CLN: TODOs and FIXMEs

pandas/core/arrays/datetimelike.py

@@ -1466,8 +1466,6 @@ def max(self, *, axis: int | None = None, skipna: bool = True, **kwargs):
         Index.max : Return the maximum value in an Index.
         Series.max : Return the maximum value in a Series.
         """
-        # TODO: skipna is broken with max.
-        # See https://github.com/pandas-dev/pandas/issues/24265
         nv.validate_max((), kwargs)
         nv.validate_minmax_axis(axis, self.ndim)
 

pandas/core/arrays/masked.py

@@ -605,7 +605,7 @@ def value_counts(self, dropna: bool = True) -> Series:
         data = self._data[~self._mask]
         value_counts = Index(data).value_counts()
 
-        # TODO(extension)
+        # TODO(ExtensionIndex)
         # if we have allow Index to hold an ExtensionArray
         # this is easier
         index = value_counts.index._values.astype(object)

pandas/core/groupby/generic.py

@@ -1039,7 +1039,7 @@ def _wrap_applied_output_series(
         key_index,
     ) -> DataFrame | Series:
         # this is to silence a DeprecationWarning
-        # TODO: Remove when default dtype of empty Series is object
+        # TODO(2.0): Remove when default dtype of empty Series is object
         kwargs = first_not_none._construct_axes_dict()
         backup = create_series_with_explicit_dtype(dtype_if_empty=object, **kwargs)
         values = [x if (x is not None) else backup for x in values]

pandas/core/groupby/groupby.py

@@ -3337,7 +3337,8 @@ def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None, axis=0
         Series or DataFrame
             Percentage changes within each group.
         """
-        # TODO: Remove this conditional for SeriesGroupBy when GH#23918 is fixed
+        # TODO(GH#23918): Remove this conditional for SeriesGroupBy when
+        #  GH#23918 is fixed
         if freq is not None or axis != 0:
             return self.apply(
                 lambda x: x.pct_change(

pandas/core/groupby/ops.py

@@ -868,8 +868,8 @@ def result_arraylike(self) -> ArrayLike:
         Analogous to result_index, but returning an ndarray/ExtensionArray
         allowing us to retain ExtensionDtypes not supported by Index.
         """
-        # TODO: once Index supports arbitrary EAs, this can be removed in favor
-        #  of result_index
+        # TODO(ExtensionIndex): once Index supports arbitrary EAs, this can
+        #  be removed in favor of result_index
         if len(self.groupings) == 1:
             return self.groupings[0].group_arraylike
 

pandas/core/internals/managers.py

@@ -80,8 +80,6 @@
     operate_blockwise,
 )
 
-# TODO: flexible with index=None and/or items=None
-
 T = TypeVar("T", bound="BaseBlockManager")
 
 

pandas/core/nanops.py

@@ -1781,16 +1781,20 @@ def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
         # We need to define mask before masking NaTs
         mask = isna(values)
 
-        if accum_func == np.minimum.accumulate:
-            # Note: the accum_func comparison fails as an "is" comparison
-            y = values.view("i8")
-            y[mask] = lib.i8max
-            changed = True
-        else:
-            y = values
-            changed = False
+        y = values.view("i8")
+        # Note: the accum_func comparison fails as an "is" comparison
+        changed = accum_func == np.minimum.accumulate
+
+        try:
+            if changed:
+                y[mask] = lib.i8max
+
+            result = accum_func(y, axis=0)
+        finally:
+            if changed:
+                # restore NaT elements
+                y[mask] = iNaT
 
-        result = accum_func(y.view("i8"), axis=0)
         if skipna:
             result[mask] = iNaT
         elif accum_func == np.minimum.accumulate:
@@ -1800,10 +1804,6 @@ def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
                 # everything up to the first non-na entry stays NaT
                 result[: nz[0]] = iNaT
 
-        if changed:
-            # restore NaT elements
-            y[mask] = iNaT  # TODO: could try/finally for this?
-
         if isinstance(values.dtype, np.dtype):
             result = result.view(orig_dtype)
         else:

pandas/io/sas/sas7bdat.py

@@ -103,13 +103,14 @@ class _Column:
     col_id: int
     name: str | bytes
     label: str | bytes
-    format: str | bytes  # TODO: i think allowing bytes is from py2 days
+    format: str | bytes
     ctype: bytes
     length: int
 
     def __init__(
         self,
         col_id: int,
+        # These can be bytes when convert_header_text is False
         name: str | bytes,
         label: str | bytes,
         format: str | bytes,

pandas/io/sql.py

@@ -2158,9 +2158,6 @@ def to_sql(
         table.insert(chunksize, method)
 
     def has_table(self, name: str, schema: str | None = None):
-        # TODO(wesm): unused?
-        # escape = _get_valid_sqlite_name
-        # esc_name = escape(name)
 
         wld = "?"
         query = f"SELECT name FROM sqlite_master WHERE type='table' AND name={wld};"

pandas/tests/apply/test_series_apply.py

@@ -794,18 +794,15 @@ def test_apply_to_timedelta():
     list_of_valid_strings = ["00:00:01", "00:00:02"]
     a = pd.to_timedelta(list_of_valid_strings)
     b = Series(list_of_valid_strings).apply(pd.to_timedelta)
-    # FIXME: dont leave commented-out
-    # Can't compare until apply on a Series gives the correct dtype
-    # assert_series_equal(a, b)
+    tm.assert_series_equal(Series(a), b)
 
     list_of_strings = ["00:00:01", np.nan, pd.NaT, pd.NaT]
 
-    a = pd.to_timedelta(list_of_strings)  # noqa
+    a = pd.to_timedelta(list_of_strings)
     with tm.assert_produces_warning(FutureWarning, match="Inferring timedelta64"):
         ser = Series(list_of_strings)
-    b = ser.apply(pd.to_timedelta)  # noqa
-    # Can't compare until apply on a Series gives the correct dtype
-    # assert_series_equal(a, b)
+    b = ser.apply(pd.to_timedelta)
+    tm.assert_series_equal(Series(a), b)
 
 
 @pytest.mark.parametrize(

pandas/tests/arrays/floating/test_arithmetic.py

@@ -142,17 +142,16 @@ def test_error_invalid_values(data, all_arithmetic_operators):
     with pytest.raises(TypeError, match=msg):
         ops(pd.Series("foo", index=s.index))
 
-    if op != "__rpow__":
-        # TODO(extension)
-        # rpow with a datetimelike coerces the integer array incorrectly
-        msg = (
-            "can only perform ops with numeric values|"
-            "cannot perform .* with this index type: DatetimeArray|"
+    msg = "|".join(
+        [
+            "can only perform ops with numeric values",
+            "cannot perform .* with this index type: DatetimeArray",
             "Addition/subtraction of integers and integer-arrays "
-            "with DatetimeArray is no longer supported. *"
-        )
-        with pytest.raises(TypeError, match=msg):
-            ops(pd.Series(pd.date_range("20180101", periods=len(s))))
+            "with DatetimeArray is no longer supported. *",
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Series(pd.date_range("20180101", periods=len(s))))
 
 
 # Various

pandas/tests/arrays/integer/test_arithmetic.py

@@ -179,17 +179,16 @@ def test_error_invalid_values(data, all_arithmetic_operators):
     with pytest.raises(TypeError, match=msg):
         ops(pd.Series("foo", index=s.index))
 
-    if op != "__rpow__":
-        # TODO(extension)
-        # rpow with a datetimelike coerces the integer array incorrectly
-        msg = (
-            "can only perform ops with numeric values|"
-            "cannot perform .* with this index type: DatetimeArray|"
+    msg = "|".join(
+        [
+            "can only perform ops with numeric values",
+            "cannot perform .* with this index type: DatetimeArray",
             "Addition/subtraction of integers and integer-arrays "
-            "with DatetimeArray is no longer supported. *"
-        )
-        with pytest.raises(TypeError, match=msg):
-            ops(pd.Series(pd.date_range("20180101", periods=len(s))))
+            "with DatetimeArray is no longer supported. *",
+        ]
+    )
+    with pytest.raises(TypeError, match=msg):
+        ops(pd.Series(pd.date_range("20180101", periods=len(s))))
 
 
 # Various

pandas/tests/arrays/test_datetimes.py

@@ -33,9 +33,13 @@ def test_cmp_dt64_arraylike_tznaive(self, comparison_op):
 
         result = op(arr, arr)
         tm.assert_numpy_array_equal(result, expected)
-        for other in [right, np.array(right)]:
-            # TODO: add list and tuple, and object-dtype once those
-            #  are fixed in the constructor
+        for other in [
+            right,
+            np.array(right),
+            list(right),
+            tuple(right),
+            right.astype(object),
+        ]:
             result = op(arr, other)
             tm.assert_numpy_array_equal(result, expected)
 

pandas/tests/frame/test_arithmetic.py

@@ -925,8 +925,8 @@ def test_binop_other(self, op, value, dtype, switch_numexpr_min_elements):
             (operator.mul, "bool"),
         }
 
-        e = DummyElement(value, dtype)
-        s = DataFrame({"A": [e.value, e.value]}, dtype=e.dtype)
+        elem = DummyElement(value, dtype)
+        df = DataFrame({"A": [elem.value, elem.value]}, dtype=elem.dtype)
 
         invalid = {
             (operator.pow, "<M8[ns]"),
@@ -960,7 +960,7 @@ def test_binop_other(self, op, value, dtype, switch_numexpr_min_elements):
 
             with pytest.raises(TypeError, match=msg):
                 with tm.assert_produces_warning(warn):
-                    op(s, e.value)
+                    op(df, elem.value)
 
         elif (op, dtype) in skip:
 
@@ -971,19 +971,17 @@ def test_binop_other(self, op, value, dtype, switch_numexpr_min_elements):
                 else:
                     warn = None
                 with tm.assert_produces_warning(warn):
-                    op(s, e.value)
+                    op(df, elem.value)
 
             else:
                 msg = "operator '.*' not implemented for .* dtypes"
                 with pytest.raises(NotImplementedError, match=msg):
-                    op(s, e.value)
+                    op(df, elem.value)
 
         else:
-            # FIXME: Since dispatching to Series, this test no longer
-            # asserts anything meaningful
             with tm.assert_produces_warning(None):
-                result = op(s, e.value).dtypes
-                expected = op(s, value).dtypes
+                result = op(df, elem.value).dtypes
+                expected = op(df, value).dtypes
             tm.assert_series_equal(result, expected)
 
 
@@ -1240,9 +1238,7 @@ def test_combineFrame(self, float_frame, mixed_float_frame, mixed_int_frame):
         added = float_frame + mixed_int_frame
         _check_mixed_float(added, dtype="float64")
 
-    def test_combine_series(
-        self, float_frame, mixed_float_frame, mixed_int_frame, datetime_frame
-    ):
+    def test_combine_series(self, float_frame, mixed_float_frame, mixed_int_frame):
 
         # Series
         series = float_frame.xs(float_frame.index[0])
@@ -1272,17 +1268,18 @@ def test_combine_series(
         added = mixed_float_frame + series.astype("float16")
         _check_mixed_float(added, dtype={"C": None})
 
-        # FIXME: don't leave commented-out
-        # these raise with numexpr.....as we are adding an int64 to an
-        # uint64....weird vs int
-
-        # added = mixed_int_frame + (100*series).astype('int64')
-        # _check_mixed_int(added, dtype = {"A": 'int64', "B": 'float64', "C":
-        # 'int64', "D": 'int64'})
-        # added = mixed_int_frame + (100*series).astype('int32')
-        # _check_mixed_int(added, dtype = {"A": 'int32', "B": 'float64', "C":
-        # 'int32', "D": 'int64'})
+        # these used to raise with numexpr as we are adding an int64 to an
+        #  uint64....weird vs int
+        added = mixed_int_frame + (100 * series).astype("int64")
+        _check_mixed_int(
+            added, dtype={"A": "int64", "B": "float64", "C": "int64", "D": "int64"}
+        )
+        added = mixed_int_frame + (100 * series).astype("int32")
+        _check_mixed_int(
+            added, dtype={"A": "int32", "B": "float64", "C": "int32", "D": "int64"}
+        )
 
+    def test_combine_timeseries(self, datetime_frame):
         # TimeSeries
         ts = datetime_frame["A"]
 

pandas/tests/indexes/datetimes/methods/test_insert.py

@@ -236,7 +236,6 @@ def test_insert_mismatched_types_raises(self, tz_aware_fixture, item):
         result = dti.insert(1, item)
 
         if isinstance(item, np.ndarray):
-            # FIXME: without doing .item() here this segfaults
             assert item.item() == 0
             expected = Index([dti[0], 0] + list(dti[1:]), dtype=object, name=9)
         else:

pandas/tests/indexes/multi/test_formats.py

@@ -87,10 +87,7 @@ def test_unicode_repr_issues(self):
         index = MultiIndex(levels=levels, codes=codes)
 
         repr(index.levels)
-
-        # FIXME: dont leave commented-out
-        # NumPy bug
-        # repr(index.get_level_values(1))
+        repr(index.get_level_values(1))
 
     def test_repr_max_seq_items_equal_to_n(self, idx):
         # display.max_seq_items == n

pandas/tests/series/methods/test_convert.py

@@ -108,15 +108,15 @@ def test_convert(self):
         result = ser._convert(datetime=True)
         tm.assert_series_equal(result, expected)
 
-        # preserver if non-object
+        # preserve if non-object
         ser = Series([1], dtype="float32")
         result = ser._convert(datetime=True)
         tm.assert_series_equal(result, ser)
 
         # FIXME: dont leave commented-out
         # res = ser.copy()
-        # r[0] = np.nan
-        # result = res._convert(convert_dates=True,convert_numeric=False)
+        # res[0] = np.nan
+        # result = res._convert(datetime=True, numeric=False)
         # assert result.dtype == 'M8[ns]'
 
     def test_convert_no_arg_error(self):

pandas/tests/series/test_arithmetic.py

@@ -244,13 +244,6 @@ def test_add_corner_cases(self, datetime_series):
         result = empty + empty.copy()
         assert len(result) == 0
 
-        # FIXME: dont leave commented-out
-        # TODO: this returned NotImplemented earlier, what to do?
-        # deltas = Series([timedelta(1)] * 5, index=np.arange(5))
-        # sub_deltas = deltas[::2]
-        # deltas5 = deltas * 5
-        # deltas = deltas + sub_deltas
-
     def test_add_float_plus_int(self, datetime_series):
         # float + int
         int_ts = datetime_series.astype(int)[:-5]
@@ -613,11 +606,6 @@ def test_comparison_operators_with_nas(self, comparison_op):
 
         tm.assert_series_equal(result, expected)
 
-        # FIXME: dont leave commented-out
-        # result = comparison_op(val, ser)
-        # expected = comparison_op(val, ser.dropna()).reindex(ser.index)
-        # tm.assert_series_equal(result, expected)
-
     def test_ne(self):
         ts = Series([3, 4, 5, 6, 7], [3, 4, 5, 6, 7], dtype=float)
         expected = [True, True, False, True, True]