TST/REF: collect tests by method

pandas/tests/base/test_conversion.py

@@ -5,7 +5,7 @@
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 
 import pandas as pd
-from pandas import CategoricalIndex, Series, Timedelta, Timestamp
+from pandas import CategoricalIndex, Series, Timedelta, Timestamp, date_range
 import pandas._testing as tm
 from pandas.core.arrays import (
     DatetimeArray,
@@ -449,3 +449,39 @@ def test_to_numpy_dataframe_single_block_no_mutate():
     expected = pd.DataFrame(np.array([1.0, 2.0, np.nan]))
     result.to_numpy(na_value=0.0)
     tm.assert_frame_equal(result, expected)
+
+
+class TestAsArray:
+    @pytest.mark.parametrize("tz", [None, "US/Central"])
+    def test_asarray_object_dt64(self, tz):
+        ser = Series(date_range("2000", periods=2, tz=tz))
+
+        with tm.assert_produces_warning(None):
+            # Future behavior (for tzaware case) with no warning
+            result = np.asarray(ser, dtype=object)
+
+        expected = np.array(
+            [Timestamp("2000-01-01", tz=tz), Timestamp("2000-01-02", tz=tz)]
+        )
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_asarray_tz_naive(self):
+        # This shouldn't produce a warning.
+        ser = Series(date_range("2000", periods=2))
+        expected = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]")
+        result = np.asarray(ser)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_asarray_tz_aware(self):
+        tz = "US/Central"
+        ser = Series(date_range("2000", periods=2, tz=tz))
+        expected = np.array(["2000-01-01T06", "2000-01-02T06"], dtype="M8[ns]")
+        result = np.asarray(ser, dtype="datetime64[ns]")
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        # Old behavior with no warning
+        result = np.asarray(ser, dtype="M8[ns]")
+
+        tm.assert_numpy_array_equal(result, expected)

pandas/tests/frame/indexing/test_getitem.py

@@ -1,6 +1,8 @@
+import numpy as np
 import pytest
 
-from pandas import DataFrame, MultiIndex
+from pandas import DataFrame, MultiIndex, period_range
+import pandas._testing as tm
 
 
 class TestGetitem:
@@ -14,3 +16,16 @@ def test_getitem_unused_level_raises(self):
 
         with pytest.raises(KeyError, match="notevenone"):
             df["notevenone"]
+
+    def test_getitem_periodindex(self):
+        rng = period_range("1/1/2000", periods=5)
+        df = DataFrame(np.random.randn(10, 5), columns=rng)
+
+        ts = df[rng[0]]
+        tm.assert_series_equal(ts, df.iloc[:, 0])
+
+        # GH#1211; smoketest unrelated to the rest of this test
+        repr(df)
+
+        ts = df["1/1/2000"]
+        tm.assert_series_equal(ts, df.iloc[:, 0])

pandas/tests/frame/methods/test_reindex.py

@@ -1,4 +1,5 @@
 from datetime import datetime
+import inspect
 from itertools import permutations
 
 import numpy as np
@@ -846,3 +847,20 @@ def test_reindex_with_categoricalindex(self):
             df.reindex(["a"], level=1)
         with pytest.raises(NotImplementedError, match=msg.format("limit")):
             df.reindex(["a"], limit=2)
+
+    def test_reindex_signature(self):
+        sig = inspect.signature(DataFrame.reindex)
+        parameters = set(sig.parameters)
+        assert parameters == {
+            "self",
+            "labels",
+            "index",
+            "columns",
+            "axis",
+            "limit",
+            "copy",
+            "level",
+            "method",
+            "fill_value",
+            "tolerance",
+        }

pandas/tests/frame/methods/test_rename.py

@@ -1,4 +1,5 @@
 from collections import ChainMap
+import inspect
 
 import numpy as np
 import pytest
@@ -8,6 +9,21 @@
 
 
 class TestRename:
+    def test_rename_signature(self):
+        sig = inspect.signature(DataFrame.rename)
+        parameters = set(sig.parameters)
+        assert parameters == {
+            "self",
+            "mapper",
+            "index",
+            "columns",
+            "axis",
+            "inplace",
+            "copy",
+            "level",
+            "errors",
+        }
+
     @pytest.mark.parametrize("klass", [Series, DataFrame])
     def test_rename_mi(self, klass):
         obj = klass(

pandas/tests/frame/methods/test_set_index.py

@@ -4,6 +4,7 @@
 import pytest
 
 from pandas import (
+    Categorical,
     DataFrame,
     DatetimeIndex,
     Index,
@@ -372,6 +373,21 @@ def test_construction_with_categorical_index(self):
         idf = idf.reset_index().set_index("B")
         tm.assert_index_equal(idf.index, ci)
 
+    def test_set_index_preserve_categorical_dtype(self):
+        # GH#13743, GH#13854
+        df = DataFrame(
+            {
+                "A": [1, 2, 1, 1, 2],
+                "B": [10, 16, 22, 28, 34],
+                "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),
+                "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),
+            }
+        )
+        for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]:
+            result = df.set_index(cols).reset_index()
+            result = result.reindex(columns=df.columns)
+            tm.assert_frame_equal(result, df)
+
     def test_set_index_datetime(self):
         # GH#3950
         df = DataFrame(

pandas/tests/frame/test_alter_axes.py

@@ -1,5 +1,4 @@
 from datetime import datetime
-import inspect
 
 import numpy as np
 import pytest
@@ -137,34 +136,6 @@ def test_dti_set_index_reindex(self):
 
     # Renaming
 
-    def test_reindex_api_equivalence(self):
-        # equivalence of the labels/axis and index/columns API's
-        df = DataFrame(
-            [[1, 2, 3], [3, 4, 5], [5, 6, 7]],
-            index=["a", "b", "c"],
-            columns=["d", "e", "f"],
-        )
-
-        res1 = df.reindex(["b", "a"])
-        res2 = df.reindex(index=["b", "a"])
-        res3 = df.reindex(labels=["b", "a"])
-        res4 = df.reindex(labels=["b", "a"], axis=0)
-        res5 = df.reindex(["b", "a"], axis=0)
-        for res in [res2, res3, res4, res5]:
-            tm.assert_frame_equal(res1, res)
-
-        res1 = df.reindex(columns=["e", "d"])
-        res2 = df.reindex(["e", "d"], axis=1)
-        res3 = df.reindex(labels=["e", "d"], axis=1)
-        for res in [res2, res3]:
-            tm.assert_frame_equal(res1, res)
-
-        res1 = df.reindex(index=["b", "a"], columns=["e", "d"])
-        res2 = df.reindex(columns=["e", "d"], index=["b", "a"])
-        res3 = df.reindex(labels=["b", "a"], axis=0).reindex(labels=["e", "d"], axis=1)
-        for res in [res2, res3]:
-            tm.assert_frame_equal(res1, res)
-
     def test_assign_columns(self, float_frame):
         float_frame["hi"] = "there"
 
@@ -173,38 +144,6 @@ def test_assign_columns(self, float_frame):
         tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False)
         tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False)
 
-    def test_rename_signature(self):
-        sig = inspect.signature(DataFrame.rename)
-        parameters = set(sig.parameters)
-        assert parameters == {
-            "self",
-            "mapper",
-            "index",
-            "columns",
-            "axis",
-            "inplace",
-            "copy",
-            "level",
-            "errors",
-        }
-
-    def test_reindex_signature(self):
-        sig = inspect.signature(DataFrame.reindex)
-        parameters = set(sig.parameters)
-        assert parameters == {
-            "self",
-            "labels",
-            "index",
-            "columns",
-            "axis",
-            "limit",
-            "copy",
-            "level",
-            "method",
-            "fill_value",
-            "tolerance",
-        }
-
 
 class TestIntervalIndex:
     def test_setitem(self):
@@ -256,21 +195,6 @@ def test_set_reset_index(self):
 
 
 class TestCategoricalIndex:
-    def test_set_index_preserve_categorical_dtype(self):
-        # GH13743, GH13854
-        df = DataFrame(
-            {
-                "A": [1, 2, 1, 1, 2],
-                "B": [10, 16, 22, 28, 34],
-                "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),
-                "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),
-            }
-        )
-        for cols in ["C1", "C2", ["A", "C1"], ["A", "C2"], ["C1", "C2"]]:
-            result = df.set_index(cols).reset_index()
-            result = result.reindex(columns=df.columns)
-            tm.assert_frame_equal(result, df)
-
     @pytest.mark.parametrize(
         "codes", ([[0, 0, 1, 1], [0, 1, 0, 1]], [[0, 0, -1, 1], [0, 1, 0, 1]])
     )

pandas/tests/series/test_analytics.py

@@ -5,13 +5,6 @@
 
 
 class TestSeriesAnalytics:
-    def test_ptp(self):
-        # GH21614
-        N = 1000
-        arr = np.random.randn(N)
-        ser = Series(arr)
-        assert np.ptp(ser) == np.ptp(arr)
-
     def test_is_monotonic(self):
 
         s = Series(np.random.randint(0, 10, size=1000))

pandas/tests/series/test_npfuncs.py

@@ -0,0 +1,16 @@
+"""
+Tests for np.foo applied to Series, not necessarily ufuncs.
+"""
+
+import numpy as np
+
+from pandas import Series
+
+
+class TestPtp:
+    def test_ptp(self):
+        # GH#21614
+        N = 1000
+        arr = np.random.randn(N)
+        ser = Series(arr)
+        assert np.ptp(ser) == np.ptp(arr)

pandas/tests/series/test_timeseries.py

@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 
 import pandas as pd
 from pandas import DataFrame, Series, date_range, timedelta_range
@@ -70,37 +69,3 @@ def test_view_tz(self):
             ]
         )
         tm.assert_series_equal(result, expected)
-
-    @pytest.mark.parametrize("tz", [None, "US/Central"])
-    def test_asarray_object_dt64(self, tz):
-        ser = Series(pd.date_range("2000", periods=2, tz=tz))
-
-        with tm.assert_produces_warning(None):
-            # Future behavior (for tzaware case) with no warning
-            result = np.asarray(ser, dtype=object)
-
-        expected = np.array(
-            [pd.Timestamp("2000-01-01", tz=tz), pd.Timestamp("2000-01-02", tz=tz)]
-        )
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_asarray_tz_naive(self):
-        # This shouldn't produce a warning.
-        ser = Series(pd.date_range("2000", periods=2))
-        expected = np.array(["2000-01-01", "2000-01-02"], dtype="M8[ns]")
-        result = np.asarray(ser)
-
-        tm.assert_numpy_array_equal(result, expected)
-
-    def test_asarray_tz_aware(self):
-        tz = "US/Central"
-        ser = Series(pd.date_range("2000", periods=2, tz=tz))
-        expected = np.array(["2000-01-01T06", "2000-01-02T06"], dtype="M8[ns]")
-        result = np.asarray(ser, dtype="datetime64[ns]")
-
-        tm.assert_numpy_array_equal(result, expected)
-
-        # Old behavior with no warning
-        result = np.asarray(ser, dtype="M8[ns]")
-
-        tm.assert_numpy_array_equal(result, expected)