BUG: "replace" does not work with different numpy-int types even when values are the same

Pandas version checks

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

• 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

maps = pd.Series([np.int64(0),np.int64(2),np.int64(1)]) #index-value should be mapped to series-value

# 0 0    0-->0
# 1 2    1-->2
# 2 1    2-->1

map_dict = {old:new for (old,new) in zip(maps.values,maps.index)}

map_dict == {0: 0, 2: 1, 1: 2} #True

labs = pd.Series([1,1,1,0,0,2,2,2]).astype(np.int32) #Simulate a list of observations

(labs.replace({0: 0, 2: 1, 1: 2}) == labs.replace(map_dict)).mean() #0.25

labs.replace({0: 0, 2: 1, 1: 2}) # works

# 0    2
# 1    2
# 2    2
# 3    0
# 4    0
# 5    1
# 6    1
# 7    1



labs.replace(map_dict) #doesnt work
# 0    1
# 1    1
# 2    1
# 3    0
# 4    0
# 5    2
# 6    2
# 7    2

Issue Description

Using a mapping dictionary where the keys have np.int64 as type but a series of data where the values are np.int32 then the replacement is not working.

Expected Behavior

Expected to that all integer types can be evaluated against each-other

Installed Versions

INSTALLED VERSIONS

commit : 7c48ff4409c622c582c56a5702373f726de08e96 python : 3.8.1.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.19041 machine : AMD64 processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : Danish_Denmark.1252

pandas : 1.2.5 numpy : 1.21.0 pytz : 2021.1 dateutil : 2.8.1 pip : 19.2.3 setuptools : 41.2.0 Cython : 0.29.23 pytest : None hypothesis : None sphinx : None blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : 2.9.1 (dt dec pq3 ext lo64) jinja2 : None IPython : 7.24.1 pandas_datareader: None bs4 : 4.9.3 bottleneck : None fsspec : None fastparquet : None gcsfs : None matplotlib : 3.4.2 numexpr : 2.7.3 odfpy : None openpyxl : 3.0.7 pandas_gbq : None pyarrow : 5.0.0 pyxlsb : None s3fs : None scipy : 1.7.1 sqlalchemy : 1.4.20 tables : 3.6.1 tabulate : None xarray : None xlrd : None xlwt : None numba : None

[mroeschke]

Thanks for checking. Looks like a lot of bug fixes were applied to Series.replace in the upcoming version, 1.4, which may have fixed this issue. Don’t see this case explicitly mentions so it may be good to add a unit test

[vaish-muk]

take