Repository: pandas-dev/pandas (python)

Issue:
BUG: pandas.to_datetime produces wrong/strange results on 32-bit float data for 6-column format
### Pandas version checks

- [X] 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
from datetime import datetime, UTC
import pandas as pd

start = datetime(2024, 1, 1)
end = datetime(2025, 1, 1)
samples = 10


df = pd.DataFrame([
    [  2024,  1,  7, 11, 42, 13],
    [  2024,  9, 19, 11, 54, 20],
    [  2024,  9, 17,  1, 22,  0],
    [  2024,  1, 24, 21, 59, 55],
    [  2024,  6, 15, 12, 27, 30],
    [  2024,  9, 26, 23, 58, 26],
    [  2024,  6,  6,  0, 19, 59],
    [  2024,  1,  8,  2,  7, 43],
    [  2024,  2, 16, 16, 20, 13],
    [  2024, 12, 22, 23, 54,  4]])

df.columns = ['year', 'month', 'day', 'hour', 'minute', 'second']


ts = pd.to_datetime(df, utc=True)
ts32 = pd.to_datetime(df.astype('float32'), utc=True)
ts64 = pd.to_datetime(df.astype('float64'), utc=True)

print (ts - ts32)

assert ts.equals(ts64)
assert ts.equals(ts32)
```


### Issue Description

When constructing datetime from 6-column format, and the data is stored at 32-bit floats pandas.to_datetime silently produces strange (off by one day) results. 
pandas.to_datetime should either produce correct results or throw an Exception. Correct results would be preferred :)

### Expected Behavior

from datetime import datetime, UTC
import pandas as pd

start = datetime(2024, 1, 1)
end = datetime(2025, 1, 1)
samples = 10


df = pd.DataFrame([
    [  2024,  1,  7, 11, 42, 13],
    [  2024,  9, 19, 11, 54, 20],
    [  2024,  9, 17,  1, 22,  0],
    [  2024,  1, 24, 21, 59, 55],
    [  2024,  6, 15, 12, 27, 30],
    [  2024,  9, 26, 23, 58, 26],
    [  2024,  6,  6,  0, 19, 59],
    [  2024,  1,  8,  2,  7, 43],
    [  2024,  2, 16, 16, 20, 13],
    [  2024, 12, 22, 23, 54,  4]])

df.columns = ['year', 'month', 'day', 'hour', 'minute', 'second']


ts = pd.to_datetime(df, utc=True)
ts32 = pd.to_datetime(df.astype('float32'), utc=True)
ts64 = pd.to_datetime(df.astype('float64'), utc=True)

print (ts - ts32)

assert ts.equals(ts64)
assert ts.equals(ts32)


### Installed Versions

<details>
INSTALLED VERSIONS
------------------
commit                : 0691c5cf90477d3503834d983f69350f250a6ff7
python                : 3.12.7
python-bits           : 64
OS                    : Windows
OS-release            : 10
Version               : 10.0.19045
machine               : AMD64
processor             : Intel64 Family 6 Model 186 Stepping 2, GenuineIntel
byteorder             : little
LC_ALL                : None
LANG                  : en_US.UTF-8
LOCALE                : English_Denmark.1252

pandas                : 2.2.3
numpy                 : 2.1.2
pytz                  : 2024.2
dateutil              : 2.9.0.post0
pip                   : 24.3.1
Cython                : None
sphinx                : None
IPython               : 8.28.0
adbc-driver-postgresql: None
adbc-driver-sqlite    : None
bs4                   : None
blosc                 : None
bottleneck            : None
dataframe-api-compat  : None
fastparquet           : None
fsspec                : None
html5lib              : None
hypothesis            : None
gcsfs                 : None
jinja2                : None
lxml.etree            : None
matplotlib            : None
numba                 : None
numexpr               : None
odfpy                 : None
openpyxl              : None
pandas_gbq            : None
psycopg2              : None
pymysql               : None
pyarrow               : 17.0.0
pyreadstat            : None
pytest                : 8.3.3
python-calamine       : None
pyxlsb                : None
s3fs                  : None
scipy                 : None
sqlalchemy            : None
tables                : None
tabulate              : None
xarray                : None
xlrd                  : None
xlsxwriter            : None
zstandard             : None
tzdata                : 2024.2
qtpy                  : None
pyqt5                 : None

</details>


Pull request description:
BUG: Fix float32 precision issues in pd.to_datetime
- [x] closes #60506(Replace xxxx with the GitHub issue number)
- [x] [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
- [x] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [x] 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 adc5bc9a01..7bc220acdd 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -95,9 +95,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.core.resample.Resampler.std SA01" \
         -i "pandas.core.resample.Resampler.transform PR01,RT03,SA01" \
         -i "pandas.core.resample.Resampler.var SA01" \
-        -i "pandas.errors.NullFrequencyError SA01" \
-        -i "pandas.errors.NumbaUtilError SA01" \
-        -i "pandas.errors.PerformanceWarning SA01" \
         -i "pandas.errors.UndefinedVariableError PR01,SA01" \
         -i "pandas.errors.ValueLabelTypeMismatch SA01" \
         -i "pandas.io.json.build_table_schema PR07,RT03,SA01" \
diff --git a/doc/source/reference/frame.rst b/doc/source/reference/frame.rst
index 7680c8b434..e701d48a89 100644
--- a/doc/source/reference/frame.rst
+++ b/doc/source/reference/frame.rst
@@ -185,7 +185,6 @@ Reindexing / selection / label manipulation
    DataFrame.duplicated
    DataFrame.equals
    DataFrame.filter
-   DataFrame.head
    DataFrame.idxmax
    DataFrame.idxmin
    DataFrame.reindex
@@ -196,7 +195,6 @@ Reindexing / selection / label manipulation
    DataFrame.sample
    DataFrame.set_axis
    DataFrame.set_index
-   DataFrame.tail
    DataFrame.take
    DataFrame.truncate
 
diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index ab5746eca1..2013f81d4d 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -626,6 +626,7 @@ Datetimelike
 - Bug in :meth:`DatetimeIndex.union` and :meth:`DatetimeIndex.intersection` 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 :meth:`to_datetime` on float32 df with year, month, day etc. columns leads to precision issues and incorrect result. (:issue:`60506`)
 - Bug in :meth:`to_datetime` reports incorrect index in case of any failure scenario. (:issue:`58298`)
 - Bug in :meth:`to_datetime` wrongly converts when ``arg`` is a ``np.datetime64`` object with unit of ``ps``. (:issue:`60341`)
 - Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)
@@ -798,6 +799,7 @@ Other
 - Bug in :meth:`read_csv` where chained fsspec TAR file and ``compression="infer"`` fails with ``tarfile.ReadError`` (:issue:`60028`)
 - Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)
 - Bug in ``Series.list`` methods not preserving the original :class:`Index`. (:issue:`58425`)
+- Bug in ``Series.list`` methods not preserving the original name. (:issue:`60522`)
 - Bug in printing a :class:`DataFrame` with a :class:`DataFrame` stored in :attr:`DataFrame.attrs` raised a ``ValueError`` (:issue:`60455`)
 
 .. ***DO NOT USE THIS SECTION***
diff --git a/pandas/core/arrays/arrow/accessors.py b/pandas/core/arrays/arrow/accessors.py
index 230522846d..b220a94d03 100644
--- a/pandas/core/arrays/arrow/accessors.py
+++ b/pandas/core/arrays/arrow/accessors.py
@@ -117,7 +117,10 @@ class ListAccessor(ArrowAccessor):
 
         value_lengths = pc.list_value_length(self._pa_array)
         return Series(
-            value_lengths, dtype=ArrowDtype(value_lengths.type), index=self._data.index
+            value_lengths,
+            dtype=ArrowDtype(value_lengths.type),
+            index=self._data.index,
+            name=self._data.name,
         )
 
     def __getitem__(self, key: int | slice) -> Series:
@@ -162,7 +165,10 @@ class ListAccessor(ArrowAccessor):
             #     key = pc.add(key, pc.list_value_length(self._pa_array))
             element = pc.list_element(self._pa_array, key)
             return Series(
-                element, dtype=ArrowDtype(element.type), index=self._data.index
+                element,
+                dtype=ArrowDtype(element.type),
+                index=self._data.index,
+                name=self._data.name,
             )
         elif isinstance(key, slice):
             if pa_version_under11p0:
@@ -181,7 +187,12 @@ class ListAccessor(ArrowAccessor):
             if step is None:
                 step = 1
             sliced = pc.list_slice(self._pa_array, start, stop, step)
-            return Series(sliced, dtype=ArrowDtype(sliced.type), index=self._data.index)
+            return Series(
+                sliced,
+                dtype=ArrowDtype(sliced.type),
+                index=self._data.index,
+                name=self._data.name,
+            )
         else:
             raise ValueError(f"key must be an int or slice, got {type(key).__name__}")
 
@@ -223,7 +234,12 @@ class ListAccessor(ArrowAccessor):
         counts = pa.compute.list_value_length(self._pa_array)
         flattened = pa.compute.list_flatten(self._pa_array)
         index = self._data.index.repeat(counts.fill_null(pa.scalar(0, counts.type)))
-        return Series(flattened, dtype=ArrowDtype(flattened.type), index=index)
+        return Series(
+            flattened,
+            dtype=ArrowDtype(flattened.type),
+            index=index,
+            name=self._data.name,
+        )
 
 
 class StructAccessor(ArrowAccessor):
diff --git a/pandas/core/computation/expressions.py b/pandas/core/computation/expressions.py
index e2acd9a2c9..a2c3a706ae 100644
--- a/pandas/core/computation/expressions.py
+++ b/pandas/core/computation/expressions.py
@@ -65,23 +65,23 @@ def set_numexpr_threads(n=None) -> None:
         ne.set_num_threads(n)
 
 
-def _evaluate_standard(op, op_str, a, b):
+def _evaluate_standard(op, op_str, left_op, right_op):
     """
     Standard evaluation.
     """
     if _TEST_MODE:
         _store_test_result(False)
-    return op(a, b)
+    return op(left_op, right_op)
 
 
-def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
-    """return a boolean if we WILL be using numexpr"""
+def _can_use_numexpr(op, op_str, left_op, right_op, dtype_check) -> bool:
+    """return left_op boolean if we WILL be using numexpr"""
     if op_str is not None:
         # required min elements (otherwise we are adding overhead)
-        if a.size > _MIN_ELEMENTS:
+        if left_op.size > _MIN_ELEMENTS:
             # check for dtype compatibility
             dtypes: set[str] = set()
-            for o in [a, b]:
+            for o in [left_op, right_op]:
                 # ndarray and Series Case
                 if hasattr(o, "dtype"):
                     dtypes |= {o.dtype.name}
@@ -93,22 +93,22 @@ def _can_use_numexpr(op, op_str, a, b, dtype_check) -> bool:
     return False
 
 
-def _evaluate_numexpr(op, op_str, a, b):
+def _evaluate_numexpr(op, op_str, left_op, right_op):
     result = None
 
-    if _can_use_numexpr(op, op_str, a, b, "evaluate"):
+    if _can_use_numexpr(op, op_str, left_op, right_op, "evaluate"):
         is_reversed = op.__name__.strip("_").startswith("r")
         if is_reversed:
             # we were originally called by a reversed op method
-            a, b = b, a
+            left_op, right_op = right_op, left_op
 
-        a_value = a
-        b_value = b
+        left_value = left_op
+        right_value = right_op
 
         try:
             result = ne.evaluate(
-                f"a_value {op_str} b_value",
-                local_dict={"a_value": a_value, "b_value": b_value},
+                f"left_value {op_str} right_value",
+                local_dict={"left_value": left_value, "right_value": right_op},
                 casting="safe",
             )
         except TypeError:
@@ -116,20 +116,20 @@ def _evaluate_numexpr(op, op_str, a, b):
             # (https://github.com/pydata/numexpr/issues/379)
             pass
         except NotImplementedError:
-            if _bool_arith_fallback(op_str, a, b):
+            if _bool_arith_fallback(op_str, left_op, right_op):
                 pass
             else:
                 raise
 
         if is_reversed:
             # reverse order to original for fallback
-            a, b = b, a
+            left_op, right_op = right_op, left_op
 
     if _TEST_MODE:
         _store_test_result(result is not None)
 
     if result is None:
-        result = _evaluate_standard(op, op_str, a, b)
+        result = _evaluate_standard(op, op_str, left_op, right_op)
 
     return result
 
@@ -170,24 +170,24 @@ _op_str_mapping = {
 }
 
 
-def _where_standard(cond, a, b):
+def _where_standard(cond, left_op, right_op):
     # Caller is responsible for extracting ndarray if necessary
-    return np.where(cond, a, b)
+    return np.where(cond, left_op, right_op)
 
 
-def _where_numexpr(cond, a, b):
+def _where_numexpr(cond, left_op, right_op):
     # Caller is responsible for extracting ndarray if necessary
     result = None
 
-    if _can_use_numexpr(None, "where", a, b, "where"):
+    if _can_use_numexpr(None, "where", left_op, right_op, "where"):
         result = ne.evaluate(
             "where(cond_value, a_value, b_value)",
-            local_dict={"cond_value": cond, "a_value": a, "b_value": b},
+            local_dict={"cond_value": cond, "a_value": left_op, "b_value": right_op},
             casting="safe",
         )
 
     if result is None:
-        result = _where_standard(cond, a, b)
+        result = _where_standard(cond, left_op, right_op)
 
     return result
 
@@ -206,13 +206,13 @@ def _has_bool_dtype(x):
 _BOOL_OP_UNSUPPORTED = {"+": "|", "*": "&", "-": "^"}
 
 
-def _bool_arith_fallback(op_str, a, b) -> bool:
+def _bool_arith_fallback(op_str, left_op, right_op) -> bool:
     """
     Check if we should fallback to the python `_evaluate_standard` in case
     of an unsupported operation by numexpr, which is the case for some
     boolean ops.
     """
-    if _has_bool_dtype(a) and _has_bool_dtype(b):
+    if _has_bool_dtype(left_op) and _has_bool_dtype(right_op):
         if op_str in _BOOL_OP_UNSUPPORTED:
             warnings.warn(
                 f"evaluating in Python space because the {op_str!r} "
@@ -224,15 +224,15 @@ def _bool_arith_fallback(op_str, a, b) -> bool:
     return False
 
 
-def evaluate(op, a, b, use_numexpr: bool = True):
+def evaluate(op, left_op, right_op, use_numexpr: bool = True):
     """
-    Evaluate and return the expression of the op on a and b.
+    Evaluate and return the expression of the op on left_op and right_op.
 
     Parameters
     ----------
     op : the actual operand
-    a : left operand
-    b : right operand
+    left_op : left operand
+    right_op : right operand
     use_numexpr : bool, default True
         Whether to try to use numexpr.
     """
@@ -240,24 +240,24 @@ def evaluate(op, a, b, use_numexpr: bool = True):
     if op_str is not None:
         if use_numexpr:
             # error: "None" not callable
-            return _evaluate(op, op_str, a, b)  # type: ignore[misc]
-    return _evaluate_standard(op, op_str, a, b)
+            return _evaluate(op, op_str, left_op, right_op)  # type: ignore[misc]
+    return _evaluate_standard(op, op_str, left_op, right_op)
 
 
-def where(cond, a, b, use_numexpr: bool = True):
+def where(cond, left_op, right_op, use_numexpr: bool = True):
     """
-    Evaluate the where condition cond on a and b.
+    Evaluate the where condition cond on left_op and right_op.
 
     Parameters
     ----------
     cond : np.ndarray[bool]
-    a : return if cond is True
-    b : return if cond is False
+    left_op : return if cond is True
+    right_op : return if cond is False
     use_numexpr : bool, default True
         Whether to try to use numexpr.
     """
     assert _where is not None
-    return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)
+    return _where(cond, left_op, right_op) if use_numexpr else _where_standard(cond, left_op, right_op)
 
 
 def set_test_mode(v: bool = True) -> None:
diff --git a/pandas/core/computation/pytables.py b/pandas/core/computation/pytables.py
index fe7e27f537..4a75acce46 100644
--- a/pandas/core/computation/pytables.py
+++ b/pandas/core/computation/pytables.py
@@ -205,7 +205,7 @@ class BinOp(ops.BinOp):
         val = v.tostring(self.encoding)
         return f"({self.lhs} {self.op} {val})"
 
-    def convert_value(self, v) -> TermValue:
+    def convert_value(self, conv_val) -> TermValue:
         """
         convert the expression that is in the term to something that is
         accepted by pytables
@@ -219,44 +219,44 @@ class BinOp(ops.BinOp):
         kind = ensure_decoded(self.kind)
         meta = ensure_decoded(self.meta)
         if kind == "datetime" or (kind and kind.startswith("datetime64")):
-            if isinstance(v, (int, float)):
-                v = stringify(v)
-            v = ensure_decoded(v)
-            v = Timestamp(v).as_unit("ns")
-            if v.tz is not None:
-                v = v.tz_convert("UTC")
-            return TermValue(v, v._value, kind)
+            if isinstance(conv_val, (int, float)):
+                conv_val = stringify(conv_val)
+            conv_val = ensure_decoded(conv_val)
+            conv_val = Timestamp(conv_val).as_unit("ns")
+            if conv_val.tz is not None:
+                conv_val = conv_val.tz_convert("UTC")
+            return TermValue(conv_val, conv_val._value, kind)
         elif kind in ("timedelta64", "timedelta"):
-            if isinstance(v, str):
-                v = Timedelta(v)
+            if isinstance(conv_val, str):
+                conv_val = Timedelta(conv_val)
             else:
-                v = Timedelta(v, unit="s")
-            v = v.as_unit("ns")._value
-            return TermValue(int(v), v, kind)
+                conv_val = Timedelta(conv_val, unit="s")
+            conv_val = conv_val.as_unit("ns")._value
+            return TermValue(int(conv_val), conv_val, kind)
         elif meta == "category":
             metadata = extract_array(self.metadata, extract_numpy=True)
             result: npt.NDArray[np.intp] | np.intp | int
-            if v not in metadata:
+            if conv_val not in metadata:
                 result = -1
             else:
-                result = metadata.searchsorted(v, side="left")
+                result = metadata.searchsorted(conv_val, side="left")
             return TermValue(result, result, "integer")
         elif kind == "integer":
             try:
-                v_dec = Decimal(v)
+                v_dec = Decimal(conv_val)
             except InvalidOperation:
                 # GH 54186
                 # convert v to float to raise float's ValueError
-                float(v)
+                float(conv_val)
             else:
-                v = int(v_dec.to_integral_exact(rounding="ROUND_HALF_EVEN"))
-            return TermValue(v, v, kind)
+                conv_val = int(v_dec.to_integral_exact(rounding="ROUND_HALF_EVEN"))
+            return TermValue(conv_val, conv_val, kind)
         elif kind == "float":
-            v = float(v)
-            return TermValue(v, v, kind)
+            conv_val = float(conv_val)
+            return TermValue(conv_val, conv_val, kind)
         elif kind == "bool":
-            if isinstance(v, str):
-                v = v.strip().lower() not in [
+            if isinstance(conv_val, str):
+                conv_val = conv_val.strip().lower() not in [
                     "false",
                     "f",
                     "no",
@@ -268,13 +268,13 @@ class BinOp(ops.BinOp):
                     "",
                 ]
             else:
-                v = bool(v)
-            return TermValue(v, v, kind)
-        elif isinstance(v, str):
+                conv_val = bool(conv_val)
+            return TermValue(conv_val, conv_val, kind)
+        elif isinstance(conv_val, str):
             # string quoting
-            return TermValue(v, stringify(v), "string")
+            return TermValue(conv_val, stringify(conv_val), "string")
         else:
-            raise TypeError(f"Cannot compare {v} of type {type(v)} to {kind} column")
+            raise TypeError(f"Cannot compare {conv_val} of type {type(conv_val)} to {kind} column")
 
     def convert_values(self) -> None:
         pass
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 33a419925f..34b448a0d8 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -1018,7 +1018,7 @@ class DataFrame(NDFrame, OpsMixin):
 
         See Also
         --------
-        ndarray.shape : Tuple of array dimensions.
+        numpy.ndarray.shape : Tuple of array dimensions.
 
         Examples
         --------
diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 3a48cc8a66..d1aa20501b 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -640,7 +640,7 @@ class NDFrame(PandasObject, indexing.IndexingMixin):
 
         See Also
         --------
-        ndarray.ndim : Number of array dimensions.
+        numpy.ndarray.ndim : Number of array dimensions.
 
         Examples
         --------
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index fdfb9f21bd..0d1541bbb3 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -694,7 +694,7 @@ class Resampler(BaseGroupBy, PandasObject):
 
         References
         ----------
-        .. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)
+        .. [1] https://en.wikipedia.org/wiki/Imputation_%28statistics%29
 
         Examples
         --------
diff --git a/pandas/core/strings/accessor.py b/pandas/core/strings/accessor.py
index 05e1a36877..c68b630366 100644
--- a/pandas/core/strings/accessor.py
+++ b/pandas/core/strings/accessor.py
@@ -1374,6 +1374,11 @@ class StringMethods(NoNewAttributesMixin):
         """
         Determine if each string starts with a match of a regular expression.
 
+        Determines whether each string in the Series or Index starts with a
+        match to a specified regular expression. This function is especially
+        useful for validating prefixes, such as ensuring that codes, tags, or
+        identifiers begin with a specific pattern.
+
         Parameters
         ----------
         pat : str
@@ -1419,6 +1424,11 @@ class StringMethods(NoNewAttributesMixin):
         """
         Determine if each string entirely matches a regular expression.
 
+        Checks if each string in the Series or Index fully matches the
+        specified regular expression pattern. This function is useful when the
+        requirement is for an entire string to conform to a pattern, such as
+        validating formats like phone numbers or email addresses.
+
         Parameters
         ----------
         pat : str
@@ -1647,6 +1657,10 @@ class StringMethods(NoNewAttributesMixin):
         """
         Duplicate each string in the Series or Index.
 
+        Duplicates each string in the Series or Index, either by applying the
+        same repeat count to all elements or by using different repeat values
+        for each element.
+
         Parameters
         ----------
         repeats : int or sequence of int
@@ -1710,6 +1724,12 @@ class StringMethods(NoNewAttributesMixin):
         """
         Pad strings in the Series/Index up to width.
 
+        This function pads strings in a Series or Index to a specified width,
+        filling the extra space with a character of your choice. It provides
+        flexibility in positioning the padding, allowing it to be added to the
+        left, right, or both sides. This is useful for formatting strings to
+        align text or ensure consistent string lengths in data processing.
+
         Parameters
         ----------
         width : int
@@ -1920,6 +1940,11 @@ class StringMethods(NoNewAttributesMixin):
         """
         Slice substrings from each element in the Series or Index.
 
+        Slicing substrings from strings in a Series or Index helps extract
+        specific portions of data, making it easier to analyze or manipulate
+        text. This is useful for tasks like parsing structured text fields or
+        isolating parts of strings with a consistent format.
+
         Parameters
         ----------
         start : int, optional
@@ -1996,6 +2021,11 @@ class StringMethods(NoNewAttributesMixin):
         """
         Replace a positional slice of a string with another value.
 
+        This function allows replacing specific parts of a string in a Series
+        or Index by specifying start and stop positions. It is useful for
+        modifying substrings in a controlled way, such as updating sections of
+        text based on their positions or patterns.
+
         Parameters
         ----------
         start : int, optional
diff --git a/pandas/core/tools/datetimes.py b/pandas/core/tools/datetimes.py
index 4680a63bf5..30487de7ba 100644
--- a/pandas/core/tools/datetimes.py
+++ b/pandas/core/tools/datetimes.py
@@ -44,6 +44,7 @@ from pandas.util._exceptions import find_stack_level
 from pandas.core.dtypes.common import (
     ensure_object,
     is_float,
+    is_float_dtype,
     is_integer,
     is_integer_dtype,
     is_list_like,
@@ -1153,6 +1154,10 @@ def _assemble_from_unit_mappings(
         # we allow coercion to if errors allows
         values = to_numeric(values, errors=errors)
 
+        # prevent prevision issues in case of float32 # GH#60506
+        if is_float_dtype(values.dtype):
+            values = values.astype("float64")
+
         # prevent overflow in case of int8 or int16
         if is_integer_dtype(values.dtype):
             values = values.astype("int64")
diff --git a/pandas/errors/__init__.py b/pandas/errors/__init__.py
index 1de6f06ef3..cd31ec3052 100644
--- a/pandas/errors/__init__.py
+++ b/pandas/errors/__init__.py
@@ -45,6 +45,11 @@ class NullFrequencyError(ValueError):
     Particularly ``DatetimeIndex.shift``, ``TimedeltaIndex.shift``,
     ``PeriodIndex.shift``.
 
+    See Also
+    --------
+    Index.shift : Shift values of Index.
+    Series.shift : Shift values of Series.
+
     Examples
     --------
     >>> df = pd.DatetimeIndex(["2011-01-01 10:00", "2011-01-01"], freq=None)
@@ -58,6 +63,12 @@ class PerformanceWarning(Warning):
     """
     Warning raised when there is a possible performance impact.
 
+    See Also
+    --------
+    DataFrame.set_index : Set the DataFrame index using existing columns.
+    DataFrame.loc : Access a group of rows and columns by label(s) \
+    or a boolean array.
+
     Examples
     --------
     >>> df = pd.DataFrame(
@@ -385,6 +396,13 @@ class NumbaUtilError(Exception):
     """
     Error raised for unsupported Numba engine routines.
 
+    See Also
+    --------
+    DataFrame.groupby : Group DataFrame using a mapper or by a Series of columns.
+    Series.groupby : Group Series using a mapper or by a Series of columns.
+    DataFrame.agg : Aggregate using one or more operations over the specified axis.
+    Series.agg : Aggregate using one or more operations over the specified axis.
+
     Examples
     --------
     >>> df = pd.DataFrame(


Test patch:
diff --git a/pandas/tests/series/accessors/test_list_accessor.py b/pandas/tests/series/accessors/test_list_accessor.py
index c153e800cb..bec8ca13a2 100644
--- a/pandas/tests/series/accessors/test_list_accessor.py
+++ b/pandas/tests/series/accessors/test_list_accessor.py
@@ -25,9 +25,10 @@ def test_list_getitem(list_dtype):
     ser = Series(
         [[1, 2, 3], [4, None, 5], None],
         dtype=ArrowDtype(list_dtype),
+        name="a",
     )
     actual = ser.list[1]
-    expected = Series([2, None, None], dtype="int64[pyarrow]")
+    expected = Series([2, None, None], dtype="int64[pyarrow]", name="a")
     tm.assert_series_equal(actual, expected)
 
 
@@ -37,9 +38,15 @@ def test_list_getitem_index():
         [[1, 2, 3], [4, None, 5], None],
         dtype=ArrowDtype(pa.list_(pa.int64())),
         index=[1, 3, 7],
+        name="a",
     )
     actual = ser.list[1]
-    expected = Series([2, None, None], dtype="int64[pyarrow]", index=[1, 3, 7])
+    expected = Series(
+        [2, None, None],
+        dtype="int64[pyarrow]",
+        index=[1, 3, 7],
+        name="a",
+    )
     tm.assert_series_equal(actual, expected)
 
 
@@ -48,6 +55,7 @@ def test_list_getitem_slice():
         [[1, 2, 3], [4, None, 5], None],
         dtype=ArrowDtype(pa.list_(pa.int64())),
         index=[1, 3, 7],
+        name="a",
     )
     if pa_version_under11p0:
         with pytest.raises(
@@ -60,6 +68,7 @@ def test_list_getitem_slice():
             [[2, 3], [None, 5], None],
             dtype=ArrowDtype(pa.list_(pa.int64())),
             index=[1, 3, 7],
+            name="a",
         )
         tm.assert_series_equal(actual, expected)
 
@@ -68,9 +77,10 @@ def test_list_len():
     ser = Series(
         [[1, 2, 3], [4, None], None],
         dtype=ArrowDtype(pa.list_(pa.int64())),
+        name="a",
     )
     actual = ser.list.len()
-    expected = Series([3, 2, None], dtype=ArrowDtype(pa.int32()))
+    expected = Series([3, 2, None], dtype=ArrowDtype(pa.int32()), name="a")
     tm.assert_series_equal(actual, expected)
 
 
@@ -78,12 +88,14 @@ def test_list_flatten():
     ser = Series(
         [[1, 2, 3], None, [4, None], [], [7, 8]],
         dtype=ArrowDtype(pa.list_(pa.int64())),
+        name="a",
     )
     actual = ser.list.flatten()
     expected = Series(
         [1, 2, 3, 4, None, 7, 8],
         dtype=ArrowDtype(pa.int64()),
         index=[0, 0, 0, 2, 2, 4, 4],
+        name="a",
     )
     tm.assert_series_equal(actual, expected)
 
diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
index b73839f406..74b051aec7 100644
--- a/pandas/tests/tools/test_to_datetime.py
+++ b/pandas/tests/tools/test_to_datetime.py
@@ -2084,6 +2084,18 @@ class TestToDatetimeDataFrame:
         )
         tm.assert_series_equal(result, expected)
 
+    def test_dataframe_float32_dtype(self, df, cache):
+        # GH#60506
+        # coerce to float64
+        result = to_datetime(df.astype(np.float32), cache=cache)
+        expected = Series(
+            [
+                Timestamp("20150204 06:58:10.001002003"),
+                Timestamp("20160305 07:59:11.001002003"),
+            ]
+        )
+        tm.assert_series_equal(result, expected)
+
     def test_dataframe_coerce(self, cache):
         # passing coerce
         df2 = DataFrame({"year": [2015, 2016], "month": [2, 20], "day": [4, 5]})