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 |
|---------:|
| 19811120 |
| 20210311 |
| 20110301 |

Output Table:
|    Input | Output       |
|---------:|:-------------|
| 19811120 | Nov 20, 1981 |
| 20210311 | Mar 11, 2021 |
| 20110301 | Mar 01, 2011 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_35.txt
instruction: //convert yyyymmdd to datetime
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv
Task: Data-transform-pbe (source dataset: 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                                         |
|:----------------------------------------------|
| 3002 Mt Angeles Rd, Port Angeles, WA, USA     |
| 14820 36th st ne redmond, wa, 98052           |
| 2329 West Mall, Vancouver, BC V6T 1Z4, Canada |

Output Table:
| Input                                         | Output                 |
|:----------------------------------------------|:-----------------------|
| 3002 Mt Angeles Rd, Port Angeles, WA, USA     | 48.099339, -123.425532 |
| 14820 36th st ne redmond, wa, 98052           | 47.642095, -122.142068 |
| 2329 West Mall, Vancouver, BC V6T 1Z4, Canada | 49.261375, -123.253672 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_23.txt
instruction: //convert address to gps coordinates  http://www.latlong.net/convert-address-to-lat-long.html
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 |
|--------:|
|   0.125 |
|   0.2   |
|   4.5   |

Output Table:
|   Input | Output   |
|--------:|:---------|
|   0.125 | 1/8      |
|   0.2   | 1/5      |
|   4.5   | 9/2      |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_3.txt
instruction: //convert to fractional notation
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 |
|--------:|
|   39934 |
|   39270 |
|   39958 |

Output Table:
|   Input | Output     |
|--------:|:-----------|
|   39934 | 05/01/2009 |
|   39270 | 07/07/2007 |
|   39958 | 05/25/2009 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_32.txt
instruction: //convert excel date to datetime   http://www.dotnetperls.com/fromoadate
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 |
|--------:|
|      60 |
|      91 |
|      95 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|      60 | 01:00:00 |
|      91 | 01:31:00 |
|      95 | 01:35:00 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_28.txt
instruction: //convert decimal minutes to 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              |
|:-------------------|
| 15S WC 80817 51205 |
| 15S WC 40817 12345 |
| 19T DJ 38588 97366 |

Output Table:
| Input              | Output              |
|:-------------------|:--------------------|
| 15S WC 80817 51205 | 38.40543  -92.07440 |
| 15S WC 40817 12345 | 38.05793  -92.53473 |
| 19T DJ 38588 97366 | 44.22694  -69.76892 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_38.txt
instruction: //convert mgrs to lat long http://www.legallandconverter.com/p50.html
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 m/s  |
| 33 m/s  |
| 123 m/s |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 89 m/s  | 199.0873 mph |
| 33 m/s  | 73.81890 mph |
| 123 m/s | 275.1432 mph |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_37.txt
instruction: //convert m s to mph   http://www.metric-conversions.org/speed/meters-per-second-to-miles-per-hour.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 |
|--------:|
|       7 |
|       3 |
|      12 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|       7 | July     |
|       3 | March    |
|      12 | December |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_36.txt
instruction: //convert month number to month name
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         |
|:--------------|
| 44.11, -77.33 |
| 44.22, -77.44 |
| 66.99, -33.11 |

Output Table:
| Input         | Output          |
|:--------------|:----------------|
| 44.11, -77.33 | 18TUP1353886730 |
| 44.22, -77.44 | 18TUP0509799203 |
| 66.99, -33.11 | 25WDQ9520130269 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_30.txt
instruction: //convert lat long to MGRS  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    |
|:---------|
| 17 psia  |
| 3 psia   |
| 100 psia |

Output Table:
| Input    | Output          |
|:---------|:----------------|
| 17 psia  | 2.304051 psig   |
| 3 psia   | -11.695949 psig |
| 100 psia | 85.304051 psig  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_27.txt
instruction: //convert psia to psig http://www.endmemo.com/cconvert/psiag.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                  |
|:-----------------------|
| 1:00 AM, Jun 3, 2015   |
| 11:00 AM, Mar 20, 2015 |
| 10:00 AM, Mar 20, 2015 |

Output Table:
| Input                  | Output                |
|:-----------------------|:----------------------|
| 1:00 AM, Jun 3, 2015   | 4:00 AM, Jun 3, 2015  |
| 11:00 AM, Mar 20, 2015 | 2:00 PM, Mar 20, 2015 |
| 10:00 AM, Mar 20, 2015 | 1:00 PM, Mar 20, 2015 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_44.txt
instruction: //convert pst to est
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 Muharram 1300 |
| 1 Muharram 1431 |
| 19 Rajab 1460   |

Output Table:
| Input           | Output                        |
|:----------------|:------------------------------|
| 5 Muharram 1300 | Thursday 16 November 1882 C.E |
| 1 Muharram 1431 | Friday 18 December 2009 C.E   |
| 19 Rajab 1460   | Friday 20 August 2038 C.E     |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_25.txt
instruction: //convert hijri to gregorian   http://www.islamicfinder.org/phone/hijri.php?day=1&year=1431&month=1&mode=hij-ger&date_result=1
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 |
|--------:|
|  198645 |
|    1234 |
|      33 |

Output Table:
|   Input |   Output |
|--------:|---------:|
|  198645 |   603765 |
|    1234 |     2322 |
|      33 |       41 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_33.txt
instruction: //convert decimal to octal   http://www.binaryhexconverter.com/decimal-to-octal-converter
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 2/5  |
| 3 4/5   |
| 129     |

Output Table:
| Input   |   Output |
|:--------|---------:|
| 21 2/5  |     21.4 |
| 3 4/5   |      3.8 |
| 129     |    129   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_46.txt
instruction: //convert mixed fraction to decimal
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               |
|:--------------------|
| 28.889139, -77.1234 |
| 36.889139, -76.1234 |
| 42.889139, -75.1234 |

Output Table:
| Input               | Output                  |
|:--------------------|:------------------------|
| 28.889139, -77.1234 | 28.8894274, -77.1230153 |
| 36.889139, -76.1234 | 36.8892866, -76.1230571 |
| 42.889139, -75.1234 | 42.8892103, -75.1230043 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_41.txt
instruction: //convert Geographic Coordinate to nad83   http://www.apsalin.com/nad-conversion.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      |
|:-----------|
| 43 minutes |
| 44 minutes |
| 58 minutes |

Output Table:
| Input      |   Output |
|:-----------|---------:|
| 43 minutes |     0.72 |
| 44 minutes |     0.73 |
| 58 minutes |     0.97 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_45.txt
instruction: //convert minutes to hundredths http://www.postalemployeenetwork.com/time-conversion-print.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   |
|:--------|
| 1000g   |
| 123g    |
| 3400g   |

Output Table:
| Input   | Output         |
|:--------|:---------------|
| 1000g   | 2lb 3.273965oz |
| 123g    | 0lb 4.338698oz |
| 3400g   | 7lb 7.931480oz |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_43.txt
instruction: //convert grams to ounces and pounds http://www.metric-conversions.org/weight/grams-to-pounds.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 |
|--------:|
| 1246367 |
|  123234 |
| 9872031 |

Output Table:
|   Input | Output   |
|--------:|:---------|
| 1246367 | 13049F   |
|  123234 | 1E162    |
| 9872031 | 96A29F   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_42.txt
instruction: //convert to hexadecimal http://www.binaryhexconverter.com/decimal-to-hex-converter
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    |
|:---------|
| (4, 60)  |
| (11, 22) |
| (10, 30) |

Output Table:
| Input    | Output              |
|:---------|:--------------------|
| (4, 60)  | (2.00000, 3.46410)  |
| (11, 22) | (10.19902, 4.12067) |
| (10, 30) | (8.66025, 5.00000)  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_49.txt
instruction: //convert polar to rectangular  http://www.daycounter.com/Calculators/Polar-To-Rectangular-Calculator.phtml
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 mm   |
| 41 mm   |
| 130 mm  |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 39 mm   | 1.53543 inch |
| 41 mm   | 1.61417 inch |
| 130 mm  | 5.11811 inch |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_19.txt
instruction: //convert millimeters 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       |
|:------------|
| 238,130,238 |
| 34,139,34   |
| 147,112,219 |

Output Table:
| Input       | Output   |
|:------------|:---------|
| 238,130,238 | #EE82EE  |
| 34,139,34   | #228B22  |
| 147,112,219 | #9370DB  |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_6.txt
instruction: //convert rgb to hex  http://www.javascripter.net/faq/rgbtohex.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   |
|:--------|
| 37 inch |
| 44 inch |
| 12 inch |

Output Table:
| Input   | Output          |
|:--------|:----------------|
| 37 inch | 3.08333333 feet |
| 44 inch | 3.66666667 feet |
| 12 inch | 1 feet          |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_12.txt
instruction: //convert inches to feet  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=inches+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   |
|:--------|
| 30 feet |
| 23 feet |
| 53 feet |

Output Table:
| Input   | Output     |
|:--------|:-----------|
| 30 feet | 360 inches |
| 23 feet | 276 inches |
| 53 feet | 636 inches |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_27.txt
instruction: //convert feet to inches  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+feet+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 |
|--------:|
|    2156 |
|  123234 |
|    9876 |

Output Table:
|   Input | Output   |
|--------:|:---------|
|    2156 | 86C      |
|  123234 | 1E162    |
|    9876 | 2694     |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_7.txt
instruction: //convert decimal to hex
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/hour |
| $11/hour |
| $40/hour |

Output Table:
| Input    | Output     |
|:---------|:-----------|
| $15/hour | $31,200.00 |
| $11/hour | $22,880.00 |
| $40/hour | $83,200.00 |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/semantic_48.txt
instruction: //convert hourly wage to salary  http://us.thesalarycalculator.co.uk/hourly.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   |
|:--------|
| 18 b    |
| 12 b    |
| 101 b   |

Output Table:
| Input   | Output        |
|:--------|:--------------|
| 18 b    | 8.16466265 kg |
| 12 b    | 5.44310844 kg |
| 101 b   | 45.8128293 kg |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_14.txt
instruction: //convert lbs to kg  http://www.bing.com/search?q=convert+lbs+to+kg&qs=n&form=QBLH&pq=convert+lbs+to+kg&sc=8-17&sp=-1&sk=&cvid=ac58462166184654886c0e146be3c81e
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 inch  |
| 2 inch   |
| 300 inch |

Output Table:
| Input    | Output   |
|:---------|:---------|
| 39 inch  | 99.06 cm |
| 2 inch   | 5.08 cm  |
| 300 inch | 762 cm   |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_21.txt
instruction: //convert inch to centimeter   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   |
|:--------|
| 99 cm   |
| 123 cm  |
| 367 cm  |

Output Table:
| Input   | Output       |
|:--------|:-------------|
| 99 cm   | 38.9764 inch |
| 123 cm  | 48.4252 inch |
| 367 cm  | 144.488 inch |



filename: Transform-Data-by-Example/Benchmark/benchmark-bing-query-logs/unit_23.txt
instruction: //convert centimeters to inches  https://www.google.com/search?q=convert+fahrenheit+to+celsius&ie=utf-8&oe=utf-8#q=convert+centimeters+to+inches
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv