# ds1000 / 32

- 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

```
# 32: DS-1000 Task

## Prompt
Problem:
Considering a simple df:
HeaderA | HeaderB | HeaderC | HeaderX
    476      4365      457        345


Is there a way to rename all columns, for example to add to columns which don’t end with "X" and add to all columns an "X" in the head?
XHeaderAX | XHeaderBX | XHeaderCX  | XHeaderX
    476      4365      457    345


I am concatenating multiple dataframes and want to easily differentiate the columns dependent on which dataset they came from. 
Or is this the only way?
df.rename(columns={'HeaderA': 'HeaderAX'}, inplace=True)


I have over 50 column headers and ten files; so the above approach will take a long time. 
Thank You


A:
<code>
import pandas as pd


df = pd.DataFrame(
    {'HeaderA': [476],
     'HeaderB': [4365],
     'HeaderC': [457],
     "HeaderX": [345]})
</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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