# ds1000 / 41 - 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 ``` # 41: DS-1000 Task ## Prompt Problem: I have a dataframe with numerous columns (≈30) from an external source (csv file) but several of them have no value or always the same. Thus, I would to see quickly the value_counts for each column. How can i do that? For example id, temp, name 1 34, null, mark 2 22, null, mark 3 34, null, mark Please return a String like this: ---- id --- 34 2 22 1 Name: id, dtype: int64 ---- temp --- null 3 Name: temp, dtype: int64 ---- name --- mark 3 Name: name, dtype: int64 So I would know that temp is irrelevant and name is not interesting (always the same) A: <code> import pandas as pd df = pd.DataFrame(data=[[34, 'null', 'mark'], [22, 'null', 'mark'], [34, 'null', 'mark']], columns=['id', 'temp', 'name'], index=[1, 2, 3]) </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