BUG: Difference between calamine and openpyxl readers - columns with mixed data types

[asishm]

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Reproducible Example

import pandas as pd
import numpy as np
from io import BytesIO
from datetime import datetime

df = pd.DataFrame({'a': ['abc', np.nan, datetime.now(), 'def']})

out = BytesIO()
df.to_excel(out, index=False)
df_openpyxl = pd.read_excel(out, engine='openpyxl')
df_calamine = pd.read_excel(out, engine='calamine')

In [49]: df_openpyxl['a'].map(type)
Out[49]:
0                  <class 'str'>
1                <class 'float'>
2    <class 'datetime.datetime'>
3                  <class 'str'>
Name: a, dtype: object

In [50]: df_calamine['a'].map(type)
Out[50]:
0                                        <class 'str'>
1                                      <class 'float'>
2    <class 'pandas._libs.tslibs.timestamps.Timesta...
3                                        <class 'str'>
Name: a, dtype: object

Issue Description

When trying to read an excel file that has mixed formats, using the calamine engine - results in an object dtype column where the value is pandas._libs.tslibs.timestamps.Timestamp and pandas._libs.tslibs.timedeltas.Timedelta whereas with the openpyxl engine, these values are datetime.datetime and datetime.timedelta instead.

The difference seems to be because the calamine implementation explicitly sets pd.Timestamp/pd.Timedelta data types

pandas/pandas/io/excel/_calamine.py

Lines 109 to 115 in 79067a7

	elif isinstance(value, date):
	return pd.Timestamp(value)
	elif isinstance(value, timedelta):
	return pd.Timedelta(value)
	elif isinstance(value, time):
	# cast needed here because Scalar doesn't include datetime.time
	return cast(Scalar, value)

whereas the openpyxl implementation uses whatever the library returns.

Expected Behavior

The individual item data types should match

Installed Versions

INSTALLED VERSIONS

commit : dd87dd3
python : 3.10.14
python-bits : 64
OS : Linux
OS-release : 4.19.128-microsoft-standard
Version : #1 SMP Tue Jun 23 12:58:10 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : C.UTF-8
LOCALE : en_US.UTF-8

pandas : 3.0.0.dev0+1113.gdd87dd3ef6
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.9.0
pip : 24.0
Cython : 3.0.10
sphinx : 7.3.7
IPython : 8.25.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
blosc : None
bottleneck : 1.3.8
fastparquet : 2024.5.0
fsspec : 2024.6.0
html5lib : 1.1
hypothesis : 6.103.0
gcsfs : 2024.6.0
jinja2 : 3.1.4
lxml.etree : 5.2.2
matplotlib : 3.8.4
numba : 0.59.1
numexpr : 2.10.0
odfpy : None
openpyxl : 3.1.2
psycopg2 : 2.9.9
pymysql : 1.4.6
pyarrow : 16.1.0
pyreadstat : 1.2.7
pytest : 8.2.1
python-calamine : None
pyxlsb : 1.0.10
s3fs : 2024.6.0
scipy : 1.13.1
sqlalchemy : 2.0.30
tables : 3.9.2
tabulate : 0.9.0
xarray : 2024.5.0
xlrd : 2.0.1
xlsxwriter : 3.1.9
zstandard : 0.19.0
tzdata : 2024.1
qtpy : None
pyqt5 : None

[asishm]

Looking at it a bit more, isinstance(pd.Timestamp(...), datetime) returns True and same with isinstance(pd.Timedelta(...), timedelta), so maybe it's moot.

[rhshadrach]

I don't see a very strong reason to prefer one over the other. When doing inference for constructors, we store these as datetime.date objects. E.g.

import datetime

print(type(pd.Series(datetime.date(2024, 3, 10)).iloc[0]))
# <class 'datetime.date'>

In addition, openpyxl has been around much longer than calamine. Both of these suggest to me we should return the Python objects.