STYLE: Resolved Inconsistent namespace (#40286)

* STYLE: Inconsistent namespace - tools (#39992)

* Pre-Commit Fixes

* STYLE: Resolved Inconsistent namespace except test_algos.py - (#39992)

* STYLE: Resolved Inconsistent namespace by excluding test_algos.py

* test_algos.py fixes

* Updated test_algos.py

* STYLE: inconsistent-namespace-usage fixes

* updated test_interval.py

* FIX: Improved varible names

* test_interval.py fixes

* FIX: Resolved confusing variable names

* test_string.py fixes

* getitem.py fixes

* rename more variables to arr

Co-authored-by: Marco Gorelli <[email protected]>

Author: deepang17

.pre-commit-config.yaml

@@ -95,7 +95,7 @@ repos:
         entry: python scripts/check_for_inconsistent_pandas_namespace.py
         language: python
         types: [python]
-        files: ^pandas/tests/frame/
+        files: ^pandas/tests/
     -   id: incorrect-code-directives
         name: Check for incorrect code block or IPython directives
         language: pygrep

pandas/tests/arithmetic/test_interval.py

@@ -50,7 +50,7 @@ def left_right_dtypes(request):
 
 
 @pytest.fixture
-def array(left_right_dtypes):
+def interval_array(left_right_dtypes):
     """
     Fixture to generate an IntervalArray of various dtypes containing NA if possible
     """
@@ -98,42 +98,42 @@ def interval_constructor(self, request):
         """
         return request.param
 
-    def elementwise_comparison(self, op, array, other):
+    def elementwise_comparison(self, op, interval_array, other):
         """
         Helper that performs elementwise comparisons between `array` and `other`
         """
-        other = other if is_list_like(other) else [other] * len(array)
-        expected = np.array([op(x, y) for x, y in zip(array, other)])
+        other = other if is_list_like(other) else [other] * len(interval_array)
+        expected = np.array([op(x, y) for x, y in zip(interval_array, other)])
         if isinstance(other, Series):
             return Series(expected, index=other.index)
         return expected
 
-    def test_compare_scalar_interval(self, op, array):
+    def test_compare_scalar_interval(self, op, interval_array):
         # matches first interval
-        other = array[0]
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        other = interval_array[0]
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
         # matches on a single endpoint but not both
-        other = Interval(array.left[0], array.right[1])
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        other = Interval(interval_array.left[0], interval_array.right[1])
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
     def test_compare_scalar_interval_mixed_closed(self, op, closed, other_closed):
-        array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
         other = Interval(0, 1, closed=other_closed)
 
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_compare_scalar_na(self, op, array, nulls_fixture, request):
-        result = op(array, nulls_fixture)
-        expected = self.elementwise_comparison(op, array, nulls_fixture)
+    def test_compare_scalar_na(self, op, interval_array, nulls_fixture, request):
+        result = op(interval_array, nulls_fixture)
+        expected = self.elementwise_comparison(op, interval_array, nulls_fixture)
 
-        if nulls_fixture is pd.NA and array.dtype.subtype != "int64":
+        if nulls_fixture is pd.NA and interval_array.dtype.subtype != "int64":
             mark = pytest.mark.xfail(
                 reason="broken for non-integer IntervalArray; see GH 31882"
             )
@@ -154,38 +154,40 @@ def test_compare_scalar_na(self, op, array, nulls_fixture, request):
             Period("2017-01-01", "D"),
         ],
     )
-    def test_compare_scalar_other(self, op, array, other):
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+    def test_compare_scalar_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_compare_list_like_interval(self, op, array, interval_constructor):
+    def test_compare_list_like_interval(self, op, interval_array, interval_constructor):
         # same endpoints
-        other = interval_constructor(array.left, array.right)
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        other = interval_constructor(interval_array.left, interval_array.right)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_equal(result, expected)
 
         # different endpoints
-        other = interval_constructor(array.left[::-1], array.right[::-1])
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        other = interval_constructor(
+            interval_array.left[::-1], interval_array.right[::-1]
+        )
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_equal(result, expected)
 
         # all nan endpoints
         other = interval_constructor([np.nan] * 4, [np.nan] * 4)
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_equal(result, expected)
 
     def test_compare_list_like_interval_mixed_closed(
         self, op, interval_constructor, closed, other_closed
     ):
-        array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
+        interval_array = IntervalArray.from_arrays(range(2), range(1, 3), closed=closed)
         other = interval_constructor(range(2), range(1, 3), closed=other_closed)
 
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_equal(result, expected)
 
     @pytest.mark.parametrize(
@@ -206,17 +208,17 @@ def test_compare_list_like_interval_mixed_closed(
             ),
         ],
     )
-    def test_compare_list_like_object(self, op, array, other):
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+    def test_compare_list_like_object(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
-    def test_compare_list_like_nan(self, op, array, nulls_fixture, request):
+    def test_compare_list_like_nan(self, op, interval_array, nulls_fixture, request):
         other = [nulls_fixture] * 4
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
 
-        if nulls_fixture is pd.NA and array.dtype.subtype != "i8":
+        if nulls_fixture is pd.NA and interval_array.dtype.subtype != "i8":
             reason = "broken for non-integer IntervalArray; see GH 31882"
             mark = pytest.mark.xfail(reason=reason)
             request.node.add_marker(mark)
@@ -239,18 +241,18 @@ def test_compare_list_like_nan(self, op, array, nulls_fixture, request):
         ],
         ids=lambda x: str(x.dtype),
     )
-    def test_compare_list_like_other(self, op, array, other):
-        result = op(array, other)
-        expected = self.elementwise_comparison(op, array, other)
+    def test_compare_list_like_other(self, op, interval_array, other):
+        result = op(interval_array, other)
+        expected = self.elementwise_comparison(op, interval_array, other)
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize("length", [1, 3, 5])
     @pytest.mark.parametrize("other_constructor", [IntervalArray, list])
     def test_compare_length_mismatch_errors(self, op, other_constructor, length):
-        array = IntervalArray.from_arrays(range(4), range(1, 5))
+        interval_array = IntervalArray.from_arrays(range(4), range(1, 5))
         other = other_constructor([Interval(0, 1)] * length)
         with pytest.raises(ValueError, match="Lengths must match to compare"):
-            op(array, other)
+            op(interval_array, other)
 
     @pytest.mark.parametrize(
         "constructor, expected_type, assert_func",

pandas/tests/arrays/sparse/test_array.py

@@ -522,7 +522,7 @@ def test_astype_all(self, any_real_dtype):
         tm.assert_numpy_array_equal(np.asarray(res.to_dense()), vals.astype(typ))
 
     @pytest.mark.parametrize(
-        "array, dtype, expected",
+        "arr, dtype, expected",
         [
             (
                 SparseArray([0, 1]),
@@ -557,8 +557,8 @@ def test_astype_all(self, any_real_dtype):
             ),
         ],
     )
-    def test_astype_more(self, array, dtype, expected):
-        result = array.astype(dtype)
+    def test_astype_more(self, arr, dtype, expected):
+        result = arr.astype(dtype)
         tm.assert_sp_array_equal(result, expected)
 
     def test_astype_nan_raises(self):

pandas/tests/arrays/string_/test_string.py

@@ -223,31 +223,31 @@ def test_mul(dtype, request):
 
 @pytest.mark.xfail(reason="GH-28527")
 def test_add_strings(dtype):
-    array = pd.array(["a", "b", "c", "d"], dtype=dtype)
+    arr = pd.array(["a", "b", "c", "d"], dtype=dtype)
     df = pd.DataFrame([["t", "u", "v", "w"]])
-    assert array.__add__(df) is NotImplemented
+    assert arr.__add__(df) is NotImplemented
 
-    result = array + df
+    result = arr + df
     expected = pd.DataFrame([["at", "bu", "cv", "dw"]]).astype(dtype)
     tm.assert_frame_equal(result, expected)
 
-    result = df + array
+    result = df + arr
     expected = pd.DataFrame([["ta", "ub", "vc", "wd"]]).astype(dtype)
     tm.assert_frame_equal(result, expected)
 
 
 @pytest.mark.xfail(reason="GH-28527")
 def test_add_frame(dtype):
-    array = pd.array(["a", "b", np.nan, np.nan], dtype=dtype)
+    arr = pd.array(["a", "b", np.nan, np.nan], dtype=dtype)
     df = pd.DataFrame([["x", np.nan, "y", np.nan]])
 
-    assert array.__add__(df) is NotImplemented
+    assert arr.__add__(df) is NotImplemented
 
-    result = array + df
+    result = arr + df
     expected = pd.DataFrame([["ax", np.nan, np.nan, np.nan]]).astype(dtype)
     tm.assert_frame_equal(result, expected)
 
-    result = df + array
+    result = df + arr
     expected = pd.DataFrame([["xa", np.nan, np.nan, np.nan]]).astype(dtype)
     tm.assert_frame_equal(result, expected)
 

pandas/tests/arrays/test_datetimelike.py

@@ -1160,7 +1160,7 @@ def test_strftime_nat(self):
 
 
 @pytest.mark.parametrize(
-    "array,casting_nats",
+    "arr,casting_nats",
     [
         (
             TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
@@ -1174,17 +1174,17 @@ def test_strftime_nat(self):
     ],
     ids=lambda x: type(x).__name__,
 )
-def test_casting_nat_setitem_array(array, casting_nats):
-    expected = type(array)._from_sequence([NaT, array[1], array[2]])
+def test_casting_nat_setitem_array(arr, casting_nats):
+    expected = type(arr)._from_sequence([NaT, arr[1], arr[2]])
 
     for nat in casting_nats:
-        arr = array.copy()
+        arr = arr.copy()
         arr[0] = nat
         tm.assert_equal(arr, expected)
 
 
 @pytest.mark.parametrize(
-    "array,non_casting_nats",
+    "arr,non_casting_nats",
     [
         (
             TimedeltaIndex(["1 Day", "3 Hours", "NaT"])._data,
@@ -1201,50 +1201,50 @@ def test_casting_nat_setitem_array(array, casting_nats):
     ],
     ids=lambda x: type(x).__name__,
 )
-def test_invalid_nat_setitem_array(array, non_casting_nats):
+def test_invalid_nat_setitem_array(arr, non_casting_nats):
     msg = (
         "value should be a '(Timestamp|Timedelta|Period)', 'NaT', or array of those. "
         "Got '(timedelta64|datetime64|int)' instead."
     )
 
     for nat in non_casting_nats:
         with pytest.raises(TypeError, match=msg):
-            array[0] = nat
+            arr[0] = nat
 
 
 @pytest.mark.parametrize(
-    "array",
+    "arr",
     [
         pd.date_range("2000", periods=4).array,
         pd.timedelta_range("2000", periods=4).array,
     ],
 )
-def test_to_numpy_extra(array):
+def test_to_numpy_extra(arr):
     if np_version_under1p18:
         # np.isnan(NaT) raises, so use pandas'
         isnan = pd.isna
     else:
         isnan = np.isnan
 
-    array[0] = NaT
-    original = array.copy()
+    arr[0] = NaT
+    original = arr.copy()
 
-    result = array.to_numpy()
+    result = arr.to_numpy()
     assert isnan(result[0])
 
-    result = array.to_numpy(dtype="int64")
+    result = arr.to_numpy(dtype="int64")
     assert result[0] == -9223372036854775808
 
-    result = array.to_numpy(dtype="int64", na_value=0)
+    result = arr.to_numpy(dtype="int64", na_value=0)
     assert result[0] == 0
 
-    result = array.to_numpy(na_value=array[1].to_numpy())
+    result = arr.to_numpy(na_value=arr[1].to_numpy())
     assert result[0] == result[1]
 
-    result = array.to_numpy(na_value=array[1].to_numpy(copy=False))
+    result = arr.to_numpy(na_value=arr[1].to_numpy(copy=False))
     assert result[0] == result[1]
 
-    tm.assert_equal(array, original)
+    tm.assert_equal(arr, original)
 
 
 @pytest.mark.parametrize("as_index", [True, False])