# ds1000 / 52 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 52: DS-1000 Task ## Prompt Problem: Sample dataframe: df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 0]}) I'd like to add inverses of each existing column to the dataframe and name them based on existing column names with a prefix, e.g. inv_A is an inverse of column A and so on. Notice that 0 has no inverse and please keep it in inv_A The resulting dataframe should look like so: result = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 0], "inv_A": [1/1, 1/2, 1/3], "inv_B": [1/4, 1/5, 0]}) Obviously there are redundant methods like doing this in a loop, but there should exist much more pythonic ways of doing it and after searching for some time I didn't find anything. I understand that this is most probably a duplicate; if so, please point me to an existing answer. A: <code> import pandas as pd df = pd.DataFrame({"A": [1, 0, 3], "B": [4, 5, 6]}) </code> result = ... # put solution in this variable BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp