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

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

  • [ ] I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

df_org = pd.DataFrame({"a": [None, "two"], "b": [1, 2]}, index=[0, 1]).astype({"a": "string", "b": "Int64"})
df_update = pd.DataFrame({"a": ["one"]}, index=[0], dtype="string")

print(df_org, "\n")
print(df_org.dtypes, "\n")

print(df_update, "\n")
print(df_update.dtypes, "\n")

# update with consistent dtype
df_org.update(df_update)

print(df_org, "\n")
print(df_org.dtypes, "\n")

# dtype not conserved - string -> object

Issue Description

String dtype column is casted to object when .update is performed even when the "other" dataframe has consistent dtypes. See code example

Expected Behavior

The dtype should be preserved after the update operation, i.e., string should remain string and not casted to object.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5 python : 3.8.10.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19043 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : Norwegian Bokmål_Norway.1252 pandas : 1.3.4 numpy : 1.20.3 pytz : 2021.3 dateutil : 2.8.2 pip : 21.1.3 setuptools : 58.2.0 Cython : None pytest : 6.2.5 hypothesis : None sphinx : 4.0.2 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 3.0.2 IPython : 7.28.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : None numexpr : None odfpy : None openpyxl : None pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : None tables : None tabulate : None xarray : None xlrd : None xlwt : None numba : 0.54.0

Comment From: CloseChoice

>>> import pandas as pd
>>> df_org = pd.DataFrame({"a": [None, "two"], "b": [1, 2]}, index=[0, 1]).astype({"a": "string", "b": "Int64"})
>>> df_update = pd.DataFrame({"a": ["one"]}, index=[0], dtype="string")
>>> 
>>> print(df_org, "\n")
      a  b
0  <NA>  1
1   two  2 

>>> print(df_org.dtypes, "\n")
a    string
b     Int64
dtype: object 

>>> 
>>> print(df_update, "\n")
     a
0  one 

>>> print(df_update.dtypes, "\n")
a    string
dtype: object 

>>> 
>>> # update with consistent dtype
>>> df_org.update(df_update)
>>> 
>>> print(df_org, "\n")
     a  b
0  one  1
1  two  2 

>>> print(df_org.dtypes, "\n")
a    object
b     Int64
dtype: object

Is this a behaviour that was different in previous releases? Is this really unexpected behaviour? I guess there are a couple of situations where we cast to object without necessity.

Comment From: ali-cetin-4ss

For an outsider, this feels like update method is not aware of the "new" built-in native string data type. In practice, it's a bit confusing to unintentionally re-cast dtypes without any apparent reason.

Comment From: rpkilby

This issue seems to affect other new-style dtypes such as Int64. Additionally, I think this is the source of some update() calls generating FutureWarnings about loc/iloc usage.

Comment From: rpkilby

After digging through the issue tracker a bit further, I believe this can be closed as a duplicate of #4094.