Repository: modin-project/modin (python)

Issue:
BUG: Dataframe.dtypes is NaN for empty Dataframes with pyarrow dtypes
### Modin version checks

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

- [x] I have confirmed this bug exists on the latest released version of Modin.

- [x] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-main-branch).)


### Reproducible Example

```python
import modin.pandas as pd
import pyarrow as pa

df = pd.DataFrame({"A": []}, dtype=pd.ArrowDtype(pa.int64()))
print(df.dtypes["A"], type(df.dtypes["A"]))
```

### Issue Description

When creating an empty Dataframe with pyarrow dtypes, the dtype ends up as NaN.

### Expected Behavior

The returned dtypes should be the pyarrow dtype used when the Dataframe was created.

### Error Logs

<details>

```python-traceback

nan <class 'numpy.float64'>

```

</details>


### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit                : 994221012c8738cf2da5b0b2a0f07a22749e63ee
python                : 3.12.10
python-bits           : 64
OS                    : Darwin
OS-release            : 24.5.0
Version               : Darwin Kernel Version 24.5.0: Tue Apr 22 19:54:49 PDT 2025; root:xnu-11417.121.6~2/RELEASE_ARM64_T6000
machine               : arm64
processor             : arm
byteorder             : little
LC_ALL                : None
LANG                  : None
LOCALE                : en_US.UTF-8
Modin dependencies
------------------
modin                 : 0.32.0+63.g99422101.dirty
ray                   : 2.46.0
dask                  : 2025.5.0
distributed           : 2025.5.0
pandas dependencies
-------------------
pandas                : 2.2.3
numpy                 : 2.2.5
pytz                  : 2025.2
dateutil              : 2.9.0.post0
pip                   : 25.1.1
Cython                : None
sphinx                : 8.2.3
IPython               : 9.2.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : 4.13.4
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : 2024.11.0
fsspec                : 2025.3.2
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : 3.1.6
lxml.etree            : 5.4.0
matplotlib            : 3.10.3
numba                 : None
numexpr               : 2.10.2
odfpy                 : None
openpyxl              : 3.1.5
pandas_gbq            : 0.28.0
psycopg2              : 2.9.9
pymysql               : None
pyarrow               : 20.0.0
pyreadstat            : None
pytest                : 8.3.5
python-calamine       : None
pyxlsb                : None
s3fs                  : 2025.3.2
scipy                 : 1.15.2
sqlalchemy            : 2.0.40
tables                : 3.10.2
tabulate              : 0.9.0
xarray                : 2025.4.0
xlrd                  : 2.0.1
xlsxwriter            : None
zstandard             : 0.23.0
tzdata                : 2025.2
qtpy                  : None
pyqt5                 : None
</details>


Pull request:
FIX-#7569: Fix handling of pyarrow dtype and empty dataframes
<!--
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Please review the contributing docs: https://modin.readthedocs.io/en/latest/development/contributing.html
if you have questions about contributing.
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## What do these changes do?

Ensure empty Dataframes with pyarrow dtypes maintain the dtype metadata.

<!-- Please give a short brief about these changes. -->

- [x] first commit message and PR title follow format outlined [here](https://modin.readthedocs.io/en/latest/development/contributing.html#commit-message-formatting)
  > **_NOTE:_**  If you edit the PR title to match this format, you need to add another commit (even if it's empty) or amend your last commit for the CI job that checks the PR title to pick up the new PR title.
- [x] passes `flake8 modin/ asv_bench/benchmarks scripts/doc_checker.py`
- [x] passes `black --check modin/ asv_bench/benchmarks scripts/doc_checker.py`
- [x] signed commit with `git commit -s` <!-- you can amend your commit with a signature via `git commit -amend -s` -->
- [x] Resolves #7569 <!-- issue must be created for each patch -->
- [x] tests added and passing
- [x] module layout described at `docs/development/architecture.rst` is up-to-date <!-- if you have added, renamed or removed files or directories please update the documentation accordingly -->


Patch:
diff --git a/modin/core/dataframe/pandas/dataframe/dataframe.py b/modin/core/dataframe/pandas/dataframe/dataframe.py
index 22f3b320..e59a024f 100644
--- a/modin/core/dataframe/pandas/dataframe/dataframe.py
+++ b/modin/core/dataframe/pandas/dataframe/dataframe.py
@@ -174,7 +174,9 @@ class PandasDataframe(
         self._row_lengths_cache = row_lengths
         self._column_widths_cache = column_widths
         self._pandas_backend = pandas_backend
-        if pandas_backend != "pyarrow":
+        if pandas_backend != "pyarrow" or len(partitions) == 0:
+            # If the backend is pyarrow and there are no partitions, the computed dtype otherwise becomes NaN,
+            # which means we lost the dtype, so actually set it in that case
             self.set_dtypes_cache(dtypes)
         else:
             # In this case, the type precomputation may be incorrect; we need


Test patch:
diff --git a/modin/tests/pandas/dataframe/test_default.py b/modin/tests/pandas/dataframe/test_default.py
index b3efa30c..6ad89051 100644
--- a/modin/tests/pandas/dataframe/test_default.py
+++ b/modin/tests/pandas/dataframe/test_default.py
@@ -18,6 +18,7 @@ import matplotlib
 import numpy as np
 import pandas
 import pandas._libs.lib as lib
+import pyarrow as pa
 import pytest
 from numpy.testing import assert_array_equal
 
@@ -1533,3 +1534,9 @@ def test_series_does_not_warn_distributing_takes_time():
     with warnings.catch_warnings():
         warnings.filterwarnings("error", regex, UserWarning)
         pd.Series(np.random.randint(1_000_000, size=(2_400_000)))
+
+
+@pytest.mark.parametrize("dtype", [np.int64, pd.ArrowDtype(pa.int64())])
+def test_empty_df_dtypes(dtype):
+    df = pd.DataFrame({"A": []}, dtype=dtype)
+    assert df.dtypes["A"] == dtype