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

[Jakobhenningjensen]

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 : 7c48ff4
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]

Could you try on a newer version of pandas? I get this on the main branch

In [7]: labs.replace({0: 0, 2: 1, 1: 2})
Out[7]:
0    2
1    2
2    2
3    0
4    0
5    1
6    1
7    1
dtype: int32

In [8]: labs.replace(map_dict)
Out[8]:
0    2
1    2
2    2
3    0
4    0
5    1
6    1
7    1
dtype: int32

[Jakobhenningjensen]

I still get the same on version 1.3.5

import numpy as np
import pandas as pd

print(pd.__version__) #1.3.5

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

[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

[anupamsingh24]

I can add few unit tests for this check.

[simonjayhawkins]

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

fixed in commit: [fb9f205] BUG: Series.setitem failing to cast numeric values (#45121)

not directly related to replace, so adding a test is probably a good idea.