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### 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 os import pandas as pd os.environ["MODIN_ENGINE"] = "ray" import modin.pandas as md pd_t0 = pd.read_csv("t0.csv") md_t0 = md.read_csv("t0.csv") print("Pandas:") print(pd_t0) print(pd_t0['c0'].apply(type)) print("Modin ray:") print(md_t0) print(md_t0['c0'].apply(type)) ``` t0.csv: ```bash c0 128 0.25531019349575323 1969-12-08 ``` ```bash Pandas: c0 0 128 1 0.25531019349575323 2 1969-12-08 0 <class 'str'> 1 <class 'str'> 2 <class 'str'> Name: c0, dtype: object Modin ray: c0 0 128.0 1 0.25531 2 1969-12-08 0 <class 'float'> 1 <class 'float'> 2 <class 'str'> Name: c0, dtype: object ``` ### Issue Description I found that Modin's data types differ from those of Pandas when reading from csv files. Both Modin's `ray` and `dask` engines may appear in the above situation. I wonder whether Modin should be consistent with Pandas. ### Expected Behavior Modin's data tpyes are the same with Pandas's when reading from csv files. ### Error Logs <details> ```python-traceback None ``` </details> ### Installed Versions <details> INSTALLED VERSIONS ------------------ commit : bce3707443d525cdca5c50f9c5e65f2f82fcc882 python : 3.10.19 python-bits : 64 OS : Linux OS-release : 6.14.0-35-generic Version : #35~24.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 13:55:17 UTC 2 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 Modin dependencies ------------------ modin : 0.37.1 ray : 2.51.1 dask : 2025.11.0 distributed : 2025.11.0 pandas dependencies ------------------- pandas : 2.3.3 numpy : 1.26.4 pytz : 2025.2 dateutil : 2.9.0.post0 pip : 25.3 Cython : None sphinx : None IPython : 8.27.0 adbc-driver-postgresql: None adbc-driver-sqlite : None bs4 : 4.14.2 blosc : None bottleneck : None dataframe-api-compat : None fastparquet : None fsspec : 2025.10.0 html5lib : None hypothesis : None gcsfs : None jinja2 : 3.1.6 lxml.etree : None matplotlib : 3.10.7 numba : 0.61.2 numexpr : None odfpy : None openpyxl : None pandas_gbq : None psycopg2 : None pymysql : None pyarrow : 22.0.0 pyreadstat : None pytest : None python-calamine : None pyxlsb : None s3fs : None scipy : 1.15.3 sqlalchemy : 2.0.44 tables : None tabulate : 0.9.0 xarray : 2025.6.1 xlrd : None xlsxwriter : None zstandard : 0.25.0 tzdata : 2025.2 qtpy : None pyqt5 : None </details>