20.txt
instruction: //convert grams to ounces
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   |
|:--------|
| 870 cm  |
| 555 cm  |
| 69 cm   |

Output Table:
| Input   | Output                    |
|:--------|:--------------------------|
| 870 cm  | 28 feet and 6.5197 inches |
| 555 cm  | 18 feet and 2.5039 inches |
| 69 cm   | 2 feet and 3.1654 inches  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_9.txt
instruction: //convert cm to feet and inches  http://www.thecalculatorsite.com/conversions/common/cm-to-feet-inches.php
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   |
|:--------|
| 259 m   |
| 888 m   |
| 387 m   |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 259 m   | 849.738 feet |
| 888 m   | 2913.39 feet |
| 387 m   | 1269.69 feet |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_15.txt
instruction: //convert meter to feet  https://www.google.com/search?q=convert+meter+to+feet&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   |
|:--------|
| 18 km   |
| 33 km   |
| 1349 km |

Output Table:
| Input   | Output          |
|:--------|:----------------|
| 18 km   | 11.1846815 mile |
| 33 km   | 20.5052493 mile |
| 1349 km | 838.229738 mile |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_11.txt
instruction: //convert kilometers to miles   https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=kilometers+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   |
|:--------|
| 89 g    |
| 23 g    |
| 100 g   |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 89 g    | 0.19621141 b |
| 23 g    | 0.05070632 b |
| 100 g   | 0.22046226 b |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_16.txt
instruction: //convert grams to pounds  http://www.bing.com/search?q=convert+grams+to+pounds&qs=n&form=QBRE&pq=convert+grams+to+pounds&sc=8-23&sp=-1&sk=&cvid=9BB5A5EDFF42464AB966C353D2AA7869
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 grams |
| 10 grams |
| 50 grams |

Output Table:
| Input    | Output       |
|:---------|:-------------|
| 15 grams | 0.0634 cups  |
| 10 grams | 0.04227 cups |
| 50 grams | 0.21134 cups |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_26.txt
instruction: //convert grams to cups  http://calculator-converter.com/converter_g_to_c_grams_to_cups_calculator.php
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   |
|:--------|
| 39 ml   |
| 30 ml   |
| 200 ml  |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 39 ml   | 1.31874689 oz |
| 30 ml   | 1.01442068 oz |
| 200 ml  | 6.76280455 oz |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_22.txt
instruction: //convert ml to oz  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+ml+to+oz
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      |
|:-----------|
| 20 dollar  |
| 222 dollar |
| 300 dollar |

Output Table:
| Input      | Output      |
|:-----------|:------------|
| 20 dollar  | 17.79 euro  |
| 222 dollar | 197.50 euro |
| 300 dollar | 266.89 euro |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_18.txt
instruction: //convert dollars to euros  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+dollars+to+euros
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     |
|:----------|
| 180 meter |
| 11 meter  |
| 92 meter  |

Output Table:
| Input     | Output          |
|:----------|:----------------|
| 180 meter | 0.111847 mile   |
| 11 meter  | 0.00683508 mile |
| 92 meter  | 0.0571661 mile  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_40.txt
instruction: //convert meters to miles https://www.google.com/search?q=convert+meters+to+miles&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        |
|:-------------|
| 12 Celsuis   |
| 24 Celsuis   |
| 12.3 Celsuis |

Output Table:
| Input        | Output        |
|:-------------|:--------------|
| 12 Celsuis   | 285.15 Kelvin |
| 24 Celsuis   | 297.15 Kelvin |
| 12.3 Celsuis | 285.45 Kelvin |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_38.txt
instruction: // convert celcius to kelvin  http://www.bing.com/search?q=convert+celcius+to+kelvin&qs=n&form=QBRE&pq=convert+celcius+to+kelvin&sc=8-25&sp=-1&sk=&cvid=72635BAF4E2E4D52A3FD009812644B35
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   |
|:--------|
| 145 mm  |
| 199 mm  |
| 23 mm   |

Output Table:
| Input   | Output   |
|:--------|:---------|
| 145 mm  | 14.5 cm  |
| 199 mm  | 19.9 cm  |
| 23 mm   | 2.3 cm   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_28.txt
instruction: //convert mm to cm https://www.google.com/search?q=square+feet+to+acres&ie=utf-8&oe=utf-8#q=convert+mm+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 Celsius       |
| 50.5556 Celsius |
| 18 Celsius      |

Output Table:
| Input           | Output          |
|:----------------|:----------------|
| 7 Celsius       | 44.6 Fahrenheit |
| 50.5556 Celsius | 123 Fahrenheit  |
| 18 Celsius      | 64.4 Fahrenheit |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_3.txt
instruction: //convert celsius to fahrenheit  https://www.google.com/search?q=%2F%2Fconvert+celsius+to+fahrenheit&ie=utf-8&oe=utf-8#q=convert+celsius+to+fahrenheit
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    |
|:---------|
| 23 inch  |
| 22 inch  |
| 921 inch |

Output Table:
| Input    | Output     |
|:---------|:-----------|
| 23 inch  | 58.42 cm   |
| 22 inch  | 55.88 cm   |
| 921 inch | 2339.34 cm |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_30.txt
instruction: //convert inches to centimeters  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+inches+to+centimeters
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            |
|:-----------------|
| 120 square feet  |
| 890 square feet  |
| 2300 square feet |

Output Table:
| Input            | Output           |
|:-----------------|:-----------------|
| 120 square feet  | 0.00275482 acres |
| 890 square feet  | 0.02043159 acres |
| 2300 square feet | 0.05280073 acres |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_29.txt
instruction: //convert square feet to acres http://www.bing.com/search?q=convert+square+feet+to+acres&qs=n&form=QBLH&pq=convert+square+feet+to+acres&sc=5-28&sp=-1&sk=&cvid=d472d11b971042988da81476fee2f9ce
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            |
|:-----------------|
| 390 square feet  |
| 872 square feet  |
| 2456 square feet |

Output Table:
| Input            | Output                  |
|:-----------------|:------------------------|
| 390 square feet  | 43.3333333 square yards |
| 872 square feet  | 96.8888889 square yards |
| 2456 square feet | 272.888889 square yards |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_46.txt
instruction: //convert square feet to square yards  http://www.bing.com/search?q=square+feet+to+square+yards&qs=n&form=QBRE&pq=square+feet+to+square+yards&sc=8-27&sp=-1&sk=&cvid=DB7CB65FF57241AFABB51EC37B83D89A
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   |
|:--------|
| 200/4   |
| 1/2     |
| 9/100   |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 200/4   |    50    |
| 1/2     |     0.5  |
| 9/100   |     0.09 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_5.txt
instruction: //convert to decimal notation
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv