REGR: to_datetime with non-ISO format, float, and nan fails on main

[MarcoGorelli]

Pandas version checks

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• 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

ser = Series([198012, 198012] + [198101] * 5)
ser[2] = np.nan

result = to_datetime(ser, format="%Y%m")

Issue Description

This gives

File ~/pandas-dev/pandas/core/tools/datetimes.py:1066, in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
   1064         result = arg.map(cache_array)
   1065     else:
-> 1066         values = convert_listlike(arg._values, format)
   1067         result = arg._constructor(values, index=arg.index, name=arg.name)
   1068 elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):

File ~/pandas-dev/pandas/core/tools/datetimes.py:442, in _convert_listlike_datetimes(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)
    439 require_iso8601 = format is not None and format_is_iso(format)
    441 if format is not None and not require_iso8601:
--> 442     return _to_datetime_with_format(
    443         arg,
    444         orig_arg,
    445         name,
    446         utc,
    447         format,
    448         exact,
    449         errors,
    450     )
    452 result, tz_parsed = objects_to_datetime64ns(
    453     arg,
    454     dayfirst=dayfirst,
   (...)
    461     exact=exact,
    462 )
    464 if tz_parsed is not None:
    465     # We can take a shortcut since the datetime64 numpy array
    466     # is in UTC

File ~/pandas-dev/pandas/core/tools/datetimes.py:543, in _to_datetime_with_format(arg, orig_arg, name, utc, fmt, exact, errors)
    540         return _box_as_indexlike(result, utc=utc, name=name)
    542 # fallback
--> 543 res = _array_strptime_with_fallback(arg, name, utc, fmt, exact, errors)
    544 return res

File ~/pandas-dev/pandas/core/tools/datetimes.py:485, in _array_strptime_with_fallback(arg, name, utc, fmt, exact, errors)
    481 """
    482 Call array_strptime, with fallback behavior depending on 'errors'.
    483 """
    484 try:
--> 485     result, timezones = array_strptime(
    486         arg, fmt, exact=exact, errors=errors, utc=utc
    487     )
    488 except OutOfBoundsDatetime:
    489     if errors == "raise":

File ~/pandas-dev/pandas/_libs/tslibs/strptime.pyx:198, in pandas._libs.tslibs.strptime.array_strptime()
    196         iresult[i] = NPY_NAT
    197         continue
--> 198     raise ValueError(f"time data '{val}' does not match "
    199                      f"format '{fmt}' (match)")
    200 if len(val) != found.end():

ValueError: time data '-9223372036854775808' does not match format '%Y%m' (match)

Expected Behavior

I think I'd still expect it to fail, because 198012.0 doesn't match '%Y%m'. But

ValueError: time data '-9223372036854775808' does not match format '%Y%m' (match)

does look quite mysterious, and not expected

---

From git bisect, this was caused by #49361 (cc @jbrockmendel sorry for yet another ping!)

https://www.kaggle.com/code/marcogorelli/pandas-regression-example?scriptVersionId=113733639

Labelling as regression as it works on 1.5.2:

In [2]: ser = Series([198012, 198012] + [198101] * 5)
   ...: ser[2] = np.nan
   ...:
   ...: result = to_datetime(ser, format="%Y%m")

In [3]: result
Out[3]:
0   1980-12-01
1   1980-12-01
2          NaT
3   1981-01-01
4   1981-01-01
5   1981-01-01
6   1981-01-01
dtype: datetime64[ns]

Installed Versions

INSTALLED VERSIONS

commit : 749d59d
python : 3.8.15.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.102.1-microsoft-standard-WSL2
Version : #1 SMP Wed Mar 2 00:30:59 UTC 2022
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 2.0.0.dev0+915.g749d59db6e
numpy : 1.23.5
pytz : 2022.6
dateutil : 2.8.2
setuptools : 65.5.1
pip : 22.3.1
Cython : 0.29.32
pytest : 7.2.0
hypothesis : 6.61.0
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : 3.0.3
lxml.etree : 4.9.1
html5lib : 1.1
pymysql : 1.0.2
psycopg2 : 2.9.3
jinja2 : 3.1.2
IPython : 8.7.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : 1.3.5
brotli :
fastparquet : 2022.12.0
fsspec : 2021.11.0
gcsfs : 2021.11.0
matplotlib : 3.6.2
numba : 0.56.4
numexpr : 2.8.3
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : 9.0.0
pyreadstat : 1.2.0
pyxlsb : 1.0.10
s3fs : 2021.11.0
scipy : 1.9.3
snappy :
sqlalchemy : 1.4.45
tables : 3.7.0
tabulate : 0.9.0
xarray : 2022.12.0
xlrd : 2.0.1
zstandard : 0.19.0
tzdata : None
qtpy : None
pyqt5 : None
None