{"task": {"agent_timeout": 1800, "task": "822", "verifier_timeout": 1800, "instruction": "# 822: DS-1000 Task\n\n## Prompt\nProblem:\n\nI'd like to do some operations to my df. And there is an example below.\ndf\n\nCol1   Col2         Col3\n C      33     [Apple, Orange, Banana]\n A      2.5    [Apple, Grape]\n B      42     [Banana]\nafter the operations, the df is converted into\n\ndf\n\nCol1   Col2   Apple   Orange   Banana   Grape\n C      33     1        1        1       0\n A      2.5    1        0        0       1\n B      42     0        0        1       0\nGenerally, I want this pandas column which consisting of a list of String names broken down into as many columns as the unique names.\nMaybe it's like one-hot-encode them (note that value 1 representing a given name existing in a row and then 0 is absence).\nCould any one give me any suggestion of pandas or sklearn methods? thanks!\n\nA:\n\n<code>\nimport pandas as pd\nimport numpy as np\nimport sklearn\ndf = load_data()\n</code>\ndf_out = ... # put solution in this variable\nBEGIN SOLUTION\n<code>\n\n## What to do\n- Edit `solution/solution.py` so the code passes the DS-1000 tests.\n- Do not access the internet or install new packages; required libraries are preinstalled in the Docker image.\n- Run tests locally via `bash tests/test.sh`.\n\n## Notes\n- Keep the variable names/signatures implied by the prompt/code_context.\n- The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`).\n", "memory": "", "runnable": false, "difficulty": "", "language": "", "cpus": "", "instruction_truncated": false, "category": "", "compose": false, "has_solution": true, "oracle": null, "docker_image": "ds1000:latest", "taskset": "ds1000", "tags": []}, "runs": []}