CLN: FIXMEs (pandas-dev#44771)

pandas/_config/config.py

@@ -642,7 +642,6 @@ def _warn_if_deprecated(key: str) -> bool:
     d = _get_deprecated_option(key)
     if d:
         if d.msg:
-            print(d.msg)
             warnings.warn(d.msg, FutureWarning)
         else:
             msg = f"'{key}' is deprecated"

pandas/core/generic.py

@@ -6024,7 +6024,7 @@ def _convert(
         timedelta: bool_t = False,
     ) -> NDFrameT:
         """
-        Attempt to infer better dtype for object columns
+        Attempt to infer better dtype for object columns.
 
         Parameters
         ----------

pandas/core/indexes/datetimes.py

@@ -647,7 +647,8 @@ def get_loc(self, key, method=None, tolerance=None):
             try:
                 key = self._maybe_cast_for_get_loc(key)
             except ValueError as err:
-                # FIXME: we get here because parse_with_reso doesn't raise on "t2m"
+                # FIXME(dateutil#1180): we get here because parse_with_reso
+                #  doesn't raise on "t2m"
                 raise KeyError(key) from err
 
         elif isinstance(key, timedelta):

pandas/core/internals/blocks.py

@@ -1797,9 +1797,7 @@ def fillna(
                 value, limit, inplace, downcast
             )
 
-        values = self.values
-        values = values if inplace else values.copy()
-        new_values = values.fillna(value=value, limit=limit)
+        new_values = self.values.fillna(value=value, limit=limit)
         return [self.make_block_same_class(values=new_values)]
 
 

pandas/core/internals/concat.py

@@ -528,7 +528,6 @@ def _get_empty_dtype(join_units: Sequence[JoinUnit]) -> DtypeObj:
         return blk.dtype
 
     if _is_uniform_reindex(join_units):
-        # FIXME: integrate property
         empty_dtype = join_units[0].block.dtype
         return empty_dtype
 

pandas/io/clipboard/__init__.py

@@ -518,7 +518,7 @@ def determine_clipboard():
         "cygwin" in platform.system().lower()
     ):  # Cygwin has a variety of values returned by platform.system(),
         # such as 'CYGWIN_NT-6.1'
-        # FIXME: pyperclip currently does not support Cygwin,
+        # FIXME(pyperclip#55): pyperclip currently does not support Cygwin,
         # see https://github.com/asweigart/pyperclip/issues/55
         if os.path.exists("/dev/clipboard"):
             warnings.warn(

pandas/tests/arrays/floating/test_construction.py

@@ -56,12 +56,6 @@ def test_floating_array_disallows_float16(request):
     with pytest.raises(TypeError, match=msg):
         FloatingArray(arr, mask)
 
-    if not np_version_under1p19:
-        # Troubleshoot
-        #  https://github.com/numpy/numpy/issues/20512#issuecomment-985807740
-        lowered = np.core._type_aliases.english_lower("Float16")
-        assert lowered == "float16", lowered
-
     if np_version_under1p19 or (
         locale.getlocale()[0] != "en_US" and not is_platform_windows()
     ):

pandas/tests/extension/base/getitem.py

@@ -2,6 +2,7 @@
 import pytest
 
 import pandas as pd
+import pandas._testing as tm
 from pandas.tests.extension.base.base import BaseExtensionTests
 
 
@@ -258,12 +259,22 @@ def test_getitem_integer_with_missing_raises(self, data, idx):
         with pytest.raises(ValueError, match=msg):
             data[idx]
 
-        # FIXME: dont leave commented-out
+    @pytest.mark.xfail(
+        reason="Tries label-based and raises KeyError; "
+        "in some cases raises when calling np.asarray"
+    )
+    @pytest.mark.parametrize(
+        "idx",
+        [[0, 1, 2, pd.NA], pd.array([0, 1, 2, pd.NA], dtype="Int64")],
+        ids=["list", "integer-array"],
+    )
+    def test_getitem_series_integer_with_missing_raises(self, data, idx):
+        msg = "Cannot index with an integer indexer containing NA values"
         # TODO: this raises KeyError about labels not found (it tries label-based)
-        # import pandas._testing as tm
-        # ser = pd.Series(data, index=[tm.rands(4) for _ in range(len(data))])
-        # with pytest.raises(ValueError, match=msg):
-        #    ser[idx]
+
+        ser = pd.Series(data, index=[tm.rands(4) for _ in range(len(data))])
+        with pytest.raises(ValueError, match=msg):
+            ser[idx]
 
     def test_getitem_slice(self, data):
         # getitem[slice] should return an array

pandas/tests/indexes/multi/test_setops.py

@@ -260,17 +260,24 @@ def test_union(idx, sort):
     else:
         assert result.equals(idx)
 
-    # FIXME: don't leave commented-out
-    # not valid for python 3
-    # def test_union_with_regular_index(self):
-    #     other = Index(['A', 'B', 'C'])
 
-    #     result = other.union(idx)
-    #     assert ('foo', 'one') in result
-    #     assert 'B' in result
+@pytest.mark.xfail(
+    # This test was commented out from Oct 2011 to Dec 2021, may no longer
+    #  be relevant.
+    reason="Length of names must match number of levels in MultiIndex",
+    raises=ValueError,
+)
+def test_union_with_regular_index(idx):
+    other = Index(["A", "B", "C"])
 
-    #     result2 = _index.union(other)
-    #     assert result.equals(result2)
+    result = other.union(idx)
+    assert ("foo", "one") in result
+    assert "B" in result
+
+    msg = "The values in the array are unorderable"
+    with tm.assert_produces_warning(RuntimeWarning, match=msg):
+        result2 = idx.union(other)
+    assert result.equals(result2)
 
 
 def test_intersection(idx, sort):
@@ -355,6 +362,7 @@ def test_union_sort_other_empty(slice_):
     other = idx[slice_]
     tm.assert_index_equal(idx.union(other), idx)
     # MultiIndex does not special case empty.union(idx)
+    # FIXME: don't leave commented-out
     # tm.assert_index_equal(other.union(idx), idx)
 
     # sort=False

pandas/tests/indexes/period/test_indexing.py

@@ -193,20 +193,18 @@ def test_getitem_seconds(self):
                 "2013/02/01 9H",
                 "2013/02/01 09:00",
             ]
-            for v in values:
+            for val in values:
                 # GH7116
                 # these show deprecations as we are trying
                 # to slice with non-integer indexers
-                # FIXME: don't leave commented-out
-                # with pytest.raises(IndexError):
-                #    idx[v]
-                continue
+                with pytest.raises(IndexError, match="only integers, slices"):
+                    idx[val]
 
-            s = Series(np.random.rand(len(idx)), index=idx)
-            tm.assert_series_equal(s["2013/01/01 10:00"], s[3600:3660])
-            tm.assert_series_equal(s["2013/01/01 9H"], s[:3600])
+            ser = Series(np.random.rand(len(idx)), index=idx)
+            tm.assert_series_equal(ser["2013/01/01 10:00"], ser[3600:3660])
+            tm.assert_series_equal(ser["2013/01/01 9H"], ser[:3600])
             for d in ["2013/01/01", "2013/01", "2013"]:
-                tm.assert_series_equal(s[d], s)
+                tm.assert_series_equal(ser[d], ser)
 
     def test_getitem_day(self):
         # GH#6716
@@ -223,24 +221,23 @@ def test_getitem_day(self):
                 "2013/02/01 9H",
                 "2013/02/01 09:00",
             ]
-            for v in values:
+            for val in values:
 
                 # GH7116
                 # these show deprecations as we are trying
                 # to slice with non-integer indexers
-                # with pytest.raises(IndexError):
-                #    idx[v]
-                continue
+                with pytest.raises(IndexError, match="only integers, slices"):
+                    idx[val]
 
-            s = Series(np.random.rand(len(idx)), index=idx)
-            tm.assert_series_equal(s["2013/01"], s[0:31])
-            tm.assert_series_equal(s["2013/02"], s[31:59])
-            tm.assert_series_equal(s["2014"], s[365:])
+            ser = Series(np.random.rand(len(idx)), index=idx)
+            tm.assert_series_equal(ser["2013/01"], ser[0:31])
+            tm.assert_series_equal(ser["2013/02"], ser[31:59])
+            tm.assert_series_equal(ser["2014"], ser[365:])
 
             invalid = ["2013/02/01 9H", "2013/02/01 09:00"]
-            for v in invalid:
-                with pytest.raises(KeyError, match=v):
-                    s[v]
+            for val in invalid:
+                with pytest.raises(KeyError, match=val):
+                    ser[val]
 
 
 class TestGetLoc:

pandas/tests/indexing/multiindex/test_partial.py

@@ -169,28 +169,6 @@ def test_getitem_intkey_leading_level(
             with tm.assert_produces_warning(FutureWarning):
                 mi.get_value(ser, 14)
 
-    # ---------------------------------------------------------------------
-    # AMBIGUOUS CASES!
-
-    def test_partial_loc_missing(self, multiindex_year_month_day_dataframe_random_data):
-        pytest.skip("skipping for now")
-
-        ymd = multiindex_year_month_day_dataframe_random_data
-        result = ymd.loc[2000, 0]
-        expected = ymd.loc[2000]["A"]
-        tm.assert_series_equal(result, expected)
-
-        # need to put in some work here
-        # FIXME: dont leave commented-out
-        # self.ymd.loc[2000, 0] = 0
-        # assert (self.ymd.loc[2000]['A'] == 0).all()
-
-        # Pretty sure the second (and maybe even the first) is already wrong.
-        with pytest.raises(KeyError, match="6"):
-            ymd.loc[(2000, 6)]
-        with pytest.raises(KeyError, match="(2000, 6)"):
-            ymd.loc[(2000, 6), 0]
-
     # ---------------------------------------------------------------------
 
     def test_setitem_multiple_partial(self, multiindex_dataframe_random_data):

pandas/tests/indexing/test_coercion.py

@@ -88,6 +88,8 @@ def _assert_setitem_series_conversion(
         # check dtype explicitly for sure
         assert temp.dtype == expected_dtype
 
+        # AFAICT the problem is in Series.__setitem__ where with integer dtype
+        #  ser[1] = 2.2 casts 2.2 to 2 instead of casting the ser to floating
         # FIXME: dont leave commented-out
         # .loc works different rule, temporary disable
         # temp = original_series.copy()

pandas/tests/indexing/test_floats.py

@@ -68,27 +68,16 @@ def test_scalar_non_numeric(self, index_func, frame_or_series, indexer_sl):
         # contains
         assert 3.0 not in s
 
-        # setting with an indexer
-        if s.index.inferred_type in ["categorical"]:
-            # Value or Type Error
-            pass
-        elif s.index.inferred_type in ["datetime64", "timedelta64", "period"]:
-
-            # FIXME: dont leave commented-out
-            # these should prob work
-            # and are inconsistent between series/dataframe ATM
-            # for idxr in [lambda x: x]:
-            #    s2 = s.copy()
-            #
-            #    with pytest.raises(TypeError):
-            #        idxr(s2)[3.0] = 0
-            pass
+        s2 = s.copy()
+        indexer_sl(s2)[3.0] = 10
 
+        if indexer_sl is tm.setitem:
+            assert 3.0 in s2.axes[-1]
+        elif indexer_sl is tm.loc:
+            assert 3.0 in s2.axes[0]
         else:
-
-            s2 = s.copy()
-            indexer_sl(s2)[3.0] = 10
-            assert s2.index.is_object()
+            assert 3.0 not in s2.axes[0]
+            assert 3.0 not in s2.axes[-1]
 
     @pytest.mark.parametrize(
         "index_func",

pandas/tests/io/excel/test_writers.py

@@ -1106,7 +1106,10 @@ def test_datetimes(self, path):
         write_frame = DataFrame({"A": datetimes})
         write_frame.to_excel(path, "Sheet1")
         if path.endswith("xlsx") or path.endswith("xlsm"):
-            pytest.skip("Defaults to openpyxl and fails - GH #38644")
+            pytest.skip(
+                "Defaults to openpyxl and fails with floating point error on "
+                "datetimes; may be fixed on newer versions of openpyxl - GH #38644"
+            )
         read_frame = pd.read_excel(path, sheet_name="Sheet1", header=0)
 
         tm.assert_series_equal(write_frame["A"], read_frame["A"])

pandas/tests/series/methods/test_convert.py

@@ -113,12 +113,6 @@ def test_convert(self):
         result = ser._convert(datetime=True)
         tm.assert_series_equal(result, ser)
 
-        # FIXME: dont leave commented-out
-        # res = ser.copy()
-        # res[0] = np.nan
-        # result = res._convert(datetime=True, numeric=False)
-        # assert result.dtype == 'M8[ns]'
-
     def test_convert_no_arg_error(self):
         ser = Series(["1.0", "2"])
         msg = r"At least one of datetime, numeric or timedelta must be True\."

pandas/tests/series/test_missing.py

@@ -1,6 +1,7 @@
 from datetime import timedelta
 
 import numpy as np
+import pytest
 
 from pandas._libs import iNaT
 
@@ -73,20 +74,23 @@ def test_timedelta64_nan(self):
         td1[2] = td[2]
         assert not isna(td1[2])
 
-        # FIXME: don't leave commented-out
         # boolean setting
-        # this doesn't work, not sure numpy even supports it
-        # result = td[(td>np.timedelta64(timedelta(days=3))) &
-        # td<np.timedelta64(timedelta(days=7)))] = np.nan
-        # assert isna(result).sum() == 7
-
+        # GH#2899 boolean setting
+        td3 = np.timedelta64(timedelta(days=3))
+        td7 = np.timedelta64(timedelta(days=7))
+        td[(td > td3) & (td < td7)] = np.nan
+        assert isna(td).sum() == 3
+
+    @pytest.mark.xfail(
+        reason="Chained inequality raises when trying to define 'selector'"
+    )
+    def test_logical_range_select(self, datetime_series):
         # NumPy limitation =(
-
-        # def test_logical_range_select(self):
-        #     np.random.seed(12345)
-        #     selector = -0.5 <= datetime_series <= 0.5
-        #     expected = (datetime_series >= -0.5) & (datetime_series <= 0.5)
-        #     tm.assert_series_equal(selector, expected)
+        # https://github.com/pandas-dev/pandas/commit/9030dc021f07c76809848925cb34828f6c8484f3
+        np.random.seed(12345)
+        selector = -0.5 <= datetime_series <= 0.5
+        expected = (datetime_series >= -0.5) & (datetime_series <= 0.5)
+        tm.assert_series_equal(selector, expected)
 
     def test_valid(self, datetime_series):
         ts = datetime_series.copy()

pandas/tests/series/test_ufunc.py

@@ -278,11 +278,7 @@ def test_reduce(values, box, request):
             # ATM Index casts to object, so we get python ints/floats
             same_type = False
 
-    if values.dtype == "i8" and box is pd.array:
-        # FIXME: pd.array casts to Int64
-        obj = values
-    else:
-        obj = box(values)
+    obj = box(values)
 
     result = np.maximum.reduce(obj)
     expected = values[1]