# ds1000 / 823

- 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

```
# 823: DS-1000 Task

## Prompt
Problem:

I would like to break down a pandas column, which is the last column, consisting of a list of elements into as many columns as there are unique elements i.e. one-hot-encode them (with value 1 representing a given element existing in a row and 0 in the case of absence).

For example, taking dataframe df

Col1   Col2    Col3          Col4
 C      33      11       [Apple, Orange, Banana]
 A      2.5     4.5      [Apple, Grape]
 B      42      14       [Banana]
 D      666     1919810  [Suica, Orange]
I would like to convert this to:

df

Col1 Col2     Col3  Apple  Banana  Grape  Orange  Suica
C   33       11      1       1      0       1      0
A  2.5      4.5      1       0      1       0      0
B   42       14      0       1      0       0      0
D  666  1919810      0       0      0       1      1
How can I use pandas/sklearn to achieve this?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
df = load_data()
</code>
df_out = ... # 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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