Task: Data-transform-pbe (source dataset: Transformation-text)

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

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

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

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

Input Table:
| Input                                                                  |
|:-----------------------------------------------------------------------|
| 206 558 906 442 977 Cecep 632_469 995 677 Shakenuly                    |
| 949 270 496 327 Jakob 588_908 119 102 613 637 570 Abela                |
| 457 Khady 283@630 12 445 294 Dangol                                    |
| 699 526 934 687 546 81 Jan 988@642 343 957 Pirc                        |
| 150 Andrey 388@816 509 962 993 16 653 278 Ansari                       |
| 900 678 93 149 106 609 Meaza 677@569 360 850 62 281 704 Dursunowa      |
| 195 887 Julia 368@244 983 961 157 772 91 Moeller                       |
| 86 838 170 644 826 219 Foluke 719_809 125 905 189 476 540 Abdirashkyzy |
| 663 80 533 Ojerinde 63_152 34 517 Cheburko                             |
| 945 851 896 466 844 356 Faruk 390_421 912 299 680 342 466 Zweber       |

Output Table:
| Input                                                                  | Output          |
|:-----------------------------------------------------------------------|:----------------|
| 206 558 906 442 977 Cecep 632_469 995 677 Shakenuly                    | Cecep 632_469   |
| 949 270 496 327 Jakob 588_908 119 102 613 637 570 Abela                | Jakob 588_908   |
| 457 Khady 283@630 12 445 294 Dangol                                    | Khady 283@630   |
| 699 526 934 687 546 81 Jan 988@642 343 957 Pirc                        | Jan 988@642     |
| 150 Andrey 388@816 509 962 993 16 653 278 Ansari                       | Andrey 388@816  |
| 900 678 93 149 106 609 Meaza 677@569 360 850 62 281 704 Dursunowa      | Meaza 677@569   |
| 195 887 Julia 368@244 983 961 157 772 91 Moeller                       | Julia 368@244   |
| 86 838 170 644 826 219 Foluke 719_809 125 905 189 476 540 Abdirashkyzy | Foluke 719_809  |
| 663 80 533 Ojerinde 63_152 34 517 Cheburko                             | Ojerinde 63_152 |
| 945 851 896 466 844 356 Faruk 390_421 912 299 680 342 466 Zweber       | Faruk 390_421   |



filename: Name.000010
instruction: Substring
example_input: example_input.csv
example_output: example_output.csv
test_input: test_input.csv
test_output: test_output.csv