CLN: assorted

pandas/_libs/tslib.pyx

@@ -338,7 +338,7 @@ def array_with_unit_to_datetime(
                     f"unit='{unit}' not valid with non-numerical val='{val}'"
                 )
 
-        except (ValueError, OutOfBoundsDatetime, TypeError) as err:
+        except (ValueError, TypeError) as err:
             if is_raise:
                 err.args = (f"{err}, at position {i}",)
                 raise
@@ -435,15 +435,15 @@ cpdef array_to_datetime(
     Parameters
     ----------
     values : ndarray of object
-         date-like objects to convert
+        date-like objects to convert
     errors : str, default 'raise'
-         error behavior when parsing
+        error behavior when parsing
     dayfirst : bool, default False
-         dayfirst parsing behavior when encountering datetime strings
+        dayfirst parsing behavior when encountering datetime strings
     yearfirst : bool, default False
-         yearfirst parsing behavior when encountering datetime strings
+        yearfirst parsing behavior when encountering datetime strings
     utc : bool, default False
-         indicator whether the dates should be UTC
+        indicator whether the dates should be UTC
     creso : NPY_DATETIMEUNIT, default NPY_FR_ns
         Set to NPY_FR_GENERIC to infer a resolution.
 
@@ -464,7 +464,7 @@ cpdef array_to_datetime(
         bint is_ignore = errors == "ignore"
         bint is_coerce = errors == "coerce"
         bint is_same_offsets
-        _TSObject _ts
+        _TSObject tsobj
         float tz_offset
         set out_tzoffset_vals = set()
         tzinfo tz, tz_out = None
@@ -550,29 +550,28 @@ cpdef array_to_datetime(
                         creso = state.creso
                     continue
 
-                _ts = convert_str_to_tsobject(
+                tsobj = convert_str_to_tsobject(
                     val, None, dayfirst=dayfirst, yearfirst=yearfirst
                 )
 
-                if _ts.value == NPY_NAT:
+                if tsobj.value == NPY_NAT:
                     # e.g. "NaT" string or empty string, we do not consider
                     #  this as either tzaware or tznaive. See
                     #  test_to_datetime_with_empty_str_utc_false_format_mixed
                     # We also do not update resolution inference based on this,
                     #  see test_infer_with_nat_int_float_str
-                    iresult[i] = _ts.value
+                    iresult[i] = tsobj.value
                     continue
 
-                item_reso = _ts.creso
+                item_reso = tsobj.creso
                 state.update_creso(item_reso)
                 if infer_reso:
                     creso = state.creso
 
-                _ts.ensure_reso(creso, val)
-
-                iresult[i] = _ts.value
+                tsobj.ensure_reso(creso, val)
+                iresult[i] = tsobj.value
 
-                tz = _ts.tzinfo
+                tz = tsobj.tzinfo
                 if tz is not None:
                     # dateutil timezone objects cannot be hashed, so
                     # store the UTC offsets in seconds instead

pandas/_libs/tslibs/conversion.pyx

@@ -29,7 +29,6 @@ from cpython.datetime cimport (
 import_datetime()
 
 from pandas._libs.missing cimport checknull_with_nat_and_na
-from pandas._libs.tslibs.base cimport ABCTimestamp
 from pandas._libs.tslibs.dtypes cimport (
     abbrev_to_npy_unit,
     get_supported_reso,
@@ -492,7 +491,7 @@ cdef _TSObject convert_datetime_to_tsobject(
         pydatetime_to_dtstruct(ts, &obj.dts)
         obj.tzinfo = ts.tzinfo
 
-    if isinstance(ts, ABCTimestamp):
+    if isinstance(ts, _Timestamp):
         obj.dts.ps = ts.nanosecond * 1000
 
     if nanos:
@@ -766,7 +765,7 @@ cpdef inline datetime localize_pydatetime(datetime dt, tzinfo tz):
     """
     if tz is None:
         return dt
-    elif isinstance(dt, ABCTimestamp):
+    elif isinstance(dt, _Timestamp):
         return dt.tz_localize(tz)
     return _localize_pydatetime(dt, tz)
 

pandas/_libs/tslibs/dtypes.pyi

@@ -4,7 +4,7 @@ OFFSET_TO_PERIOD_FREQSTR: dict[str, str]
 
 def periods_per_day(reso: int = ...) -> int: ...
 def periods_per_second(reso: int) -> int: ...
-def abbrev_to_npy_unit(abbrev: str) -> int: ...
+def abbrev_to_npy_unit(abbrev: str | None) -> int: ...
 def freq_to_period_freqstr(freq_n: int, freq_name: str) -> str: ...
 
 class PeriodDtypeBase:

pandas/_libs/tslibs/np_datetime.pyx

@@ -596,7 +596,7 @@ cdef int64_t get_conversion_factor(
     ):
         raise ValueError("unit-less resolutions are not supported")
     if from_unit > to_unit:
-        raise ValueError
+        raise ValueError("from_unit must be <= to_unit")
 
     if from_unit == to_unit:
         return 1

pandas/_libs/tslibs/strptime.pyx

@@ -319,14 +319,14 @@ def array_strptime(
         Py_ssize_t i, n = len(values)
         npy_datetimestruct dts
         int64_t[::1] iresult
-        object val, tz
+        object val
         bint seen_datetime_offset = False
         bint is_raise = errors=="raise"
         bint is_ignore = errors=="ignore"
         bint is_coerce = errors=="coerce"
         bint is_same_offsets
         set out_tzoffset_vals = set()
-        tzinfo tz_out = None
+        tzinfo tz, tz_out = None
         bint iso_format = format_is_iso(fmt)
         NPY_DATETIMEUNIT out_bestunit, item_reso
         int out_local = 0, out_tzoffset = 0
@@ -484,7 +484,7 @@ def array_strptime(
                 tz = None
                 out_tzoffset_vals.add("naive")
 
-        except (ValueError, OutOfBoundsDatetime) as ex:
+        except ValueError as ex:
             ex.args = (
                 f"{str(ex)}, at position {i}. You might want to try:\n"
                 "    - passing `format` if your strings have a consistent format;\n"
@@ -1084,7 +1084,7 @@ cdef tzinfo parse_timezone_directive(str z):
     cdef:
         int hours, minutes, seconds, pad_number, microseconds
         int total_minutes
-        object gmtoff_remainder, gmtoff_remainder_padding
+        str gmtoff_remainder, gmtoff_remainder_padding
 
     if z == "Z":
         return timezone(timedelta(0))

pandas/_libs/tslibs/timedeltas.pyx

@@ -499,9 +499,9 @@ cdef int64_t parse_timedelta_string(str ts) except? -1:
     """
 
     cdef:
-        unicode c
+        str c
         bint neg = 0, have_dot = 0, have_value = 0, have_hhmmss = 0
-        object current_unit = None
+        str current_unit = None
         int64_t result = 0, m = 0, r
         list number = [], frac = [], unit = []
 

pandas/core/dtypes/dtypes.py

@@ -1453,7 +1453,7 @@ class NumpyEADtype(ExtensionDtype):
 
     def __init__(self, dtype: npt.DTypeLike | NumpyEADtype | None) -> None:
         if isinstance(dtype, NumpyEADtype):
-            # make constructor univalent
+            # make constructor idempotent
             dtype = dtype.numpy_dtype
         self._dtype = np.dtype(dtype)
 

pandas/core/frame.py

@@ -143,7 +143,6 @@
 from pandas.core.arrays.sparse import SparseFrameAccessor
 from pandas.core.construction import (
     ensure_wrapped_if_datetimelike,
-    extract_array,
     sanitize_array,
     sanitize_masked_array,
 )
@@ -8784,11 +8783,11 @@ def combine_first(self, other: DataFrame) -> DataFrame:
         """
         from pandas.core.computation import expressions
 
-        def combiner(x, y):
-            mask = extract_array(isna(x))
+        def combiner(x: Series, y: Series):
+            mask = x.isna()._values
 
-            x_values = extract_array(x, extract_numpy=True)
-            y_values = extract_array(y, extract_numpy=True)
+            x_values = x._values
+            y_values = y._values
 
             # If the column y in other DataFrame is not in first DataFrame,
             # just return y_values.

pandas/core/tools/datetimes.py

@@ -1201,7 +1201,7 @@ def coerce(values):
         values = to_numeric(values, errors=errors)
 
         # prevent overflow in case of int8 or int16
-        if is_integer_dtype(values):
+        if is_integer_dtype(values.dtype):
             values = values.astype("int64", copy=False)
         return values
 

pandas/io/sas/sas7bdat.py

@@ -86,7 +86,7 @@ def _convert_datetimes(sas_datetimes: pd.Series, unit: str) -> pd.Series:
     ----------
     sas_datetimes : {Series, Sequence[float]}
        Dates or datetimes in SAS
-    unit : {str}
+    unit : {'d', 's'}
        "d" if the floats represent dates, "s" for datetimes
 
     Returns

pandas/io/stata.py

@@ -234,9 +234,6 @@
 stata_epoch: Final = datetime(1960, 1, 1)
 
 
-# TODO: Add typing. As of January 2020 it is not possible to type this function since
-#  mypy doesn't understand that a Series and an int can be combined using mathematical
-#  operations. (+, -).
 def _stata_elapsed_date_to_datetime_vec(dates: Series, fmt: str) -> Series:
     """
     Convert from SIF to datetime. https://www.stata.com/help.cgi?datetime

pandas/plotting/_matplotlib/core.py

@@ -1495,7 +1495,7 @@ def _is_ts_plot(self) -> bool:
         return not self.x_compat and self.use_index and self._use_dynamic_x()
 
     @final
-    def _use_dynamic_x(self):
+    def _use_dynamic_x(self) -> bool:
         return use_dynamic_x(self._get_ax(0), self.data)
 
     def _make_plot(self, fig: Figure) -> None:
@@ -1537,7 +1537,7 @@ def _make_plot(self, fig: Figure) -> None:
             errors = self._get_errorbars(label=label, index=i)
             kwds = dict(kwds, **errors)
 
-            label = pprint_thing(label)  # .encode('utf-8')
+            label = pprint_thing(label)
             label = self._mark_right_label(label, index=i)
             kwds["label"] = label
 

pandas/tests/arithmetic/test_datetime64.py

@@ -1082,6 +1082,8 @@ def test_dt64arr_addsub_intlike(
         self, request, dtype, index_or_series_or_array, freq, tz_naive_fixture
     ):
         # GH#19959, GH#19123, GH#19012
+        # GH#55860 use index_or_series_or_array instead of box_with_array
+        #  bc DataFrame alignment makes it inapplicable
         tz = tz_naive_fixture
 
         if freq is None:

pandas/tests/arithmetic/test_timedelta64.py

@@ -497,6 +497,7 @@ def test_addition_ops(self):
             tdi + Index([1, 2, 3], dtype=np.int64)
 
         # this is a union!
+        # FIXME: don't leave commented-out
         # pytest.raises(TypeError, lambda : Index([1,2,3]) + tdi)
 
         result = tdi + dti  # name will be reset

pandas/tests/arrays/numpy_/test_numpy.py

@@ -87,7 +87,7 @@ def test_constructor_from_string():
     assert result == expected
 
 
-def test_dtype_univalent(any_numpy_dtype):
+def test_dtype_idempotent(any_numpy_dtype):
     dtype = NumpyEADtype(any_numpy_dtype)
 
     result = NumpyEADtype(dtype)

pandas/tests/arrays/string_/test_string.py

@@ -463,7 +463,7 @@ def test_min_max_numpy(method, box, dtype, request, arrow_string_storage):
     assert result == expected
 
 
-def test_fillna_args(dtype, request, arrow_string_storage):
+def test_fillna_args(dtype, arrow_string_storage):
     # GH 37987
 
     arr = pd.array(["a", pd.NA], dtype=dtype)

pandas/tests/arrays/test_datetimelike.py

@@ -183,9 +183,7 @@ def test_take_fill_raises(self, fill_value, arr1d):
             arr1d.take([0, 1], allow_fill=True, fill_value=fill_value)
 
     def test_take_fill(self, arr1d):
-        np.arange(10, dtype="i8") * 24 * 3600 * 10**9
-
-        arr = arr1d  # self.array_cls(data, freq="D")
+        arr = arr1d
 
         result = arr.take([-1, 1], allow_fill=True, fill_value=None)
         assert result[0] is NaT

pandas/tests/indexes/datetimes/test_constructors.py

@@ -959,7 +959,7 @@ def test_dti_tz_constructors(self, tzstr):
         for other in [idx2, idx3, idx4]:
             tm.assert_index_equal(idx1, other)
 
-    def test_dti_construction_univalent(self, unit):
+    def test_dti_construction_idempotent(self, unit):
         rng = date_range(
             "03/12/2012 00:00", periods=10, freq="W-FRI", tz="US/Eastern", unit=unit
         )

pandas/tests/io/parser/common/test_data_list.py

@@ -16,10 +16,10 @@
     "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
 )
 
-skip_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
+xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
 
 
-@skip_pyarrow
+@xfail_pyarrow
 def test_read_data_list(all_parsers):
     parser = all_parsers
     kwargs = {"index_col": 0}

pandas/tests/io/sas/test_sas7bdat.py

@@ -14,6 +14,8 @@
 import pandas as pd
 import pandas._testing as tm
 
+from pandas.io.sas.sas7bdat import SAS7BDATReader
+
 
 @pytest.fixture
 def dirpath(datapath):
@@ -127,8 +129,6 @@ def test_encoding_options(datapath):
             pass
     tm.assert_frame_equal(df1, df2)
 
-    from pandas.io.sas.sas7bdat import SAS7BDATReader
-
     with contextlib.closing(SAS7BDATReader(fname, convert_header_text=False)) as rdr:
         df3 = rdr.read()
     for x, y in zip(df1.columns, df3.columns):
@@ -189,10 +189,9 @@ def test_date_time(datapath):
         fname, parse_dates=["Date1", "Date2", "DateTime", "DateTimeHi", "Taiw"]
     )
     # GH 19732: Timestamps imported from sas will incur floating point errors
-    # 2023-11-16 we don't know the correct "expected" result bc we do not have
-    #  access to SAS to read the sas7bdat file. We are really just testing
-    #  that we are "close". This only seems to be an issue near the
-    #  implementation bounds.
+    # See GH#56014 for discussion of the correct "expected" results
+    #  We are really just testing that we are "close". This only seems to be
+    #  an issue near the implementation bounds.
 
     df[df.columns[3]] = df.iloc[:, 3].dt.round("us")
     df0["Date1"] = df0["Date1"].astype("M8[s]")
@@ -271,6 +270,7 @@ def test_max_sas_date(datapath):
     # NB. max datetime in SAS dataset is 31DEC9999:23:59:59.999
     #    but this is read as 29DEC9999:23:59:59.998993 by a buggy
     #    sas7bdat module
+    # See also GH#56014 for discussion of the correct "expected" results.
     fname = datapath("io", "sas", "data", "max_sas_date.sas7bdat")
     df = pd.read_sas(fname, encoding="iso-8859-1")
 

pandas/tests/io/test_stata.py

@@ -197,6 +197,7 @@ def test_read_dta2(self, datapath):
             # datapath("io", "data", "stata", "stata2_113.dta")
             # )
 
+        # FIXME: don't leave commented-out
         # buggy test because of the NaT comparison on certain platforms
         # Format 113 test fails since it does not support tc and tC formats
         # tm.assert_frame_equal(parsed_113, expected)

pandas/tests/tools/test_to_datetime.py

@@ -3107,7 +3107,7 @@ class TestDatetimeParsingWrappers:
             ("Thu Sep 25 2003", datetime(2003, 9, 25)),
             ("Sep 25 2003", datetime(2003, 9, 25)),
             ("January 1 2014", datetime(2014, 1, 1)),
-            # GHE10537
+            # GH#10537
             ("2014-06", datetime(2014, 6, 1)),
             ("06-2014", datetime(2014, 6, 1)),
             ("2014-6", datetime(2014, 6, 1)),