# ds1000 / 938

- 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

```
# 938: DS-1000 Task

## Prompt
Problem:

I'd like to convert a torch tensor to pandas dataframe but by using pd.DataFrame I'm getting a dataframe filled with tensors instead of numeric values.

import torch
import pandas as  pd
x = torch.rand(4,4)
px = pd.DataFrame(x)
Here's what I get when clicking on px in the variable explorer:

0   1   2   3
tensor(0.3880)  tensor(0.4598)  tensor(0.4239)  tensor(0.7376)
tensor(0.4174)  tensor(0.9581)  tensor(0.0987)  tensor(0.6359)
tensor(0.6199)  tensor(0.8235)  tensor(0.9947)  tensor(0.9679)
tensor(0.7164)  tensor(0.9270)  tensor(0.7853)  tensor(0.6921)


A:

<code>
import numpy as np
import torch
import pandas as pd
x = load_data()
</code>
px = ... # 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