BUG: Passing string[pyarrow] to the dtype parameter of e.g. csv_read() does produce a string type Series

[ClauPet]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd

df = pd.read_csv("test.csv", dtype_backend = "pyarrow", dtype={"col2":"string[pyarrow]"})

df
  col1 col2
0  abc    1
1  dfg    2

df.dtypes
col1    string[pyarrow]
col2    string[pyarrow]
dtype: object

# Series of col1 shows string[pyarrow] dtype
df["col1"]
0    abc
1    dfg
Name: col1, dtype: string[pyarrow]

# Seris of col2 does NOT show string[pyarrow], but string dtype
df["col2"]
0    1
1    2
Name: col2, dtype: string

# Using ArrowDtype instead of string alias with the dtype parameter of read_csv() correctly shows string[pyarrow] as the dtype of the Series consisting of col2
df1 = pd.read_csv("test.csv", dtype_backend = "pyarrow", dtype={"col2": pd.ArrowDtype(pyarrow.string())})

df1["col2"]
0    1
1    2
Name: col2, dtype: string[pyarrow]

Issue Description

When reading a CSV with dtype_backend="pyarrow" and specifying a column as "string[pyarrow]" via the dtype parameter, the resulting Series displays as dtype: string instead of the expected string[pyarrow], even though df.dtypes shows string[pyarrow]. This inconsistency only occurs when using the string alias — using pd.ArrowDtype(pyarrow.string()) correctly preserves and displays the Arrow-backed string[pyarrow] dtype in the Series.

Expected Behavior

df1["col2"]
0 1
1 2
Name: col2, dtype: string[pyarrow]

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.11.10
python-bits : 64
OS : Linux
OS-release : 5.15.167.4-microsoft-standard-WSL2
Version : #1 SMP Tue Nov 5 00:21:55 UTC 2024
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.3
numpy : 2.2.6
pytz : 2025.2
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : None
IPython : 9.2.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 20.0.0
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.2
qtpy : None
pyqt5 : None

[JoshFlenniken]

take

[ClauPet]

Looking at this again, I am sure this is not a bug. Sorry for causing confusion. What I got confused about boils down this example:

ser = pd.Series(["a", "b"], dtype = "string[pyarrow]")

ser.dtypes
# string[pyarrow]

ser
# 0    a
# 1    b
# dtype: string

I had expected the display output of ser to also show "string[pyarrow]" as the dtype, not string. As I learned from this comment it is not the case. However, when specifying pyarrow as the dtype backend, e.g. pd.read_csv(..., dtype_backend='pyarrow'), the pyarrow-backed nullable ArrowDtype is used not the the string alias (which I did not realize).

From the documentation:

The string alias "string[pyarrow]" maps to pd.StringDtype("pyarrow") which is not equivalent to specifying dtype=pd.ArrowDtype(pa.string()). Generally, operations on the data will behave similarly except pd.StringDtype("pyarrow") can return NumPy-backed nullable types while pd.ArrowDtype(pa.string()) will return [ArrowDtype](https://pandas.pydata.org/docs/reference/api/pandas.ArrowDtype.html#pandas.ArrowDtype).

Please feel free to close this issue.