CLN/TST: Remove makeTimeSeries (#56293)

* CLN/TST: Remove makeTimeSeries

* adjust test

Author: mroeschke

pandas/_testing/__init__.py

@@ -101,14 +101,11 @@
 if TYPE_CHECKING:
     from pandas._typing import (
         Dtype,
-        Frequency,
         NpDtype,
     )
 
     from pandas.core.arrays import ArrowExtensionArray
 
-_N = 30
-
 UNSIGNED_INT_NUMPY_DTYPES: list[NpDtype] = ["uint8", "uint16", "uint32", "uint64"]
 UNSIGNED_INT_EA_DTYPES: list[Dtype] = ["UInt8", "UInt16", "UInt32", "UInt64"]
 SIGNED_INT_NUMPY_DTYPES: list[NpDtype] = [int, "int8", "int16", "int32", "int64"]
@@ -339,16 +336,6 @@ def to_array(obj):
 # Others
 
 
-def makeTimeSeries(nper=None, freq: Frequency = "B", name=None) -> Series:
-    if nper is None:
-        nper = _N
-    return Series(
-        np.random.default_rng(2).standard_normal(nper),
-        index=date_range("2000-01-01", periods=nper, freq=freq),
-        name=name,
-    )
-
-
 def makeCustomIndex(
     nentries,
     nlevels,
@@ -883,7 +870,6 @@ def shares_memory(left, right) -> bool:
     "loc",
     "makeCustomDataframe",
     "makeCustomIndex",
-    "makeTimeSeries",
     "maybe_produces_warning",
     "NARROW_NP_DTYPES",
     "NP_NAT_OBJECTS",

pandas/conftest.py

@@ -766,9 +766,11 @@ def datetime_series() -> Series:
     """
     Fixture for Series of floats with DatetimeIndex
     """
-    s = tm.makeTimeSeries()
-    s.name = "ts"
-    return s
+    return Series(
+        np.random.default_rng(2).standard_normal(30),
+        index=date_range("2000-01-01", periods=30, freq="B"),
+        name="ts",
+    )
 
 
 def _create_series(index):

pandas/tests/apply/test_series_apply.py

@@ -8,6 +8,7 @@
     MultiIndex,
     Series,
     concat,
+    date_range,
     timedelta_range,
 )
 import pandas._testing as tm
@@ -134,7 +135,7 @@ def foo2(x, b=2, c=0):
 
 def test_series_apply_map_box_timestamps(by_row):
     # GH#2689, GH#2627
-    ser = Series(pd.date_range("1/1/2000", periods=10))
+    ser = Series(date_range("1/1/2000", periods=10))
 
     def func(x):
         return (x.hour, x.day, x.month)
@@ -194,13 +195,11 @@ def test_apply_box_period():
 
 
 def test_apply_datetimetz(by_row):
-    values = pd.date_range("2011-01-01", "2011-01-02", freq="h").tz_localize(
-        "Asia/Tokyo"
-    )
+    values = date_range("2011-01-01", "2011-01-02", freq="h").tz_localize("Asia/Tokyo")
     s = Series(values, name="XX")
 
     result = s.apply(lambda x: x + pd.offsets.Day(), by_row=by_row)
-    exp_values = pd.date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
+    exp_values = date_range("2011-01-02", "2011-01-03", freq="h").tz_localize(
         "Asia/Tokyo"
     )
     exp = Series(exp_values, name="XX")
@@ -267,7 +266,7 @@ def test_apply_categorical_with_nan_values(series, by_row):
 
 def test_apply_empty_integer_series_with_datetime_index(by_row):
     # GH 21245
-    s = Series([], index=pd.date_range(start="2018-01-01", periods=0), dtype=int)
+    s = Series([], index=date_range(start="2018-01-01", periods=0), dtype=int)
     result = s.apply(lambda x: x, by_row=by_row)
     tm.assert_series_equal(result, s)
 
@@ -510,8 +509,12 @@ def test_series_apply_no_suffix_index(by_row):
             DataFrame(np.repeat([[1, 2]], 2, axis=0), dtype="int64"),
         ),
         (
-            tm.makeTimeSeries(nper=30),
-            DataFrame(np.repeat([[1, 2]], 30, axis=0), dtype="int64"),
+            Series(
+                np.arange(10, dtype=np.float64),
+                index=date_range("2020-01-01", periods=10),
+                name="ts",
+            ),
+            DataFrame(np.repeat([[1, 2]], 10, axis=0), dtype="int64"),
         ),
     ],
 )
@@ -528,12 +531,15 @@ def test_apply_series_on_date_time_index_aware_series(dti, exp, aware):
 
 
 @pytest.mark.parametrize(
-    "by_row, expected", [("compat", Series(np.ones(30), dtype="int64")), (False, 1)]
+    "by_row, expected", [("compat", Series(np.ones(10), dtype="int64")), (False, 1)]
 )
 def test_apply_scalar_on_date_time_index_aware_series(by_row, expected):
     # GH 25959
     # Calling apply on a localized time series should not cause an error
-    series = tm.makeTimeSeries(nper=30).tz_localize("UTC")
+    series = Series(
+        np.arange(10, dtype=np.float64),
+        index=date_range("2020-01-01", periods=10, tz="UTC"),
+    )
     result = Series(series.index).apply(lambda x: 1, by_row=by_row)
     tm.assert_equal(result, expected)
 

pandas/tests/arithmetic/test_numeric.py

@@ -19,6 +19,7 @@
     Timedelta,
     TimedeltaIndex,
     array,
+    date_range,
 )
 import pandas._testing as tm
 from pandas.core import ops
@@ -733,7 +734,7 @@ def test_mul_datelike_raises(self, numeric_idx):
         idx = numeric_idx
         msg = "cannot perform __rmul__ with this index type"
         with pytest.raises(TypeError, match=msg):
-            idx * pd.date_range("20130101", periods=5)
+            idx * date_range("20130101", periods=5)
 
     def test_mul_size_mismatch_raises(self, numeric_idx):
         idx = numeric_idx
@@ -820,7 +821,11 @@ def test_ops_np_scalar(self, other):
     # TODO: This came from series.test.test_operators, needs cleanup
     def test_operators_frame(self):
         # rpow does not work with DataFrame
-        ts = tm.makeTimeSeries()
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         ts.name = "ts"
 
         df = pd.DataFrame({"A": ts})
@@ -926,8 +931,11 @@ def test_series_frame_radd_bug(self, fixed_now_ts):
         expected = pd.DataFrame({"vals": vals.map(lambda x: "foo_" + x)})
         tm.assert_frame_equal(result, expected)
 
-        ts = tm.makeTimeSeries()
-        ts.name = "ts"
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
 
         # really raise this time
         fix_now = fixed_now_ts.to_pydatetime()
@@ -955,8 +963,8 @@ def test_datetime64_with_index(self):
         # GH#4629
         # arithmetic datetime64 ops with an index
         ser = Series(
-            pd.date_range("20130101", periods=5),
-            index=pd.date_range("20130101", periods=5),
+            date_range("20130101", periods=5),
+            index=date_range("20130101", periods=5),
         )
         expected = ser - ser.index.to_series()
         result = ser - ser.index
@@ -969,7 +977,7 @@ def test_datetime64_with_index(self):
 
         df = pd.DataFrame(
             np.random.default_rng(2).standard_normal((5, 2)),
-            index=pd.date_range("20130101", periods=5),
+            index=date_range("20130101", periods=5),
         )
         df["date"] = pd.Timestamp("20130102")
         df["expected"] = df["date"] - df.index.to_series()
@@ -1031,7 +1039,11 @@ def test_frame_operators_empty_like(self, dtype):
     )
     def test_series_operators_arithmetic(self, all_arithmetic_functions, func):
         op = all_arithmetic_functions
-        series = tm.makeTimeSeries().rename("ts")
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         other = func(series)
         compare_op(series, other, op)
 
@@ -1040,7 +1052,11 @@ def test_series_operators_arithmetic(self, all_arithmetic_functions, func):
     )
     def test_series_operators_compare(self, comparison_op, func):
         op = comparison_op
-        series = tm.makeTimeSeries().rename("ts")
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         other = func(series)
         compare_op(series, other, op)
 
@@ -1050,7 +1066,11 @@ def test_series_operators_compare(self, comparison_op, func):
         ids=["multiply", "slice", "constant"],
     )
     def test_divmod(self, func):
-        series = tm.makeTimeSeries().rename("ts")
+        series = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         other = func(series)
         results = divmod(series, other)
         if isinstance(other, abc.Iterable) and len(series) != len(other):
@@ -1081,7 +1101,11 @@ def test_series_divmod_zero(self):
         #  -1/0 == -np.inf
         #  1/-0.0 == -np.inf
         #  -1/-0.0 == np.inf
-        tser = tm.makeTimeSeries().rename("ts")
+        tser = Series(
+            np.arange(1, 11, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         other = tser * 0
 
         result = divmod(tser, other)

pandas/tests/frame/methods/test_reindex.py

@@ -609,7 +609,9 @@ def test_reindex_sparse(self):
         tm.assert_frame_equal(result, expected)
 
     def test_reindex(self, float_frame, using_copy_on_write):
-        datetime_series = tm.makeTimeSeries(nper=30)
+        datetime_series = Series(
+            np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30)
+        )
 
         newFrame = float_frame.reindex(datetime_series.index)
 

pandas/tests/frame/test_constructors.py

@@ -500,9 +500,11 @@ def test_constructor_ordereddict(self):
         assert expected == list(df.columns)
 
     def test_constructor_dict(self):
-        datetime_series = tm.makeTimeSeries(nper=30)
+        datetime_series = Series(
+            np.arange(30, dtype=np.float64), index=date_range("2020-01-01", periods=30)
+        )
         # test expects index shifted by 5
-        datetime_series_short = tm.makeTimeSeries(nper=30)[5:]
+        datetime_series_short = datetime_series[5:]
 
         frame = DataFrame({"col1": datetime_series, "col2": datetime_series_short})
 
@@ -626,8 +628,10 @@ def test_constructor_dict_nan_tuple_key(self, value):
         tm.assert_frame_equal(result, expected)
 
     def test_constructor_dict_order_insertion(self):
-        datetime_series = tm.makeTimeSeries(nper=30)
-        datetime_series_short = tm.makeTimeSeries(nper=25)
+        datetime_series = Series(
+            np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+        )
+        datetime_series_short = datetime_series[:5]
 
         # GH19018
         # initialization ordering: by insertion order if python>= 3.6

pandas/tests/groupby/aggregate/test_other.py

@@ -296,7 +296,9 @@ def raiseException(df):
 
 
 def test_series_agg_multikey():
-    ts = tm.makeTimeSeries()
+    ts = Series(
+        np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+    )
     grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
 
     result = grouped.agg("sum")

pandas/tests/groupby/conftest.py

@@ -4,9 +4,9 @@
 from pandas import (
     DataFrame,
     Index,
+    Series,
     date_range,
 )
-import pandas._testing as tm
 from pandas.core.groupby.base import (
     reduction_kernels,
     transformation_kernels,
@@ -47,7 +47,10 @@ def df():
 
 @pytest.fixture
 def ts():
-    return tm.makeTimeSeries()
+    return Series(
+        np.random.default_rng(2).standard_normal(30),
+        index=date_range("2000-01-01", periods=30, freq="B"),
+    )
 
 
 @pytest.fixture

pandas/tests/groupby/methods/test_describe.py

@@ -6,7 +6,9 @@
     DataFrame,
     Index,
     MultiIndex,
+    Series,
     Timestamp,
+    date_range,
 )
 import pandas._testing as tm
 
@@ -17,7 +19,9 @@ def test_apply_describe_bug(multiindex_dataframe_random_data):
 
 
 def test_series_describe_multikey():
-    ts = tm.makeTimeSeries()
+    ts = Series(
+        np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+    )
     grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
     result = grouped.describe()
     tm.assert_series_equal(result["mean"], grouped.mean(), check_names=False)
@@ -26,7 +30,9 @@ def test_series_describe_multikey():
 
 
 def test_series_describe_single():
-    ts = tm.makeTimeSeries()
+    ts = Series(
+        np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+    )
     grouped = ts.groupby(lambda x: x.month)
     result = grouped.apply(lambda x: x.describe())
     expected = grouped.describe().stack(future_stack=True)

pandas/tests/io/formats/test_to_string.py

@@ -1075,7 +1075,10 @@ def test_to_string_timedelta64(self):
         assert result == "0   1 days\n1   2 days\n2   3 days"
 
     def test_to_string(self):
-        ts = tm.makeTimeSeries()
+        ts = Series(
+            np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10, freq="B"),
+        )
         buf = StringIO()
 
         s = ts.to_string()

pandas/tests/io/json/test_pandas.py

@@ -108,8 +108,11 @@ def categorical_frame(self):
     def datetime_series(self):
         # Same as usual datetime_series, but with index freq set to None,
         #  since that doesn't round-trip, see GH#33711
-        ser = tm.makeTimeSeries()
-        ser.name = "ts"
+        ser = Series(
+            1.1 * np.arange(10, dtype=np.float64),
+            index=date_range("2020-01-01", periods=10),
+            name="ts",
+        )
         ser.index = ser.index._with_freq(None)
         return ser
 

pandas/tests/io/pytables/test_append.py

@@ -105,7 +105,9 @@ def test_append_series(setup_path):
     with ensure_clean_store(setup_path) as store:
         # basic
         ss = Series(range(20), dtype=np.float64, index=[f"i_{i}" for i in range(20)])
-        ts = tm.makeTimeSeries()
+        ts = Series(
+            np.arange(10, dtype=np.float64), index=date_range("2020-01-01", periods=10)
+        )
         ns = Series(np.arange(100))
 
         store.append("ss", ss)