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Reproducible Example

import pandas as pd
df3=pd.DataFrame(['01/30/2023','01/30/2023','01/30/2023'],columns=['date'])
print(df3)
df3.loc[:,"date"] = pd.to_datetime(df3["date"], errors='coerce', utc=True)
print("------var1.loc------")
print(df3.dtypes)

print("------var2------")
df3["date"] = pd.to_datetime(df3["date"], errors='coerce', utc=True)
print(df3.dtypes)

Issue Description

In version 2.0.1, when using .loc, there will be no column type conversion. In version 1.5.3, the column type always changed.

Output on version 2.0.1: date 0 01/30/2023 1 01/30/2023 2 01/30/2023 ------var1.loc------ date object dtype: object ------var2------ date datetime64[ns, UTC] dtype: object

Expected Behavior

As of version 1.5.3, the column type must always change to datetime64[ns, UTC]

Installed Versions

INSTALLED VERSIONS ------------------ commit : 37ea63d540fd27274cad6585082c91b1283f963d python : 3.11.3.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.20348 machine : AMD64 processor : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : Russian_Russia.1251 pandas : 2.0.1 numpy : 1.24.3 pytz : 2023.2 dateutil : 2.8.2 setuptools : 67.7.2 pip : 23.1.2 Cython : None pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : 3.1.0 lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : None IPython : 8.13.1 pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : None numba : None numexpr : None odfpy : None openpyxl : 3.1.2 pandas_gbq : None pyarrow : 11.0.0 pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None zstandard : None tzdata : 2023.3 qtpy : None pyqt5 : None

Comment From: shraik

similar to #52612

Comment From: rhshadrach

similar to #52612

Agreed - closing as a duplicate of #52593