# ds1000 / 246

- 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

```
# 246: DS-1000 Task

## Prompt
Problem:


I have a pandas series which values are numpy array. For simplicity, say




    series = pd.Series([np.array([1,2,3,4]), np.array([5,6,7,8]), np.array([9,10,11,12])], index=['file1', 'file2', 'file3'])


file1       [1, 2, 3, 4]
file2       [5, 6, 7, 8]
file3    [9, 10, 11, 12]


How can I expand it to a dataframe of the form df_concatenated:
       0   1   2   3
file1  1   2   3   4
file2  5   6   7   8
file3  9  10  11  12


A:
<code>
import pandas as pd
import numpy as np


series = pd.Series([np.array([1,2,3,4]), np.array([5,6,7,8]), np.array([9,10,11,12])], index=['file1', 'file2', 'file3'])
</code>
df = ... # 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`).
```
---
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