BUG: Series.fillna raising with `float32` dtype when using value arg

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

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

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Code Sample, a copy-pastable example

ser = pd.Series([1.0, 2.0], dtype="float32")
val = {1: 1.01}
ser.fillna(val)

Problem description

The above code raises with TypeError: Cannot cast array data from dtype('float64') to dtype('float32') according to the rule 'safe'

Expected Output

0    1.0
1    2.0
dtype: float32

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 343ac2a4179fdd1cb24633e9b95cbec33371c14d python : 3.8.10.final.0 python-bits : 64 OS : Darwin OS-release : 20.4.0 Version : Darwin Kernel Version 20.4.0: Thu Apr 22 21:46:47 PDT 2021; root:xnu-7195.101.2~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : None LOCALE : None.UTF-8

pandas : 1.4.0.dev0+599.g343ac2a417.dirty numpy : 1.21.1 pytz : 2021.1 dateutil : 2.8.2 pip : 21.2.1 setuptools : 49.6.0.post20210108 Cython : 0.29.24 pytest : 6.2.4 hypothesis : 6.14.3 sphinx : 3.5.4 blosc : None feather : None xlsxwriter : 1.4.4 lxml.etree : 4.6.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.0.1 IPython : 7.25.0 pandas_datareader: None bs4 : 4.9.3 bottleneck : 1.3.2 fsspec : 2021.05.0 fastparquet : 0.6.3 gcsfs : 2021.05.0 matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 4.0.1 pyxlsb : None s3fs : 0.4.2 scipy : 1.7.0 sqlalchemy : 1.4.22 tables : 3.6.1 tabulate : 0.8.9 xarray : 0.19.0 xlrd : 2.0.1 xlwt : 1.3.0 numba : 0.53.1

[simonjayhawkins]

A nice patch would seem to be

my comment was more to indicate that the issue is a regression rather than suggesting that the old code is a better fix. Not all users upgrade on every pandas release, so if this is fixed, we could backport and may save some users some pain. (but have not milestoned 1.3.x since it is an “old” regression)

[jbrockmendel]

Not sure if I follow the whole thread, but if changing a np.putmask usage to a __setitem__ usage fixes it, thats fine. We made an effort to use np.putmask where possible a while back bc it improved perf in dtlike comparisons