Repository: pandas-dev/pandas (python)

Issue:
BUG: to_datetime behaves differently depending of the format of the string provided
### Pandas version checks

- [ ] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.


### Reproducible Example

```python
Python 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.to_datetime("2016-03-11", dayfirst=True)
Timestamp('2016-11-03 00:00:00')
>>> pd.to_datetime("20160311", dayfirst=True)
Timestamp('2016-03-11 00:00:00')
>>>
```


### Issue Description

Dates of the format "YYYYDDMM" are not converted correctly by to_datetime() when dayfirst=True. to_datetime() still reads the date characters as the months

### Expected Behavior

Python 3.11.7 | packaged by conda-forge | (main, Dec 23 2023, 14:43:09) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> pd.to_datetime("2016-03-11", dayfirst=True)
Timestamp('2016-11-03 00:00:00')
>>> pd.to_datetime("20160311", dayfirst=True)
Timestamp('2016-11-03 00:00:00')

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit              : a671b5a8bf5dd13fb19f0e88edc679bc9e15c673
python              : 3.11.7.final.0
python-bits         : 64
OS                  : Linux
OS-release          : 3.10.0-1160.114.2.el7.x86_64
Version             : #1 SMP Sun Mar 3 08:18:39 EST 2024
machine             : x86_64
processor           : x86_64
byteorder           : little
LC_ALL              : None
LANG                : en_US.UTF-8
LOCALE              : en_US.UTF-8

pandas              : 2.1.4
numpy               : 1.26.3
pytz                : 2023.3.post1
dateutil            : 2.8.2
setuptools          : 69.0.3
pip                 : 23.3.2
Cython              : 3.0.7
pytest              : 7.4.4
hypothesis          : 6.94.0
sphinx              : 7.2.6
blosc               : None
feather             : None
xlsxwriter          : 3.1.9
lxml.etree          : 5.1.0
html5lib            : 1.1
pymysql             : None
psycopg2            : None
jinja2              : 3.1.3
IPython             : 8.20.0
pandas_datareader   : None
bs4                 : 4.12.2
bottleneck          : 1.3.7
dataframe-api-compat: None
fastparquet         : 2023.10.1
fsspec              : 2023.12.2
gcsfs               : None
matplotlib          : 3.8.2
numba               : 0.58.1
numexpr             : 2.8.8
odfpy               : None
openpyxl            : 3.1.2
pandas_gbq          : None
pyarrow             : 16.0.0
pyreadstat          : None
pyxlsb              : None
s3fs                : None
scipy               : 1.11.4
sqlalchemy          : 2.0.25
tables              : 3.9.2
tabulate            : 0.9.0
xarray              : 2023.7.0
xlrd                : 2.0.1
zstandard           : 0.19.0
tzdata              : 2023.4
qtpy                : 2.4.1
pyqt5               : None

</details>


Pull request description:
BUG: Fix to_datetime not respecting dayfirst 
- [x] closes #58859
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.


Patch:
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 255e680d8d..59cbc075b5 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -70,10 +70,8 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         --format=actions \
         -i ES01 `# For now it is ok if docstrings are missing the extended summary` \
         -i "pandas.Series.dt PR01" `# Accessors are implemented as classes, but we do not document the Parameters section` \
-        -i "pandas.Grouper PR02" \
         -i "pandas.MultiIndex.append PR07,SA01" \
         -i "pandas.MultiIndex.copy PR07,RT03,SA01" \
-        -i "pandas.MultiIndex.drop PR07,RT03,SA01" \
         -i "pandas.MultiIndex.get_level_values SA01" \
         -i "pandas.MultiIndex.get_loc PR07" \
         -i "pandas.MultiIndex.get_loc_level PR07" \
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index cc7706741e..ba6636cb42 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -509,6 +509,7 @@ Datetimelike
 - Bug in :meth:`DatetimeIndex.is_year_start` and :meth:`DatetimeIndex.is_quarter_start` returning ``False`` on double-digit frequencies (:issue:`58523`)
 - Bug in :meth:`DatetimeIndex.union` when ``unit`` was non-nanosecond (:issue:`59036`)
 - Bug in :meth:`Series.dt.microsecond` producing incorrect results for pyarrow backed :class:`Series`. (:issue:`59154`)
+- Bug in :meth:`to_datetime` not respecting dayfirst if an uncommon date string was passed. (:issue:`58859`)
 - Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)
 
 Timedelta
diff --git a/pandas/_libs/tslibs/conversion.pyx b/pandas/_libs/tslibs/conversion.pyx
index 3a55f5fa0c..0fadbbbed2 100644
--- a/pandas/_libs/tslibs/conversion.pyx
+++ b/pandas/_libs/tslibs/conversion.pyx
@@ -606,37 +606,42 @@ cdef _TSObject convert_str_to_tsobject(str ts, tzinfo tz,
         # equiv: datetime.today().replace(tzinfo=tz)
         return convert_datetime_to_tsobject(dt, tz, nanos=0, reso=NPY_FR_us)
     else:
-        string_to_dts_failed = string_to_dts(
-            ts, &dts, &out_bestunit, &out_local,
-            &out_tzoffset, False
-        )
-        if not string_to_dts_failed:
-            reso = get_supported_reso(out_bestunit)
-            check_dts_bounds(&dts, reso)
-            obj = _TSObject()
-            obj.dts = dts
-            obj.creso = reso
-            ival = npy_datetimestruct_to_datetime(reso, &dts)
-
-            if out_local == 1:
-                obj.tzinfo = timezone(timedelta(minutes=out_tzoffset))
-                obj.value = tz_localize_to_utc_single(
-                    ival, obj.tzinfo, ambiguous="raise", nonexistent=None, creso=reso
-                )
-                if tz is None:
-                    check_overflows(obj, reso)
-                    return obj
-                _adjust_tsobject_tz_using_offset(obj, tz)
-                return  obj
-            else:
-                if tz is not None:
-                    # shift for _localize_tso
-                    ival = tz_localize_to_utc_single(
-                        ival, tz, ambiguous="raise", nonexistent=None, creso=reso
+        if not dayfirst:  # GH 58859
+            string_to_dts_failed = string_to_dts(
+                ts, &dts, &out_bestunit, &out_local,
+                &out_tzoffset, False
+            )
+            if not string_to_dts_failed:
+                reso = get_supported_reso(out_bestunit)
+                check_dts_bounds(&dts, reso)
+                obj = _TSObject()
+                obj.dts = dts
+                obj.creso = reso
+                ival = npy_datetimestruct_to_datetime(reso, &dts)
+
+                if out_local == 1:
+                    obj.tzinfo = timezone(timedelta(minutes=out_tzoffset))
+                    obj.value = tz_localize_to_utc_single(
+                        ival,
+                        obj.tzinfo,
+                        ambiguous="raise",
+                        nonexistent=None,
+                        creso=reso,
                     )
-                obj.value = ival
-                maybe_localize_tso(obj, tz, obj.creso)
-                return obj
+                    if tz is None:
+                        check_overflows(obj, reso)
+                        return obj
+                    _adjust_tsobject_tz_using_offset(obj, tz)
+                    return  obj
+                else:
+                    if tz is not None:
+                        # shift for _localize_tso
+                        ival = tz_localize_to_utc_single(
+                            ival, tz, ambiguous="raise", nonexistent=None, creso=reso
+                        )
+                    obj.value = ival
+                    maybe_localize_tso(obj, tz, obj.creso)
+                    return obj
 
         dt = parse_datetime_string(
             ts,
diff --git a/pandas/_libs/tslibs/parsing.pyx b/pandas/_libs/tslibs/parsing.pyx
index 35d2433a70..3081834021 100644
--- a/pandas/_libs/tslibs/parsing.pyx
+++ b/pandas/_libs/tslibs/parsing.pyx
@@ -377,32 +377,33 @@ def parse_datetime_string_with_reso(
         raise ValueError(f'Given date string "{date_string}" not likely a datetime')
 
     # Try iso8601 first, as it handles nanoseconds
-    string_to_dts_failed = string_to_dts(
-        date_string, &dts, &out_bestunit, &out_local,
-        &out_tzoffset, False
-    )
-    if not string_to_dts_failed:
-        # Match Timestamp and drop picoseconds, femtoseconds, attoseconds
-        # The new resolution will just be nano
-        # GH#50417
-        if out_bestunit in _timestamp_units:
-            out_bestunit = NPY_DATETIMEUNIT.NPY_FR_ns
-
-        if out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns:
-            # TODO: avoid circular import
-            from pandas import Timestamp
-            parsed = Timestamp(date_string)
-        else:
-            if out_local:
-                tz = timezone(timedelta(minutes=out_tzoffset))
+    if not dayfirst:  # GH 58859
+        string_to_dts_failed = string_to_dts(
+            date_string, &dts, &out_bestunit, &out_local,
+            &out_tzoffset, False
+        )
+        if not string_to_dts_failed:
+            # Match Timestamp and drop picoseconds, femtoseconds, attoseconds
+            # The new resolution will just be nano
+            # GH#50417
+            if out_bestunit in _timestamp_units:
+                out_bestunit = NPY_DATETIMEUNIT.NPY_FR_ns
+
+            if out_bestunit == NPY_DATETIMEUNIT.NPY_FR_ns:
+                # TODO: avoid circular import
+                from pandas import Timestamp
+                parsed = Timestamp(date_string)
             else:
-                tz = None
-            parsed = datetime_new(
-                dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz
-            )
+                if out_local:
+                    tz = timezone(timedelta(minutes=out_tzoffset))
+                else:
+                    tz = None
+                parsed = datetime_new(
+                    dts.year, dts.month, dts.day, dts.hour, dts.min, dts.sec, dts.us, tz
+                )
 
-        reso = npy_unit_to_attrname[out_bestunit]
-        return parsed, reso
+            reso = npy_unit_to_attrname[out_bestunit]
+            return parsed, reso
 
     parsed = _parse_delimited_date(date_string, dayfirst, &out_bestunit)
     if parsed is not None:
diff --git a/pandas/core/groupby/grouper.py b/pandas/core/groupby/grouper.py
index 5f680de776..5f9ebdcea4 100644
--- a/pandas/core/groupby/grouper.py
+++ b/pandas/core/groupby/grouper.py
@@ -72,6 +72,9 @@ class Grouper:
         Currently unused, reserved for future use.
     **kwargs
         Dictionary of the keyword arguments to pass to Grouper.
+
+    Attributes
+    ----------
     key : str, defaults to None
         Groupby key, which selects the grouping column of the target.
     level : name/number, defaults to None
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 19c94fa410..ee24e485a9 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -2316,16 +2316,32 @@ class MultiIndex(Index):
         """
         Make a new :class:`pandas.MultiIndex` with the passed list of codes deleted.
 
+        This method allows for the removal of specified labels from a MultiIndex.
+        The labels to be removed can be provided as a list of tuples if no level
+        is specified, or as a list of labels from a specific level if the level
+        parameter is provided. This can be useful for refining the structure of a
+        MultiIndex to fit specific requirements.
+
         Parameters
         ----------
         codes : array-like
             Must be a list of tuples when ``level`` is not specified.
         level : int or level name, default None
+            Level from which the labels will be dropped.
         errors : str, default 'raise'
+            If 'ignore', suppress error and existing labels are dropped.
 
         Returns
         -------
         MultiIndex
+            A new MultiIndex with the specified labels removed.
+
+        See Also
+        --------
+        MultiIndex.remove_unused_levels : Create new MultiIndex from current that
+            removes unused levels.
+        MultiIndex.reorder_levels : Rearrange levels using input order.
+        MultiIndex.rename : Rename levels in a MultiIndex.
 
         Examples
         --------


Test patch:
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index c1d6baaf17..3a47d87286 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -2988,6 +2988,8 @@ class TestDatetimeParsingWrappers:
             ("20/12/21", True, False, datetime(2021, 12, 20)),
             ("20/12/21", False, True, datetime(2020, 12, 21)),
             ("20/12/21", True, True, datetime(2020, 12, 21)),
+            # GH 58859
+            ("20201012", True, False, datetime(2020, 12, 10)),
         ],
     )
     def test_parsers_dayfirst_yearfirst(