CLN: Testing and unused (pandas-dev#49754)

* Remove unused rand_bools

* Remove references to append

* Reuse existing fixtures

* Replace another top level fixture

Author: mroeschke

doc/source/user_guide/merging.rst

@@ -151,8 +151,7 @@ It's not a stretch to see how this can be very useful. More detail on this
 functionality below.
 
 .. note::
-   It is worth noting that :func:`~pandas.concat` (and therefore
-   :func:`~pandas.append`) makes a full copy of the data, and that constantly
+   It is worth noting that :func:`~pandas.concat` makes a full copy of the data, and that constantly
    reusing this function can create a significant performance hit. If you need
    to use the operation over several datasets, use a list comprehension.
 

pandas/_testing/__init__.py

@@ -61,7 +61,6 @@
     write_to_compressed,
 )
 from pandas._testing._random import (
-    randbool,
     rands,
     rands_array,
 )
@@ -1121,7 +1120,6 @@ def shares_memory(left, right) -> bool:
     "NULL_OBJECTS",
     "OBJECT_DTYPES",
     "raise_assert_detail",
-    "randbool",
     "rands",
     "reset_display_options",
     "RNGContext",

pandas/_testing/_random.py

@@ -4,11 +4,6 @@
 
 from pandas._typing import NpDtype
 
-
-def randbool(size=(), p: float = 0.5):
-    return np.random.rand(*size) <= p
-
-
 RANDS_CHARS = np.array(list(string.ascii_letters + string.digits), dtype=(np.str_, 1))
 RANDU_CHARS = np.array(
     list("".join(map(chr, range(1488, 1488 + 26))) + string.digits),

pandas/conftest.py

@@ -151,9 +151,6 @@ def pytest_collection_modifyitems(items, config) -> None:
     # Warnings from doctests that can be ignored; place reason in comment above.
     # Each entry specifies (path, message) - see the ignore_doctest_warning function
     ignored_doctest_warnings = [
-        # Deprecations where the docstring will emit a warning
-        ("DataFrame.append", "The frame.append method is deprecated"),
-        ("Series.append", "The series.append method is deprecated"),
         # Docstring divides by zero to show behavior difference
         ("missing.mask_zero_div_zero", "divide by zero encountered"),
         # Docstring demonstrates the call raises a warning

pandas/tests/dtypes/cast/test_promote.py

@@ -27,43 +27,6 @@
 import pandas as pd
 
 
-@pytest.fixture(
-    params=[
-        bool,
-        "uint8",
-        "int32",
-        "uint64",
-        "float32",
-        "float64",
-        "complex64",
-        "complex128",
-        "M8[ns]",
-        "m8[ns]",
-        str,
-        bytes,
-        object,
-    ]
-)
-def any_numpy_dtype_reduced(request):
-    """
-    Parameterized fixture for numpy dtypes, reduced from any_numpy_dtype.
-
-    * bool
-    * 'int32'
-    * 'uint64'
-    * 'float32'
-    * 'float64'
-    * 'complex64'
-    * 'complex128'
-    * 'M8[ns]'
-    * 'M8[ns]'
-    * str
-    * bytes
-    * object
-    """
-    return request.param
-
-
 def _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar=None):
     """
     Auxiliary function to unify testing of scalar/array promotion.
@@ -307,9 +270,9 @@ def test_maybe_promote_float_with_float(dtype, fill_value, expected_dtype):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_bool_with_any(any_numpy_dtype_reduced):
+def test_maybe_promote_bool_with_any(any_numpy_dtype):
     dtype = np.dtype(bool)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -321,8 +284,8 @@ def test_maybe_promote_bool_with_any(any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_any_with_bool(any_numpy_dtype_reduced):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_bool(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
     fill_value = True
 
     # filling anything but bool with bool casts to object
@@ -333,9 +296,9 @@ def test_maybe_promote_any_with_bool(any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype_reduced):
+def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype):
     dtype = np.dtype(bytes_dtype)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -347,8 +310,8 @@ def test_maybe_promote_bytes_with_any(bytes_dtype, any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_any_with_bytes(any_numpy_dtype_reduced):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_bytes(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype
     fill_value = b"abc"
@@ -361,9 +324,9 @@ def test_maybe_promote_any_with_bytes(any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype_reduced):
+def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype):
     dtype = np.dtype(datetime64_dtype)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -390,8 +353,8 @@ def test_maybe_promote_datetime64_with_any(datetime64_dtype, any_numpy_dtype_red
     ],
     ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
 )
-def test_maybe_promote_any_with_datetime64(any_numpy_dtype_reduced, fill_value):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_datetime64(any_numpy_dtype, fill_value):
+    dtype = np.dtype(any_numpy_dtype)
 
     # filling datetime with anything but datetime casts to object
     if is_datetime64_dtype(dtype):
@@ -421,9 +384,9 @@ def test_maybe_promote_any_with_datetime64(any_numpy_dtype_reduced, fill_value):
     ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
 )
 def test_maybe_promote_any_numpy_dtype_with_datetimetz(
-    any_numpy_dtype_reduced, tz_aware_fixture, fill_value
+    any_numpy_dtype, tz_aware_fixture, fill_value
 ):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+    dtype = np.dtype(any_numpy_dtype)
     fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture)
 
     fill_value = pd.Series([fill_value], dtype=fill_dtype)[0]
@@ -435,9 +398,9 @@ def test_maybe_promote_any_numpy_dtype_with_datetimetz(
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype_reduced):
+def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype):
     dtype = np.dtype(timedelta64_dtype)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -459,10 +422,8 @@ def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype_r
     [pd.Timedelta(days=1), np.timedelta64(24, "h"), datetime.timedelta(1)],
     ids=["pd.Timedelta", "np.timedelta64", "datetime.timedelta"],
 )
-def test_maybe_promote_any_with_timedelta64(
-    any_numpy_dtype_reduced, fill_value, request
-):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_timedelta64(any_numpy_dtype, fill_value, request):
+    dtype = np.dtype(any_numpy_dtype)
 
     # filling anything but timedelta with timedelta casts to object
     if is_timedelta64_dtype(dtype):
@@ -489,9 +450,9 @@ def test_maybe_promote_any_with_timedelta64(
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype_reduced):
+def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype):
     dtype = np.dtype(string_dtype)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -503,8 +464,8 @@ def test_maybe_promote_string_with_any(string_dtype, any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_any_with_string(any_numpy_dtype_reduced):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_string(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype
     fill_value = "abc"
@@ -516,9 +477,9 @@ def test_maybe_promote_any_with_string(any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype_reduced):
+def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype):
     dtype = np.dtype(object_dtype)
-    fill_dtype = np.dtype(any_numpy_dtype_reduced)
+    fill_dtype = np.dtype(any_numpy_dtype)
 
     # create array of given dtype; casts "1" to correct dtype
     fill_value = np.array([1], dtype=fill_dtype)[0]
@@ -530,8 +491,8 @@ def test_maybe_promote_object_with_any(object_dtype, any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_any_with_object(any_numpy_dtype_reduced):
-    dtype = np.dtype(any_numpy_dtype_reduced)
+def test_maybe_promote_any_with_object(any_numpy_dtype):
+    dtype = np.dtype(any_numpy_dtype)
 
     # create array of object dtype from a scalar value (i.e. passing
     # dtypes.common.is_scalar), which can however not be cast to int/float etc.
@@ -544,9 +505,9 @@ def test_maybe_promote_any_with_object(any_numpy_dtype_reduced):
     _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
-def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype_reduced, nulls_fixture):
+def test_maybe_promote_any_numpy_dtype_with_na(any_numpy_dtype, nulls_fixture):
     fill_value = nulls_fixture
-    dtype = np.dtype(any_numpy_dtype_reduced)
+    dtype = np.dtype(any_numpy_dtype)
 
     if isinstance(fill_value, Decimal):
         # Subject to change, but ATM (When Decimal(NAN) is being added to nulls_fixture)

pandas/tests/frame/methods/test_drop_duplicates.py

@@ -441,7 +441,6 @@ def test_drop_duplicates_null_in_object_column(nulls_fixture):
     tm.assert_frame_equal(result, df)
 
 
-@pytest.mark.parametrize("keep", ["first", "last", False])
 def test_drop_duplicates_series_vs_dataframe(keep):
     # GH#14192
     df = DataFrame(

pandas/tests/series/methods/test_duplicated.py

@@ -69,7 +69,6 @@ def test_duplicated_mask(keep, vals):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("keep", ["last", "first", False])
 def test_duplicated_mask_no_duplicated_na(keep):
     # GH#48150
     ser = Series([1, 2, NA], dtype="Int64")

pandas/tests/window/test_rolling.py

@@ -170,7 +170,6 @@ def test_numpy_compat(method):
             getattr(r, method)(dtype=np.float64)
 
 
-@pytest.mark.parametrize("closed", ["right", "left", "both", "neither"])
 def test_closed_fixed(closed, arithmetic_win_operators):
     # GH 34315
     func_name = arithmetic_win_operators