# ds1000 / 594 - 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 ``` # 594: DS-1000 Task ## Prompt import matplotlib.pyplot as plt import pandas as pd import numpy as np df = pd.DataFrame( np.random.randn(50, 4), index=pd.date_range("1/1/2000", periods=50), columns=list("ABCD"), ) df = df.cumsum() # make four line plots of data in the data frame # show the data points on the line plot # SOLUTION START ## 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