# swegym / pandas-dev__pandas-50170

- taskset: [swegym](https://harnessreport.com/tasks/swegym.md)
- difficulty: hard
- category: debugging
- language: 
- runnable from the site: no
- agent timeout: 3000s

## Results by harness

_none yet_

## Instruction

```
BUG: `Series` slicing can fail for extension array `Index`
### Pandas version checks

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [ ] I have confirmed this bug exists on the main branch of pandas.


### Reproducible Example

```python
import pandas as pd

s = pd.Series(range(5), index=pd.Index(range(5), dtype=int))
print(s.loc[4:5])  # works

# Fails with both `numpy`- and `pyarrow`-backed extension arrays for the index
s = pd.Series(range(5), index=pd.Index(range(5), dtype="Int64"))
print(s.loc[4:5])  # fails

s = pd.Series(range(5), index=pd.Index(range(5), dtype="int64[pyarrow]"))
print(s.loc[4:5])  # fails
```


### Issue Description

I ran into an issue where slicing a series (via `.loc`) works for when using an index with `dtype=int`, but fails when I cast the index to use either `numpy`- or `pyarrow`-backed extension arrays. 

### Expected Behavior

I think these are all valid `.loc` calls that should return the corresponding selection

### Installed Versions

<details>

```
INSTALLED VERSIONS
------------------
commit           : 8dab54d6573f7186ff0c3b6364d5e4dd635ff3e7
python           : 3.9.15.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 21.6.0
Version          : Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.5.2
numpy            : 1.23.5
pytz             : 2022.6
dateutil         : 2.8.2
setuptools       : 59.8.0
pip              : 22.3.1
Cython           : None
pytest           : 7.2.0
hypothesis       : None
sphinx           : 4.5.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 3.1.2
IPython          : 8.7.0
pandas_datareader: None
bs4              : 4.11.1
bottleneck       : None
brotli           :
fastparquet      : 2022.11.0
fsspec           : 2022.11.0
gcsfs            : None
matplotlib       : None
numba            : None
numexpr          : 2.8.3
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 10.0.1
pyreadstat       : None
pyxlsb           : None
s3fs             : 2022.11.0
scipy            : 1.9.3
snappy           :
sqlalchemy       : 1.4.44
tables           : 3.7.0
tabulate         : None
xarray           : 2022.11.0
xlrd             : None
xlwt             : None
zstandard        : None
tzdata           : None
```
</details>
```
---
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