{"task": {"agent_timeout": 1800, "task": "32", "verifier_timeout": 1800, "instruction": "# 32: DS-1000 Task\n\n## Prompt\nProblem:\nConsidering a simple df:\nHeaderA | HeaderB | HeaderC | HeaderX\n    476      4365      457        345\n\n\nIs there a way to rename all columns, for example to add to columns which don\u2019t end with \"X\" and add to all columns an \"X\" in the head?\nXHeaderAX | XHeaderBX | XHeaderCX  | XHeaderX\n    476      4365      457    345\n\n\nI am concatenating multiple dataframes and want to easily differentiate the columns dependent on which dataset they came from. \nOr is this the only way?\ndf.rename(columns={'HeaderA': 'HeaderAX'}, inplace=True)\n\n\nI have over 50 column headers and ten files; so the above approach will take a long time. \nThank You\n\n\nA:\n<code>\nimport pandas as pd\n\n\ndf = pd.DataFrame(\n    {'HeaderA': [476],\n     'HeaderB': [4365],\n     'HeaderC': [457],\n     \"HeaderX\": [345]})\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": []}