CLN: Assorted

pandas/io/sas/sas.pyx

@@ -375,9 +375,9 @@ cdef class Parser:
     def read(self, int nrows):
         cdef:
             bint done
-            Py_ssize_t i
+            Py_ssize_t _
 
-        for i in range(nrows):
+        for _ in range(nrows):
             done = self.readline()
             if done:
                 break

pandas/tests/frame/test_ufunc.py

@@ -117,6 +117,7 @@ def test_binary_input_aligns_columns(request, dtype_a, dtype_b):
     df1 = pd.DataFrame({"A": [1, 2], "B": [3, 4]}).astype(dtype_a)
 
     if isinstance(dtype_a, dict) and isinstance(dtype_b, dict):
+        dtype_b = dtype_b.copy()
         dtype_b["C"] = dtype_b.pop("B")
     df2 = pd.DataFrame({"A": [1, 2], "C": [3, 4]}).astype(dtype_b)
     # As of 2.0, align first before applying the ufunc

pandas/tests/io/test_pickle.py

@@ -15,7 +15,6 @@
 import datetime
 import functools
 from functools import partial
-import glob
 import gzip
 import io
 import os
@@ -53,7 +52,7 @@
 )
 
 
-@pytest.fixture(scope="module")
+@pytest.fixture
 def current_pickle_data():
     # our current version pickle data
     from pandas.tests.io.generate_legacy_storage_files import create_pickle_data
@@ -82,15 +81,6 @@ def compare_element(result, expected, typ):
         comparator(result, expected)
 
 
-legacy_dirname = os.path.join(os.path.dirname(__file__), "data", "legacy_pickle")
-files = glob.glob(os.path.join(legacy_dirname, "*", "*.pickle"))
-
-
-@pytest.fixture(params=files)
-def legacy_pickle(request, datapath):
-    return datapath(request.param)
-
-
 # ---------------------
 # tests
 # ---------------------
@@ -125,50 +115,54 @@ def test_flatten_buffer(data):
         assert result.shape == (result.nbytes,)
 
 
-def test_pickles(legacy_pickle):
+def test_pickles(datapath):
     if not is_platform_little_endian():
         pytest.skip("known failure on non-little endian")
 
-    data = pd.read_pickle(legacy_pickle)
-
-    for typ, dv in data.items():
-        for dt, result in dv.items():
-            expected = data[typ][dt]
-
-            if typ == "series" and dt == "ts":
-                # GH 7748
-                tm.assert_series_equal(result, expected)
-                assert result.index.freq == expected.index.freq
-                assert not result.index.freq.normalize
-                tm.assert_series_equal(result > 0, expected > 0)
-
-                # GH 9291
-                freq = result.index.freq
-                assert freq + Day(1) == Day(2)
-
-                res = freq + pd.Timedelta(hours=1)
-                assert isinstance(res, pd.Timedelta)
-                assert res == pd.Timedelta(days=1, hours=1)
-
-                res = freq + pd.Timedelta(nanoseconds=1)
-                assert isinstance(res, pd.Timedelta)
-                assert res == pd.Timedelta(days=1, nanoseconds=1)
-            elif typ == "index" and dt == "period":
-                tm.assert_index_equal(result, expected)
-                assert isinstance(result.freq, MonthEnd)
-                assert result.freq == MonthEnd()
-                assert result.freqstr == "M"
-                tm.assert_index_equal(result.shift(2), expected.shift(2))
-            elif typ == "series" and dt in ("dt_tz", "cat"):
-                tm.assert_series_equal(result, expected)
-            elif typ == "frame" and dt in (
-                "dt_mixed_tzs",
-                "cat_onecol",
-                "cat_and_float",
-            ):
-                tm.assert_frame_equal(result, expected)
-            else:
-                compare_element(result, expected, typ)
+    # For loop for compat with --strict-data-files
+    for legacy_pickle in Path(__file__).parent.glob("data/legacy_pickle/*/*.p*kl*"):
+        legacy_pickle = datapath(legacy_pickle)
+
+        data = pd.read_pickle(legacy_pickle)
+
+        for typ, dv in data.items():
+            for dt, result in dv.items():
+                expected = data[typ][dt]
+
+                if typ == "series" and dt == "ts":
+                    # GH 7748
+                    tm.assert_series_equal(result, expected)
+                    assert result.index.freq == expected.index.freq
+                    assert not result.index.freq.normalize
+                    tm.assert_series_equal(result > 0, expected > 0)
+
+                    # GH 9291
+                    freq = result.index.freq
+                    assert freq + Day(1) == Day(2)
+
+                    res = freq + pd.Timedelta(hours=1)
+                    assert isinstance(res, pd.Timedelta)
+                    assert res == pd.Timedelta(days=1, hours=1)
+
+                    res = freq + pd.Timedelta(nanoseconds=1)
+                    assert isinstance(res, pd.Timedelta)
+                    assert res == pd.Timedelta(days=1, nanoseconds=1)
+                elif typ == "index" and dt == "period":
+                    tm.assert_index_equal(result, expected)
+                    assert isinstance(result.freq, MonthEnd)
+                    assert result.freq == MonthEnd()
+                    assert result.freqstr == "M"
+                    tm.assert_index_equal(result.shift(2), expected.shift(2))
+                elif typ == "series" and dt in ("dt_tz", "cat"):
+                    tm.assert_series_equal(result, expected)
+                elif typ == "frame" and dt in (
+                    "dt_mixed_tzs",
+                    "cat_onecol",
+                    "cat_and_float",
+                ):
+                    tm.assert_frame_equal(result, expected)
+                else:
+                    compare_element(result, expected, typ)
 
 
 def python_pickler(obj, path):
@@ -580,9 +574,15 @@ def test_pickle_big_dataframe_compression(protocol, compression):
     tm.assert_frame_equal(df, result)
 
 
-def test_pickle_frame_v124_unpickle_130():
+def test_pickle_frame_v124_unpickle_130(datapath):
     # GH#42345 DataFrame created in 1.2.x, unpickle in 1.3.x
-    path = os.path.join(legacy_dirname, "1.2.4", "empty_frame_v1_2_4-GH#42345.pkl")
+    path = datapath(
+        Path(__file__).parent,
+        "data",
+        "legacy_pickle",
+        "1.2.4",
+        "empty_frame_v1_2_4-GH#42345.pkl",
+    )
     with open(path, "rb") as fd:
         df = pickle.load(fd)