BUG: Invalid format to to_datetime with errors='coerce' doesn't raise

[MarcoGorelli]

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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

In [2]: pd.to_datetime("00:01:18", format='H%:M%:S%')  # raises, as expected
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File ~/pandas-dev/pandas/_libs/tslibs/strptime.pyx:136, in pandas._libs.tslibs.strptime.array_strptime()
    135 try:
--> 136     format_regex = _TimeRE_cache.compile(fmt)
    137 # KeyError raised when a bad format is found; can be specified as

File ~/mambaforge/envs/pandas-dev/lib/python3.8/_strptime.py:263, in TimeRE.compile(self, format)
    262 """Return a compiled re object for the format string."""
--> 263 return re_compile(self.pattern(format), IGNORECASE)

File ~/mambaforge/envs/pandas-dev/lib/python3.8/_strptime.py:257, in TimeRE.pattern(self, format)
    254 directive_index = format.index('%')+1
    255 processed_format = "%s%s%s" % (processed_format,
    256                                format[:directive_index-1],
--> 257                                self[format[directive_index]])
    258 format = format[directive_index+1:]

File ~/pandas-dev/pandas/_libs/tslibs/strptime.pyx:461, in pandas._libs.tslibs.strptime.TimeRE.__getitem__()
    460     return self._Z
--> 461 return super().__getitem__(key)
    462 

KeyError: ':'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 pd.to_datetime("00:01:18", format='H%:M%:S%')

File ~/pandas-dev/pandas/core/tools/datetimes.py:1100, in to_datetime(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)
   1098         result = convert_listlike(argc, format)
   1099 else:
-> 1100     result = convert_listlike(np.array([arg]), format)[0]
   1101     if isinstance(arg, bool) and isinstance(result, np.bool_):
   1102         result = bool(result)  # TODO: avoid this kludge.

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:126, in pandas._libs.tslibs.strptime.array_strptime()
    124 
    125     global _TimeRE_cache, _regex_cache
--> 126     with _cache_lock:
    127         if _getlang() != _TimeRE_cache.locale_time.lang:
    128             _TimeRE_cache = TimeRE()

File ~/pandas-dev/pandas/_libs/tslibs/strptime.pyx:144, in pandas._libs.tslibs.strptime.array_strptime()
    142         bad_directive = "%"
    143     del err
--> 144     raise ValueError(f"'{bad_directive}' is a bad directive "
    145                      f"in format '{fmt}'")
    146 # IndexError only occurs when the format string is "%"

ValueError: ':' is a bad directive in format 'H%:M%:S%'

In [3]: pd.to_datetime("00:01:18", format='H%:M%:S%', errors='coerce')  # doesn't raise
Out[3]: NaT

Issue Description

The issue with this overly broad try-except:

pandas/pandas/core/tools/datetimes.py

Lines 484 to 505 in 1d5f05c

	try:
	result, timezones = array_strptime(
	arg, fmt, exact=exact, errors=errors, utc=utc
	)
	except OutOfBoundsDatetime:
	if errors == "raise":
	raise
	if errors == "coerce":
	result = np.empty(arg.shape, dtype="M8[ns]")
	iresult = result.view("i8")
	iresult.fill(iNaT)
	else:
	result = arg
	except ValueError:
	if errors == "raise":
	raise
	if errors == "coerce":
	result = np.empty(arg.shape, dtype="M8[ns]")
	iresult = result.view("i8")
	iresult.fill(iNaT)
	else:
	result = arg

The try-except should happen within the loop, as is done in tslib.pyx:

pandas/pandas/_libs/tslib.pyx

Lines 528 to 531 in 6598797

	for i in range(n):
	val = values[i]
	try:

Related: #24763, but I really disagree that this is desirable behaviour

Expected Behavior

pd.to_datetime("00:01:18", format='H%:M%:S%', errors='coerce') should still raise

errors='coerce' controls what happens during parsing - here, however, the error happens before the parsing even begins, because the format is invalid

Installed Versions

INSTALLED VERSIONS

commit : a5cbd1e
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+923.ga5cbd1e52e
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