CLN: TODOs (#44733)

Author: jbrockmendel

pandas/tests/arrays/boolean/test_construction.py

@@ -321,19 +321,3 @@ def test_to_numpy_copy():
  result = arr.to_numpy(dtype=bool, copy=True)
  result[0] = False
  tm.assert_extension_array_equal(arr, pd.array([True, False, True], dtype="boolean"))
-
-
-# FIXME: don't leave commented out
-# TODO when BooleanArray coerces to object dtype numpy array, need to do conversion
-# manually in the indexing code
-# def test_indexing_boolean_mask():
-# arr = pd.array([1, 2, 3, 4], dtype="Int64")
-# mask = pd.array([True, False, True, False], dtype="boolean")
-# result = arr[mask]
-# expected = pd.array([1, 3], dtype="Int64")
-# tm.assert_extension_array_equal(result, expected)
-
-# # missing values -> error
-# mask = pd.array([True, False, True, None], dtype="boolean")
-# with pytest.raises(IndexError):
-# result = arr[mask]

pandas/tests/arrays/masked/test_arithmetic.py

@@ -7,7 +7,6 @@
 
 import pandas as pd
 import pandas._testing as tm
-from pandas.core.arrays import ExtensionArray
 
 # integer dtypes
 arrays = [pd.array([1, 2, 3, None], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES]
@@ -71,11 +70,7 @@ def test_numpy_array_equivalence(data, all_arithmetic_operators):
 
  result = op(data, numpy_array)
  expected = op(data, pd_array)
- if isinstance(expected, ExtensionArray):
- tm.assert_extension_array_equal(result, expected)
- else:
- # TODO div still gives float ndarray -> remove this once we have Float EA
- tm.assert_numpy_array_equal(result, expected)
+ tm.assert_extension_array_equal(result, expected)
 
 
 # Test equivalence with Series and DataFrame ops

pandas/tests/extension/test_sparse.py

@@ -328,11 +328,14 @@ def test_where_series(self, data, na_value):
  expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
  self.assert_series_equal(result, expected)
 
- def test_combine_first(self, data):
+ def test_combine_first(self, data, request):
  if data.dtype.subtype == "int":
  # Right now this is upcasted to float, just like combine_first
  # for Series[int]
- pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.")
+ mark = pytest.mark.xfail(
+ reason="TODO(SparseArray.__setitem__) will preserve dtype."
+ )
+ request.node.add_marker(mark)
  super().test_combine_first(data)
 
  def test_searchsorted(self, data_for_sorting, as_series):

pandas/tests/frame/methods/test_align.py

@@ -55,9 +55,7 @@ def test_align_float(self, float_frame):
  # test fill value
  join_idx = float_frame.index.join(other.index)
  diff_a = float_frame.index.difference(join_idx)
- diff_b = other.index.difference(join_idx)
  diff_a_vals = af.reindex(diff_a).values
- diff_b_vals = bf.reindex(diff_b).values
  assert (diff_a_vals == -1).all()
 
  af, bf = float_frame.align(other, join="right", axis=0)
@@ -74,12 +72,8 @@ def test_align_float(self, float_frame):
  # test fill value
  join_idx = float_frame.index.join(other.index)
  diff_a = float_frame.index.difference(join_idx)
- diff_b = other.index.difference(join_idx)
  diff_a_vals = af.reindex(diff_a).values
 
- # TODO(wesm): unused?
- diff_b_vals = bf.reindex(diff_b).values # noqa
-
  assert (diff_a_vals == -1).all()
 
  af, bf = float_frame.align(other, join="inner", axis=1)

pandas/tests/frame/methods/test_quantile.py

@@ -571,7 +571,7 @@ def test_quantile_item_cache(self, using_array_manager):
 
 class TestQuantileExtensionDtype:
  # TODO: tests for axis=1?
- # TODO: empty case? might as well do dt64 and td64 here too
+ # TODO: empty case?
 
  @pytest.fixture(
  params=[
@@ -581,6 +581,7 @@ class TestQuantileExtensionDtype:
  ),
  pd.period_range("2016-01-01", periods=9, freq="D"),
  pd.date_range("2016-01-01", periods=9, tz="US/Pacific"),
+ pd.timedelta_range("1 Day", periods=9),
  pd.array(np.arange(9), dtype="Int64"),
  pd.array(np.arange(9), dtype="Float64"),
  ],
@@ -650,7 +651,18 @@ def test_quantile_ea_with_na(self, obj, index):
 
  # TODO(GH#39763): filtering can be removed after GH#39763 is fixed
  @pytest.mark.filterwarnings("ignore:Using .astype to convert:FutureWarning")
- def test_quantile_ea_all_na(self, obj, index, frame_or_series):
+ def test_quantile_ea_all_na(
+ self, obj, index, frame_or_series, using_array_manager, request
+ ):
+ if (
+ using_array_manager
+ and frame_or_series is DataFrame
+ and index.dtype == "m8[ns]"
+ ):
+ mark = pytest.mark.xfail(
+ reason="obj.astype fails bc obj is incorrectly dt64 at this point"
+ )
+ request.node.add_marker(mark)
 
  obj.iloc[:] = index._na_value
 

pandas/tests/groupby/test_apply.py

@@ -360,7 +360,7 @@ def test_apply_frame_not_as_index_column_name(df):
  grouped = df.groupby(["A", "B"], as_index=False)
  result = grouped.apply(len)
  expected = grouped.count().rename(columns={"C": np.nan}).drop(columns="D")
- # TODO: Use assert_frame_equal when column name is not np.nan (GH 36306)
+ # TODO(GH#34306): Use assert_frame_equal when column name is not np.nan
  tm.assert_index_equal(result.index, expected.index)
  tm.assert_numpy_array_equal(result.values, expected.values)
 
@@ -1134,9 +1134,8 @@ def test_positional_slice_groups_datetimelike():
  tm.assert_frame_equal(result, expected)
 
 
-def test_doctest_example2():
+def test_groupby_apply_shape_cache_safety():
  # GH#42702 this fails if we cache_readonly Block.shape
- # TODO: more informative name
  df = DataFrame({"A": ["a", "a", "b"], "B": [1, 2, 3], "C": [4, 6, 5]})
  gb = df.groupby("A")
  result = gb[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min())

pandas/tests/indexes/datetimes/test_constructors.py

@@ -69,20 +69,20 @@ def test_shallow_copy_inherits_array_freq(self, index):
 
  def test_categorical_preserves_tz(self):
  # GH#18664 retain tz when going DTI-->Categorical-->DTI
- # TODO: parametrize over DatetimeIndex/DatetimeArray
- # once pd.CategoricalIndex(DTA) works
-
  dti = DatetimeIndex(
  [pd.NaT, "2015-01-01", "1999-04-06 15:14:13", "2015-01-01"], tz="US/Eastern"
  )
 
- ci = pd.CategoricalIndex(dti)
- carr = pd.Categorical(dti)
- cser = pd.Series(ci)
+ for dtobj in [dti, dti._data]:
+ # works for DatetimeIndex or DatetimeArray
+
+ ci = pd.CategoricalIndex(dtobj)
+ carr = pd.Categorical(dtobj)
+ cser = pd.Series(ci)
 
- for obj in [ci, carr, cser]:
- result = DatetimeIndex(obj)
- tm.assert_index_equal(result, dti)
+  for obj in [ci, carr, cser]:
+  result = DatetimeIndex(obj)
+  tm.assert_index_equal(result, dti)
 
  def test_dti_with_period_data_raises(self):
  # GH#23675

pandas/tests/indexes/test_base.py

@@ -1364,9 +1364,9 @@ def test_str_to_bytes_raises(self):
  @pytest.mark.filterwarnings("ignore:elementwise comparison failed:FutureWarning")
  def test_index_with_tuple_bool(self):
  # GH34123
- # TODO: remove tupleize_cols=False once correct behaviour is restored
  # TODO: also this op right now produces FutureWarning from numpy
- idx = Index([("a", "b"), ("b", "c"), ("c", "a")], tupleize_cols=False)
+ # https://github.com/numpy/numpy/issues/11521
+ idx = Index([("a", "b"), ("b", "c"), ("c", "a")])
  result = idx == ("c", "a")
  expected = np.array([False, False, True])
  tm.assert_numpy_array_equal(result, expected)

pandas/tests/indexes/test_setops.py

@@ -453,10 +453,9 @@ def test_intersection_difference_match_empty(self, index, sort):
 @pytest.mark.parametrize(
  "method", ["intersection", "union", "difference", "symmetric_difference"]
 )
-def test_setop_with_categorical(index, sort, method):
- if isinstance(index, MultiIndex): # TODO: flat_index?
- # tested separately in tests.indexes.multi.test_setops
- return
+def test_setop_with_categorical(index_flat, sort, method):
+ # MultiIndex tested separately in tests.indexes.multi.test_setops
+ index = index_flat
 
  other = index.astype("category")
  exact = "equiv" if isinstance(index, RangeIndex) else True

pandas/tests/indexing/test_chaining_and_caching.py

@@ -502,8 +502,8 @@ def test_iloc_setitem_chained_assignment(self):
 
  df["bb"].iloc[0] = 0.13
 
- # TODO: unused
- df_tmp = df.iloc[ck] # noqa
+ # GH#3970 this lookup used to break the chained setting to 0.15
+ df.iloc[ck]
 
  df["bb"].iloc[0] = 0.15
  assert df["bb"].iloc[0] == 0.15