CI/CLN: Add more test file linting (#37351)

* CI/CLN: Add more test file linting * Fix

Author: dsaxton

ci/code_checks.sh

@@ -178,10 +178,10 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
  RET=$(($RET + $?)) ; echo $MSG "DONE"
 
  MSG='Check for inconsistent use of pandas namespace in tests' ; echo $MSG
- check_namespace "Series"
- RET=$(($RET + $?))
- check_namespace "DataFrame"
- RET=$(($RET + $?))
+ for class in "Series" "DataFrame" "Index"; do
+  check_namespace ${class}
+  RET=$(($RET + $?))
+ done
  echo $MSG "DONE"
 fi
 

pandas/tests/arithmetic/test_numeric.py

@@ -42,7 +42,7 @@ def adjust_negative_zero(zero, expected):
 # TODO: remove this kludge once mypy stops giving false positives here
 # List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex]
 # See GH#29725
-ser_or_index: List[Any] = [Series, pd.Index]
+ser_or_index: List[Any] = [Series, Index]
 lefts: List[Any] = [pd.RangeIndex(10, 40, 10)]
 lefts.extend(
  [
@@ -100,7 +100,7 @@ def test_compare_invalid(self):
  def test_numeric_cmp_string_numexpr_path(self, box_with_array):
  # GH#36377, GH#35700
  box = box_with_array
- xbox = box if box is not pd.Index else np.ndarray
+ xbox = box if box is not Index else np.ndarray
 
  obj = Series(np.random.randn(10 ** 5))
  obj = tm.box_expected(obj, box, transpose=False)
@@ -126,7 +126,7 @@ def test_numeric_cmp_string_numexpr_path(self, box_with_array):
 class TestNumericArraylikeArithmeticWithDatetimeLike:
 
  # TODO: also check name retentention
- @pytest.mark.parametrize("box_cls", [np.array, pd.Index, Series])
+ @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
  @pytest.mark.parametrize(
  "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
  )
@@ -146,7 +146,7 @@ def test_mul_td64arr(self, left, box_cls):
  tm.assert_equal(result, expected)
 
  # TODO: also check name retentention
- @pytest.mark.parametrize("box_cls", [np.array, pd.Index, Series])
+ @pytest.mark.parametrize("box_cls", [np.array, Index, Series])
  @pytest.mark.parametrize(
  "left", lefts, ids=lambda x: type(x).__name__ + str(x.dtype)
  )
@@ -318,7 +318,7 @@ class TestDivisionByZero:
  def test_div_zero(self, zero, numeric_idx):
  idx = numeric_idx
 
- expected = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+ expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
  # We only adjust for Index, because Series does not yet apply
  # the adjustment correctly.
  expected2 = adjust_negative_zero(zero, expected)
@@ -331,7 +331,7 @@ def test_div_zero(self, zero, numeric_idx):
  def test_floordiv_zero(self, zero, numeric_idx):
  idx = numeric_idx
 
- expected = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+ expected = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
  # We only adjust for Index, because Series does not yet apply
  # the adjustment correctly.
  expected2 = adjust_negative_zero(zero, expected)
@@ -344,7 +344,7 @@ def test_floordiv_zero(self, zero, numeric_idx):
  def test_mod_zero(self, zero, numeric_idx):
  idx = numeric_idx
 
- expected = pd.Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
+ expected = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
  result = idx % zero
  tm.assert_index_equal(result, expected)
  ser_compat = Series(idx).astype("i8") % np.array(zero).astype("i8")
@@ -353,8 +353,8 @@ def test_mod_zero(self, zero, numeric_idx):
  def test_divmod_zero(self, zero, numeric_idx):
  idx = numeric_idx
 
- exleft = pd.Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
- exright = pd.Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
+ exleft = Index([np.nan, np.inf, np.inf, np.inf, np.inf], dtype=np.float64)
+ exright = Index([np.nan, np.nan, np.nan, np.nan, np.nan], dtype=np.float64)
  exleft = adjust_negative_zero(zero, exleft)
 
  result = divmod(idx, zero)
@@ -368,9 +368,7 @@ def test_div_negative_zero(self, zero, numeric_idx, op):
  return
  idx = numeric_idx - 3
 
- expected = pd.Index(
- [-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64
- )
+ expected = Index([-np.inf, -np.inf, -np.inf, np.nan, np.inf], dtype=np.float64)
  expected = adjust_negative_zero(zero, expected)
 
  result = op(idx, zero)
@@ -1045,7 +1043,7 @@ class TestUFuncCompat:
  [pd.Int64Index, pd.UInt64Index, pd.Float64Index, pd.RangeIndex, Series],
  )
  def test_ufunc_compat(self, holder):
- box = Series if holder is Series else pd.Index
+ box = Series if holder is Series else Index
 
  if holder is pd.RangeIndex:
  idx = pd.RangeIndex(0, 5)
@@ -1060,7 +1058,7 @@ def test_ufunc_compat(self, holder):
  )
  def test_ufunc_coercions(self, holder):
  idx = holder([1, 2, 3, 4, 5], name="x")
- box = Series if holder is Series else pd.Index
+ box = Series if holder is Series else Index
 
  result = np.sqrt(idx)
  assert result.dtype == "f8" and isinstance(result, box)
@@ -1104,7 +1102,7 @@ def test_ufunc_coercions(self, holder):
  )
  def test_ufunc_multiple_return_values(self, holder):
  obj = holder([1, 2, 3], name="x")
- box = Series if holder is Series else pd.Index
+ box = Series if holder is Series else Index
 
  result = np.modf(obj)
  assert isinstance(result, tuple)
@@ -1299,14 +1297,14 @@ def test_numeric_compat2(self):
  def test_addsub_arithmetic(self, dtype, delta):
  # GH#8142
  delta = dtype(delta)
- index = pd.Index([10, 11, 12], dtype=dtype)
+ index = Index([10, 11, 12], dtype=dtype)
  result = index + delta
- expected = pd.Index(index.values + delta, dtype=dtype)
+ expected = Index(index.values + delta, dtype=dtype)
  tm.assert_index_equal(result, expected)
 
  # this subtraction used to fail
  result = index - delta
- expected = pd.Index(index.values - delta, dtype=dtype)
+ expected = Index(index.values - delta, dtype=dtype)
  tm.assert_index_equal(result, expected)
 
  tm.assert_index_equal(index + index, 2 * index)

pandas/tests/arrays/categorical/test_constructors.py

@@ -638,7 +638,7 @@ def test_constructor_imaginary(self):
  def test_constructor_string_and_tuples(self):
  # GH 21416
  c = pd.Categorical(np.array(["c", ("a", "b"), ("b", "a"), "c"], dtype=object))
- expected_index = pd.Index([("a", "b"), ("b", "a"), "c"])
+ expected_index = Index([("a", "b"), ("b", "a"), "c"])
  assert c.categories.equals(expected_index)
 
  def test_interval(self):

pandas/tests/base/test_misc.py

@@ -200,6 +200,6 @@ def test_access_by_position(index):
 
 def test_get_indexer_non_unique_dtype_mismatch():
  # GH 25459
- indexes, missing = pd.Index(["A", "B"]).get_indexer_non_unique(pd.Index([0]))
+ indexes, missing = Index(["A", "B"]).get_indexer_non_unique(Index([0]))
  tm.assert_numpy_array_equal(np.array([-1], dtype=np.intp), indexes)
  tm.assert_numpy_array_equal(np.array([0], dtype=np.intp), missing)

pandas/tests/base/test_value_counts.py

@@ -33,7 +33,7 @@ def test_value_counts(index_or_series_obj):
  expected = Series(dict(counter.most_common()), dtype=np.int64, name=obj.name)
  expected.index = expected.index.astype(obj.dtype)
  if isinstance(obj, pd.MultiIndex):
- expected.index = pd.Index(expected.index)
+ expected.index = Index(expected.index)
 
  # TODO: Order of entries with the same count is inconsistent on CI (gh-32449)
  if obj.duplicated().any():

pandas/tests/dtypes/test_inference.py

@@ -584,7 +584,7 @@ def test_maybe_convert_objects_nullable_integer(self, exp):
 
  def test_maybe_convert_objects_bool_nan(self):
  # GH32146
- ind = pd.Index([True, False, np.nan], dtype=object)
+ ind = Index([True, False, np.nan], dtype=object)
  exp = np.array([True, False, np.nan], dtype=object)
  out = lib.maybe_convert_objects(ind.values, safe=1)
  tm.assert_numpy_array_equal(out, exp)

pandas/tests/frame/indexing/test_indexing.py

@@ -801,7 +801,7 @@ def test_setitem_empty_frame_with_boolean(self, dtype, kwargs):
 
  def test_setitem_with_empty_listlike(self):
  # GH #17101
- index = pd.Index([], name="idx")
+ index = Index([], name="idx")
  result = DataFrame(columns=["A"], index=index)
  result["A"] = []
  expected = DataFrame(columns=["A"], index=index)
@@ -1630,11 +1630,11 @@ def test_loc_getitem_index_namedtuple(self):
  @pytest.mark.parametrize("tpl", [tuple([1]), tuple([1, 2])])
  def test_loc_getitem_index_single_double_tuples(self, tpl):
  # GH 20991
- idx = pd.Index([tuple([1]), tuple([1, 2])], name="A", tupleize_cols=False)
+ idx = Index([tuple([1]), tuple([1, 2])], name="A", tupleize_cols=False)
  df = DataFrame(index=idx)
 
  result = df.loc[[tpl]]
- idx = pd.Index([tpl], name="A", tupleize_cols=False)
+ idx = Index([tpl], name="A", tupleize_cols=False)
  expected = DataFrame(index=idx)
  tm.assert_frame_equal(result, expected)
 

pandas/tests/frame/indexing/test_xs.py

@@ -3,8 +3,7 @@
 import numpy as np
 import pytest
 
-import pandas as pd
-from pandas import DataFrame, Series
+from pandas import DataFrame, Index, Series
 import pandas._testing as tm
 
 from pandas.tseries.offsets import BDay
@@ -59,7 +58,7 @@ def test_xs_corner(self):
  # no columns but Index(dtype=object)
  df = DataFrame(index=["a", "b", "c"])
  result = df.xs("a")
- expected = Series([], name="a", index=pd.Index([]), dtype=np.float64)
+ expected = Series([], name="a", index=Index([]), dtype=np.float64)
  tm.assert_series_equal(result, expected)
 
  def test_xs_duplicates(self):

pandas/tests/frame/methods/test_align.py

@@ -160,8 +160,8 @@ def test_align_mixed_int(self, mixed_int_frame):
  "l_ordered,r_ordered,expected",
  [
  [True, True, pd.CategoricalIndex],
- [True, False, pd.Index],
- [False, True, pd.Index],
+ [True, False, Index],
+ [False, True, Index],
  [False, False, pd.CategoricalIndex],
  ],
  )
@@ -196,7 +196,7 @@ def test_align_multiindex(self):
  midx = pd.MultiIndex.from_product(
  [range(2), range(3), range(2)], names=("a", "b", "c")
  )
- idx = pd.Index(range(2), name="b")
+ idx = Index(range(2), name="b")
  df1 = DataFrame(np.arange(12, dtype="int64"), index=midx)
  df2 = DataFrame(np.arange(2, dtype="int64"), index=idx)
 

pandas/tests/frame/methods/test_drop.py

@@ -141,7 +141,7 @@ def test_drop(self):
  tm.assert_frame_equal(nu_df.drop("b", axis="columns"), nu_df["a"])
  tm.assert_frame_equal(nu_df.drop([]), nu_df) # GH 16398
 
- nu_df = nu_df.set_index(pd.Index(["X", "Y", "X"]))
+ nu_df = nu_df.set_index(Index(["X", "Y", "X"]))
  nu_df.columns = list("abc")
  tm.assert_frame_equal(nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :])
  tm.assert_frame_equal(nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :])