{"task": {"agent_timeout": 1800, "task": "246", "verifier_timeout": 1800, "instruction": "# 246: DS-1000 Task\n\n## Prompt\nProblem:\n\n\nI have a pandas series which values are numpy array. For simplicity, say\n\n\n\n\n    series = pd.Series([np.array([1,2,3,4]), np.array([5,6,7,8]), np.array([9,10,11,12])], index=['file1', 'file2', 'file3'])\n\n\nfile1       [1, 2, 3, 4]\nfile2       [5, 6, 7, 8]\nfile3    [9, 10, 11, 12]\n\n\nHow can I expand it to a dataframe of the form df_concatenated:\n       0   1   2   3\nfile1  1   2   3   4\nfile2  5   6   7   8\nfile3  9  10  11  12\n\n\nA:\n<code>\nimport pandas as pd\nimport numpy as np\n\n\nseries = pd.Series([np.array([1,2,3,4]), np.array([5,6,7,8]), np.array([9,10,11,12])], index=['file1', 'file2', 'file3'])\n</code>\ndf = ... # 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": []}