BUG: pd.merge fail with numpy.uintc on Windows

[Noname37486]

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 main branch of pandas.

Reproducible Example

This bug is fixed by adding np.uintc to _factorizers in pd.merge. Could someone upload it fixed please?. For me the bug is fixed by adding in line 114 of core/reshape/merge.py file: np.uintc:libhastable.UInt32Factorizer.

import numpy as np
import pandas as pd

df1 = pd.DataFrame({'a':['foo','bar'],'b':np.array([1,2], dtype=np.uintc)})
df2 = pd.DataFrame({'a':['foo','baz'],'b':np.array([3,4], dtype=np.uintc)})
df3=df1.merge(df2, how = 'outer')
print(df3)

Issue Description

Traceback (most recent call last):
File "C:\Users\noname37486\PycharmProjects\pythonProject3\main.py", line 10, in
df3=df1.merge(df2, how = 'outer')
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\frame.py", line 10832, in merge
return merge(
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 184, in merge
return op.get_result(copy=copy)
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 886, in get_result
join_index, left_indexer, right_indexer = self._get_join_info()
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 1151, in _get_join_info
(left_indexer, right_indexer) = self._get_join_indexers()
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 1125, in _get_join_indexers
return get_join_indexers(
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 1740, in get_join_indexers
zipped = zip(*mapped)
File "C:\Users\v\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 1737, in
_factorize_keys(left_keys[n], right_keys[n], sort=sort)
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 2539, in _factorize_keys
klass, lk, rk = _convert_arrays_and_get_rizer_klass(lk, rk)
File "C:\Users\noname37486\PycharmProjects\pythonProject3\venv\lib\site-packages\pandas\core\reshape\merge.py", line 2616, in _convert_arrays_and_get_rizer_klass
klass = _factorizers[lk.dtype.type]
KeyError: <class 'numpy.uintc'>

Installed Versions

INSTALLED VERSIONS

commit : d9cdd2e
python : 3.9.6.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22631
machine : AMD64
processor : Intel64 Family 6 Model 165 Stepping 2, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : es_ES.cp1252

pandas : 2.2.2
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0.post0
setuptools : 57.0.0
pip : 21.1.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[Tirthchoksi22]

I have made a pull request on this issue, kindly check

• BUG: Add np.uintc to _factorizers in merge.py to fix KeyError when merging DataFrames with uintc columns #58727

[simonjayhawkins]

xref #52451 (comment)

[rhshadrach]

From #60091 (comment)

This issue can be resolved by adding uintc similar to the code here for intc:

pandas/pandas/core/reshape/merge.py

Lines 124 to 126 in 8d2ca0b

	# See https://github.com/pandas-dev/pandas/issues/52451
	if np.intc is not np.int32:
	_factorizers[np.intc] = libhashtable.Int64Factorizer

However, we should be checking np.dtype(np.intc).itemsize when doing so and using this to determine the right dtype to map to: if it is 4 we map to libhashtable.UInt32Factorizer and if it is 8 we map to libhashtable.UInt64Factorizer.

[rhshadrach]

Resolution of this issue should also address the np.intc case with checking itemsize as well.