TST/CLN: Remove getSeriesData/makeObjectSeries/makeDatetimeIndex

pandas/_testing/__init__.py

@@ -2,7 +2,6 @@
 
 import collections
 from collections import Counter
-from datetime import datetime
 from decimal import Decimal
 import operator
 import os
@@ -36,12 +35,10 @@
     ArrowDtype,
     Categorical,
     DataFrame,
-    DatetimeIndex,
     Index,
     MultiIndex,
     RangeIndex,
     Series,
-    bdate_range,
     date_range,
     period_range,
     timedelta_range,
@@ -348,34 +345,12 @@ def getCols(k) -> str:
     return string.ascii_uppercase[:k]
 
 
-def makeDateIndex(
-    k: int = 10, freq: Frequency = "B", name=None, **kwargs
-) -> DatetimeIndex:
-    dt = datetime(2000, 1, 1)
-    dr = bdate_range(dt, periods=k, freq=freq, name=name)
-    return DatetimeIndex(dr, name=name, **kwargs)
-
-
-def makeObjectSeries(name=None) -> Series:
-    data = [f"foo_{i}" for i in range(_N)]
-    index = Index([f"bar_{i}" for i in range(_N)])
-    return Series(data, index=index, name=name, dtype=object)
-
-
-def getSeriesData() -> dict[str, Series]:
-    index = Index([f"foo_{i}" for i in range(_N)])
-    return {
-        c: Series(np.random.default_rng(i).standard_normal(_N), index=index)
-        for i, c in enumerate(getCols(_K))
-    }
-
-
 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=makeDateIndex(nper, freq=freq),
+        index=date_range("2000-01-01", periods=nper, freq=freq),
         name=name,
     )
 
@@ -390,11 +365,6 @@ def makeTimeDataFrame(nper=None, freq: Frequency = "B") -> DataFrame:
     return DataFrame(data)
 
 
-def makeDataFrame() -> DataFrame:
-    data = getSeriesData()
-    return DataFrame(data)
-
-
 def makeCustomIndex(
     nentries,
     nlevels,
@@ -925,16 +895,12 @@ def shares_memory(left, right) -> bool:
     "get_finest_unit",
     "get_obj",
     "get_op_from_name",
-    "getSeriesData",
     "getTimeSeriesData",
     "iat",
     "iloc",
     "loc",
     "makeCustomDataframe",
     "makeCustomIndex",
-    "makeDataFrame",
-    "makeDateIndex",
-    "makeObjectSeries",
     "makeTimeDataFrame",
     "makeTimeSeries",
     "maybe_produces_warning",

pandas/conftest.py

@@ -68,6 +68,7 @@
     Series,
     Timedelta,
     Timestamp,
+    date_range,
     period_range,
     timedelta_range,
 )
@@ -608,15 +609,15 @@ def _create_mi_with_dt64tz_level():
     """
     # GH#8367 round trip with pickle
     return MultiIndex.from_product(
-        [[1, 2], ["a", "b"], pd.date_range("20130101", periods=3, tz="US/Eastern")],
+        [[1, 2], ["a", "b"], date_range("20130101", periods=3, tz="US/Eastern")],
         names=["one", "two", "three"],
     )
 
 
 indices_dict = {
     "string": Index([f"pandas_{i}" for i in range(100)]),
-    "datetime": tm.makeDateIndex(100),
-    "datetime-tz": tm.makeDateIndex(100, tz="US/Pacific"),
+    "datetime": date_range("2020-01-01", periods=100),
+    "datetime-tz": date_range("2020-01-01", periods=100, tz="US/Pacific"),
     "period": period_range("2020-01-01", periods=100, freq="D"),
     "timedelta": timedelta_range(start="1 day", periods=100, freq="D"),
     "range": RangeIndex(100),
@@ -631,7 +632,7 @@ def _create_mi_with_dt64tz_level():
     "float32": Index(np.arange(100), dtype="float32"),
     "float64": Index(np.arange(100), dtype="float64"),
     "bool-object": Index([True, False] * 5, dtype=object),
-    "bool-dtype": Index(np.random.default_rng(2).standard_normal(10) < 0),
+    "bool-dtype": Index([True, False] * 5, dtype=bool),
     "complex64": Index(
         np.arange(100, dtype="complex64") + 1.0j * np.arange(100, dtype="complex64")
     ),
@@ -751,9 +752,9 @@ def object_series() -> Series:
     """
     Fixture for Series of dtype object with Index of unique strings
     """
-    s = tm.makeObjectSeries()
-    s.name = "objects"
-    return s
+    data = [f"foo_{i}" for i in range(30)]
+    index = Index([f"bar_{i}" for i in range(30)], dtype=object)
+    return Series(data, index=index, name="objects", dtype=object)
 
 
 @pytest.fixture
@@ -839,27 +840,12 @@ def int_frame() -> DataFrame:
     Fixture for DataFrame of ints with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D']
-
-                A  B  C  D
-    vpBeWjM651  1  0  1  0
-    5JyxmrP1En -1  0  0  0
-    qEDaoD49U2 -1  1  0  0
-    m66TkTfsFe  0  0  0  0
-    EHPaNzEUFm -1  0 -1  0
-    fpRJCevQhi  2  0  0  0
-    OlQvnmfi3Q  0  0 -2  0
-    ...        .. .. .. ..
-    uB1FPlz4uP  0  0  0  1
-    EcSe6yNzCU  0  0 -1  0
-    L50VudaiI8 -1  1 -2  0
-    y3bpw4nwIp  0 -1  0  0
-    H0RdLLwrCT  1  1  0  0
-    rY82K0vMwm  0  0  0  0
-    1OPIUjnkjk  2  0  0  0
-
-    [30 rows x 4 columns]
     """
-    return DataFrame(tm.getSeriesData()).astype("int64")
+    return DataFrame(
+        np.ones((30, 4), dtype=np.int64),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD"), dtype=object),
+    )
 
 
 @pytest.fixture
@@ -868,27 +854,12 @@ def float_frame() -> DataFrame:
     Fixture for DataFrame of floats with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
-
-                       A         B         C         D
-    P7GACiRnxd -0.465578 -0.361863  0.886172 -0.053465
-    qZKh6afn8n -0.466693 -0.373773  0.266873  1.673901
-    tkp0r6Qble  0.148691 -0.059051  0.174817  1.598433
-    wP70WOCtv8  0.133045 -0.581994 -0.992240  0.261651
-    M2AeYQMnCz -1.207959 -0.185775  0.588206  0.563938
-    QEPzyGDYDo -0.381843 -0.758281  0.502575 -0.565053
-    r78Jwns6dn -0.653707  0.883127  0.682199  0.206159
-    ...              ...       ...       ...       ...
-    IHEGx9NO0T -0.277360  0.113021 -1.018314  0.196316
-    lPMj8K27FA -1.313667 -0.604776 -1.305618 -0.863999
-    qa66YMWQa5  1.110525  0.475310 -0.747865  0.032121
-    yOa0ATsmcE -0.431457  0.067094  0.096567 -0.264962
-    65znX3uRNG  1.528446  0.160416 -0.109635 -0.032987
-    eCOBvKqf3e  0.235281  1.622222  0.781255  0.392871
-    xSucinXxuV -1.263557  0.252799 -0.552247  0.400426
-
-    [30 rows x 4 columns]
-    """
-    return DataFrame(tm.getSeriesData())
+    """
+    return DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD"), dtype=object),
+    )
 
 
 @pytest.fixture

pandas/tests/apply/test_numba.py

@@ -60,9 +60,10 @@ def test_numba_vs_python_indexing():
     "reduction",
     [lambda x: x.mean(), lambda x: x.min(), lambda x: x.max(), lambda x: x.sum()],
 )
-def test_numba_vs_python_reductions(float_frame, reduction, apply_axis):
-    result = float_frame.apply(reduction, engine="numba", axis=apply_axis)
-    expected = float_frame.apply(reduction, engine="python", axis=apply_axis)
+def test_numba_vs_python_reductions(reduction, apply_axis):
+    df = DataFrame(np.ones((4, 4), dtype=np.float64))
+    result = df.apply(reduction, engine="numba", axis=apply_axis)
+    expected = df.apply(reduction, engine="python", axis=apply_axis)
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/arithmetic/test_datetime64.py

@@ -394,7 +394,7 @@ def test_dt64_compare_datetime_scalar(self, datetimelike, op, expected):
 class TestDatetimeIndexComparisons:
     # TODO: moved from tests.indexes.test_base; parametrize and de-duplicate
     def test_comparators(self, comparison_op):
-        index = tm.makeDateIndex(100)
+        index = date_range("2020-01-01", periods=10)
         element = index[len(index) // 2]
         element = Timestamp(element).to_datetime64()
 

pandas/tests/arithmetic/test_object.py

@@ -169,8 +169,7 @@ def test_objarr_add_invalid(self, op, box_with_array):
         # invalid ops
         box = box_with_array
 
-        obj_ser = tm.makeObjectSeries()
-        obj_ser.name = "objects"
+        obj_ser = Series(list("abc"), dtype=object, name="objects")
 
         obj_ser = tm.box_expected(obj_ser, box)
         msg = "|".join(

pandas/tests/dtypes/test_missing.py

@@ -78,8 +78,12 @@ def test_notna_notnull(notna_f):
 @pytest.mark.parametrize(
     "ser",
     [
-        tm.makeObjectSeries(),
-        tm.makeTimeSeries(),
+        Series(
+            [str(i) for i in range(5)],
+            index=Index([str(i) for i in range(5)], dtype=object),
+            dtype=object,
+        ),
+        Series(range(5), date_range("2020-01-01", periods=5)),
         Series(range(5), period_range("2020-01-01", periods=5)),
     ],
 )

pandas/tests/frame/conftest.py

@@ -3,6 +3,7 @@
 
 from pandas import (
     DataFrame,
+    Index,
     NaT,
     date_range,
 )
@@ -44,27 +45,12 @@ def float_string_frame():
     Fixture for DataFrame of floats and strings with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D', 'foo'].
-
-                       A         B         C         D  foo
-    w3orJvq07g -1.594062 -1.084273 -1.252457  0.356460  bar
-    PeukuVdmz2  0.109855 -0.955086 -0.809485  0.409747  bar
-    ahp2KvwiM8 -1.533729 -0.142519 -0.154666  1.302623  bar
-    3WSJ7BUCGd  2.484964  0.213829  0.034778 -2.327831  bar
-    khdAmufk0U -0.193480 -0.743518 -0.077987  0.153646  bar
-    LE2DZiFlrE -0.193566 -1.343194 -0.107321  0.959978  bar
-    HJXSJhVn7b  0.142590  1.257603 -0.659409 -0.223844  bar
-    ...              ...       ...       ...       ...  ...
-    9a1Vypttgw -1.316394  1.601354  0.173596  1.213196  bar
-    h5d1gVFbEy  0.609475  1.106738 -0.155271  0.294630  bar
-    mK9LsTQG92  1.303613  0.857040 -1.019153  0.369468  bar
-    oOLksd9gKH  0.558219 -0.134491 -0.289869 -0.951033  bar
-    9jgoOjKyHg  0.058270 -0.496110 -0.413212 -0.852659  bar
-    jZLDHclHAO  0.096298  1.267510  0.549206 -0.005235  bar
-    lR0nxDp1C2 -2.119350 -0.794384  0.544118  0.145849  bar
-
-    [30 rows x 5 columns]
     """
-    df = DataFrame(tm.getSeriesData())
+    df = DataFrame(
+        np.random.default_rng(2).standard_normal((30, 4)),
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+        columns=Index(list("ABCD"), dtype=object),
+    )
     df["foo"] = "bar"
     return df
 
@@ -75,31 +61,18 @@ def mixed_float_frame():
     Fixture for DataFrame of different float types with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
-
-                       A         B         C         D
-    GI7bbDaEZe -0.237908 -0.246225 -0.468506  0.752993
-    KGp9mFepzA -1.140809 -0.644046 -1.225586  0.801588
-    VeVYLAb1l2 -1.154013 -1.677615  0.690430 -0.003731
-    kmPME4WKhO  0.979578  0.998274 -0.776367  0.897607
-    CPyopdXTiz  0.048119 -0.257174  0.836426  0.111266
-    0kJZQndAj0  0.274357 -0.281135 -0.344238  0.834541
-    tqdwQsaHG8 -0.979716 -0.519897  0.582031  0.144710
-    ...              ...       ...       ...       ...
-    7FhZTWILQj -2.906357  1.261039 -0.780273 -0.537237
-    4pUDPM4eGq -2.042512 -0.464382 -0.382080  1.132612
-    B8dUgUzwTi -1.506637 -0.364435  1.087891  0.297653
-    hErlVYjVv9  1.477453 -0.495515 -0.713867  1.438427
-    1BKN3o7YLs  0.127535 -0.349812 -0.881836  0.489827
-    9S4Ekn7zga  1.445518 -2.095149  0.031982  0.373204
-    xN1dNn6OV6  1.425017 -0.983995 -0.363281 -0.224502
-
-    [30 rows x 4 columns]
     """
-    df = DataFrame(tm.getSeriesData())
-    df.A = df.A.astype("float32")
-    df.B = df.B.astype("float32")
-    df.C = df.C.astype("float16")
-    df.D = df.D.astype("float64")
+    df = DataFrame(
+        {
+            col: np.random.default_rng(2).random(30, dtype=dtype)
+            for col, dtype in zip(
+                list("ABCD"), ["float32", "float32", "float32", "float64"]
+            )
+        },
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+    )
+    # not supported by numpy random
+    df["C"] = df["C"].astype("float16")
     return df
 
 
@@ -109,32 +82,14 @@ def mixed_int_frame():
     Fixture for DataFrame of different int types with index of unique strings
 
     Columns are ['A', 'B', 'C', 'D'].
-
-                A  B    C    D
-    mUrCZ67juP  0  1    2    2
-    rw99ACYaKS  0  1    0    0
-    7QsEcpaaVU  0  1    1    1
-    xkrimI2pcE  0  1    0    0
-    dz01SuzoS8  0  1  255  255
-    ccQkqOHX75 -1  1    0    0
-    DN0iXaoDLd  0  1    0    0
-    ...        .. ..  ...  ...
-    Dfb141wAaQ  1  1  254  254
-    IPD8eQOVu5  0  1    0    0
-    CcaKulsCmv  0  1    0    0
-    rIBa8gu7E5  0  1    0    0
-    RP6peZmh5o  0  1    1    1
-    NMb9pipQWQ  0  1    0    0
-    PqgbJEzjib  0  1    3    3
-
-    [30 rows x 4 columns]
     """
-    df = DataFrame({k: v.astype(int) for k, v in tm.getSeriesData().items()})
-    df.A = df.A.astype("int32")
-    df.B = np.ones(len(df.B), dtype="uint64")
-    df.C = df.C.astype("uint8")
-    df.D = df.C.astype("int64")
-    return df
+    return DataFrame(
+        {
+            col: np.ones(30, dtype=dtype)
+            for col, dtype in zip(list("ABCD"), ["int32", "uint64", "uint8", "int64"])
+        },
+        index=Index([f"foo_{i}" for i in range(30)], dtype=object),
+    )
 
 
 @pytest.fixture

pandas/tests/frame/methods/test_info.py

@@ -532,11 +532,11 @@ def test_info_compute_numba():
 
     with option_context("compute.use_numba", True):
         buf = StringIO()
-        df.info()
+        df.info(buf=buf)
         result = buf.getvalue()
 
     buf = StringIO()
-    df.info()
+    df.info(buf=buf)
     expected = buf.getvalue()
     assert result == expected