CI/TST: Cleanups (#54362)

.circleci/setup_env.sh

@@ -48,10 +48,10 @@ source activate pandas-dev
 # downstream CI jobs that may also build pandas from source.
 export PANDAS_CI=1
 
-if pip list | grep -q ^pandas; then
+if pip show pandas 1>/dev/null; then
     echo
     echo "remove any installed pandas package w/o removing anything else"
-    pip uninstall -y pandas || true
+    pip uninstall -y pandas
 fi
 
 echo "Install pandas"

.github/workflows/unit-tests.yml

@@ -333,7 +333,6 @@ jobs:
       PYTEST_WORKERS: "auto"
       PANDAS_CI: 1
       PATTERN: "not slow and not network and not clipboard and not single_cpu"
-      COVERAGE: true
       PYTEST_TARGET: pandas
 
     steps:
@@ -351,7 +350,6 @@ jobs:
           python --version
           python -m pip install --upgrade pip setuptools wheel meson[ninja]==1.0.1 meson-python==0.13.1
           python -m pip install --pre --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple numpy
-          python -m pip install git+https://github.com/nedbat/coveragepy.git
           python -m pip install versioneer[toml]
           python -m pip install python-dateutil pytz tzdata cython hypothesis>=6.46.1 pytest>=7.3.2 pytest-xdist>=2.2.0 pytest-cov pytest-asyncio>=0.17
           python -m pip list

pandas/tests/arrays/test_datetimelike.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import array
 import re
 import warnings
 
@@ -12,7 +11,6 @@
     OutOfBoundsDatetime,
     Timestamp,
 )
-import pandas.util._test_decorators as td
 
 import pandas as pd
 from pandas import (
@@ -1328,70 +1326,3 @@ def test_from_pandas_array(dtype):
     result = idx_cls(arr)
     expected = idx_cls(data)
     tm.assert_index_equal(result, expected)
-
-
-@pytest.fixture(
-    params=[
-        "memoryview",
-        "array",
-        pytest.param("dask", marks=td.skip_if_no("dask.array")),
-        pytest.param("xarray", marks=td.skip_if_no("xarray")),
-    ]
-)
-def array_likes(request):
-    """
-    Fixture giving a numpy array and a parametrized 'data' object, which can
-    be a memoryview, array, dask or xarray object created from the numpy array.
-    """
-    # GH#24539 recognize e.g xarray, dask, ...
-    arr = np.array([1, 2, 3], dtype=np.int64)
-
-    name = request.param
-    if name == "memoryview":
-        data = memoryview(arr)
-    elif name == "array":
-        data = array.array("i", arr)
-    elif name == "dask":
-        import dask.array
-
-        data = dask.array.array(arr)
-    elif name == "xarray":
-        import xarray as xr
-
-        data = xr.DataArray(arr)
-
-    return arr, data
-
-
-@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
-def test_from_obscure_array(dtype, array_likes):
-    # GH#24539 recognize e.g xarray, dask, ...
-    # Note: we dont do this for PeriodArray bc _from_sequence won't accept
-    #  an array of integers
-    # TODO: could check with arraylike of Period objects
-    arr, data = array_likes
-
-    cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype]
-
-    expected = cls(arr)
-    result = cls._from_sequence(data)
-    tm.assert_extension_array_equal(result, expected)
-
-    func = {"M8[ns]": _sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
-    result = func(arr)[0]
-    expected = func(data)[0]
-    tm.assert_equal(result, expected)
-
-    if not isinstance(data, memoryview):
-        # FIXME(GH#44431) these raise on memoryview and attempted fix
-        #  fails on py3.10
-        func = {"M8[ns]": pd.to_datetime, "m8[ns]": pd.to_timedelta}[dtype]
-        result = func(arr).array
-        expected = func(data).array
-        tm.assert_equal(result, expected)
-
-    # Let's check the Indexes while we're here
-    idx_cls = {"M8[ns]": DatetimeIndex, "m8[ns]": TimedeltaIndex}[dtype]
-    result = idx_cls(arr)
-    expected = idx_cls(data)
-    tm.assert_index_equal(result, expected)

pandas/tests/io/parser/test_upcast.py

@@ -38,9 +38,6 @@ def test_maybe_upcast(any_real_numpy_dtype):
 
 def test_maybe_upcast_no_na(any_real_numpy_dtype):
     # GH#36712
-    if any_real_numpy_dtype == "float32":
-        pytest.skip()
-
     arr = np.array([1, 2, 3], dtype=any_real_numpy_dtype)
     result = _maybe_upcast(arr, use_dtype_backend=True)
 

pandas/tests/io/test_gcs.py

@@ -22,7 +22,8 @@
 @pytest.fixture
 def gcs_buffer():
     """Emulate GCS using a binary buffer."""
-    import fsspec
+    pytest.importorskip("gcsfs")
+    fsspec = pytest.importorskip("fsspec")
 
     gcs_buffer = BytesIO()
     gcs_buffer.close = lambda: True
@@ -43,7 +44,6 @@ def ls(self, path, **kwargs):
     return gcs_buffer
 
 
-@td.skip_if_no("gcsfs")
 # Patches pyarrow; other processes should not pick up change
 @pytest.mark.single_cpu
 @pytest.mark.parametrize("format", ["csv", "json", "parquet", "excel", "markdown"])
@@ -131,7 +131,6 @@ def assert_equal_zip_safe(result: bytes, expected: bytes, compression: str):
         assert result == expected
 
 
-@td.skip_if_no("gcsfs")
 @pytest.mark.parametrize("encoding", ["utf-8", "cp1251"])
 def test_to_csv_compression_encoding_gcs(
     gcs_buffer, compression_only, encoding, compression_to_extension
@@ -177,10 +176,11 @@ def test_to_csv_compression_encoding_gcs(
     tm.assert_frame_equal(df, read_df)
 
 
-@td.skip_if_no("fastparquet")
-@td.skip_if_no("gcsfs")
 def test_to_parquet_gcs_new_file(monkeypatch, tmpdir):
     """Regression test for writing to a not-yet-existent GCS Parquet file."""
+    pytest.importorskip("fastparquet")
+    pytest.importorskip("gcsfs")
+
     from fsspec import AbstractFileSystem
 
     df1 = DataFrame(

pandas/tests/test_downstream.py

@@ -1,6 +1,7 @@
 """
 Testing that we work in the downstream packages
 """
+import array
 import importlib
 import subprocess
 import sys
@@ -14,9 +15,17 @@
 import pandas as pd
 from pandas import (
     DataFrame,
+    DatetimeIndex,
     Series,
+    TimedeltaIndex,
 )
 import pandas._testing as tm
+from pandas.core.arrays import (
+    DatetimeArray,
+    TimedeltaArray,
+)
+from pandas.core.arrays.datetimes import _sequence_to_dt64ns
+from pandas.core.arrays.timedeltas import sequence_to_td64ns
 
 
 def import_module(name):
@@ -277,3 +286,70 @@ def __radd__(self, other):
 
     assert right.__add__(left) is NotImplemented
     assert right + left is left
+
+
+@pytest.fixture(
+    params=[
+        "memoryview",
+        "array",
+        pytest.param("dask", marks=td.skip_if_no("dask.array")),
+        pytest.param("xarray", marks=td.skip_if_no("xarray")),
+    ]
+)
+def array_likes(request):
+    """
+    Fixture giving a numpy array and a parametrized 'data' object, which can
+    be a memoryview, array, dask or xarray object created from the numpy array.
+    """
+    # GH#24539 recognize e.g xarray, dask, ...
+    arr = np.array([1, 2, 3], dtype=np.int64)
+
+    name = request.param
+    if name == "memoryview":
+        data = memoryview(arr)
+    elif name == "array":
+        data = array.array("i", arr)
+    elif name == "dask":
+        import dask.array
+
+        data = dask.array.array(arr)
+    elif name == "xarray":
+        import xarray as xr
+
+        data = xr.DataArray(arr)
+
+    return arr, data
+
+
+@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
+def test_from_obscure_array(dtype, array_likes):
+    # GH#24539 recognize e.g xarray, dask, ...
+    # Note: we dont do this for PeriodArray bc _from_sequence won't accept
+    #  an array of integers
+    # TODO: could check with arraylike of Period objects
+    arr, data = array_likes
+
+    cls = {"M8[ns]": DatetimeArray, "m8[ns]": TimedeltaArray}[dtype]
+
+    expected = cls(arr)
+    result = cls._from_sequence(data)
+    tm.assert_extension_array_equal(result, expected)
+
+    func = {"M8[ns]": _sequence_to_dt64ns, "m8[ns]": sequence_to_td64ns}[dtype]
+    result = func(arr)[0]
+    expected = func(data)[0]
+    tm.assert_equal(result, expected)
+
+    if not isinstance(data, memoryview):
+        # FIXME(GH#44431) these raise on memoryview and attempted fix
+        #  fails on py3.10
+        func = {"M8[ns]": pd.to_datetime, "m8[ns]": pd.to_timedelta}[dtype]
+        result = func(arr).array
+        expected = func(data).array
+        tm.assert_equal(result, expected)
+
+    # Let's check the Indexes while we're here
+    idx_cls = {"M8[ns]": DatetimeIndex, "m8[ns]": TimedeltaIndex}[dtype]
+    result = idx_cls(arr)
+    expected = idx_cls(data)
+    tm.assert_index_equal(result, expected)