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authoredNov 16, 2020
making namespace usage more consistent (#37852)
* making namespace usage more consistent * Adding more index classes to test_numeric
1 parent 613f098 commit c77fc35

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8 files changed

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-110
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+125
-110
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‎pandas/tests/arithmetic/test_numeric.py

Lines changed: 54 additions & 48 deletions
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,17 @@
1111
import pytest
1212

1313
import pandas as pd
14-
from pandas import Index, Int64Index, Series, Timedelta, TimedeltaIndex, array
14+
from pandas import (
15+
Float64Index,
16+
Index,
17+
Int64Index,
18+
RangeIndex,
19+
Series,
20+
Timedelta,
21+
TimedeltaIndex,
22+
UInt64Index,
23+
array,
24+
)
1525
import pandas._testing as tm
1626
from pandas.core import ops
1727

@@ -43,7 +53,7 @@ def adjust_negative_zero(zero, expected):
4353
# List comprehension has incompatible type List[PandasObject]; expected List[RangeIndex]
4454
# See GH#29725
4555
ser_or_index: List[Any] = [Series, Index]
46-
lefts: List[Any] = [pd.RangeIndex(10, 40, 10)]
56+
lefts: List[Any] = [RangeIndex(10, 40, 10)]
4757
lefts.extend(
4858
[
4959
cls([10, 20, 30], dtype=dtype)
@@ -364,7 +374,7 @@ def test_divmod_zero(self, zero, numeric_idx):
364374
@pytest.mark.parametrize("op", [operator.truediv, operator.floordiv])
365375
def test_div_negative_zero(self, zero, numeric_idx, op):
366376
# Check that -1 / -0.0 returns np.inf, not -np.inf
367-
if isinstance(numeric_idx, pd.UInt64Index):
377+
if isinstance(numeric_idx, UInt64Index):
368378
return
369379
idx = numeric_idx - 3
370380

@@ -634,15 +644,15 @@ def test_mul_int_array(self, numeric_idx):
634644
result = idx * np.array(5, dtype="int64")
635645
tm.assert_index_equal(result, idx * 5)
636646

637-
arr_dtype = "uint64" if isinstance(idx, pd.UInt64Index) else "int64"
647+
arr_dtype = "uint64" if isinstance(idx, UInt64Index) else "int64"
638648
result = idx * np.arange(5, dtype=arr_dtype)
639649
tm.assert_index_equal(result, didx)
640650

641651
def test_mul_int_series(self, numeric_idx):
642652
idx = numeric_idx
643653
didx = idx * idx
644654

645-
arr_dtype = "uint64" if isinstance(idx, pd.UInt64Index) else "int64"
655+
arr_dtype = "uint64" if isinstance(idx, UInt64Index) else "int64"
646656
result = idx * Series(np.arange(5, dtype=arr_dtype))
647657
tm.assert_series_equal(result, Series(didx))
648658

@@ -657,7 +667,7 @@ def test_mul_float_series(self, numeric_idx):
657667
def test_mul_index(self, numeric_idx):
658668
# in general not true for RangeIndex
659669
idx = numeric_idx
660-
if not isinstance(idx, pd.RangeIndex):
670+
if not isinstance(idx, RangeIndex):
661671
result = idx * idx
662672
tm.assert_index_equal(result, idx ** 2)
663673

@@ -680,7 +690,7 @@ def test_pow_float(self, op, numeric_idx, box_with_array):
680690
# test power calculations both ways, GH#14973
681691
box = box_with_array
682692
idx = numeric_idx
683-
expected = pd.Float64Index(op(idx.values, 2.0))
693+
expected = Float64Index(op(idx.values, 2.0))
684694

685695
idx = tm.box_expected(idx, box)
686696
expected = tm.box_expected(expected, box)
@@ -1040,74 +1050,70 @@ def test_series_divmod_zero(self):
10401050
class TestUFuncCompat:
10411051
@pytest.mark.parametrize(
10421052
"holder",
1043-
[pd.Int64Index, pd.UInt64Index, pd.Float64Index, pd.RangeIndex, Series],
1053+
[Int64Index, UInt64Index, Float64Index, RangeIndex, Series],
10441054
)
10451055
def test_ufunc_compat(self, holder):
10461056
box = Series if holder is Series else Index
10471057

1048-
if holder is pd.RangeIndex:
1049-
idx = pd.RangeIndex(0, 5)
1058+
if holder is RangeIndex:
1059+
idx = RangeIndex(0, 5)
10501060
else:
10511061
idx = holder(np.arange(5, dtype="int64"))
10521062
result = np.sin(idx)
10531063
expected = box(np.sin(np.arange(5, dtype="int64")))
10541064
tm.assert_equal(result, expected)
10551065

1056-
@pytest.mark.parametrize(
1057-
"holder", [pd.Int64Index, pd.UInt64Index, pd.Float64Index, Series]
1058-
)
1066+
@pytest.mark.parametrize("holder", [Int64Index, UInt64Index, Float64Index, Series])
10591067
def test_ufunc_coercions(self, holder):
10601068
idx = holder([1, 2, 3, 4, 5], name="x")
10611069
box = Series if holder is Series else Index
10621070

10631071
result = np.sqrt(idx)
10641072
assert result.dtype == "f8" and isinstance(result, box)
1065-
exp = pd.Float64Index(np.sqrt(np.array([1, 2, 3, 4, 5])), name="x")
1073+
exp = Float64Index(np.sqrt(np.array([1, 2, 3, 4, 5])), name="x")
10661074
exp = tm.box_expected(exp, box)
10671075
tm.assert_equal(result, exp)
10681076

10691077
result = np.divide(idx, 2.0)
10701078
assert result.dtype == "f8" and isinstance(result, box)
1071-
exp = pd.Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
1079+
exp = Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
10721080
exp = tm.box_expected(exp, box)
10731081
tm.assert_equal(result, exp)
10741082

10751083
# _evaluate_numeric_binop
10761084
result = idx + 2.0
10771085
assert result.dtype == "f8" and isinstance(result, box)
1078-
exp = pd.Float64Index([3.0, 4.0, 5.0, 6.0, 7.0], name="x")
1086+
exp = Float64Index([3.0, 4.0, 5.0, 6.0, 7.0], name="x")
10791087
exp = tm.box_expected(exp, box)
10801088
tm.assert_equal(result, exp)
10811089

10821090
result = idx - 2.0
10831091
assert result.dtype == "f8" and isinstance(result, box)
1084-
exp = pd.Float64Index([-1.0, 0.0, 1.0, 2.0, 3.0], name="x")
1092+
exp = Float64Index([-1.0, 0.0, 1.0, 2.0, 3.0], name="x")
10851093
exp = tm.box_expected(exp, box)
10861094
tm.assert_equal(result, exp)
10871095

10881096
result = idx * 1.0
10891097
assert result.dtype == "f8" and isinstance(result, box)
1090-
exp = pd.Float64Index([1.0, 2.0, 3.0, 4.0, 5.0], name="x")
1098+
exp = Float64Index([1.0, 2.0, 3.0, 4.0, 5.0], name="x")
10911099
exp = tm.box_expected(exp, box)
10921100
tm.assert_equal(result, exp)
10931101

10941102
result = idx / 2.0
10951103
assert result.dtype == "f8" and isinstance(result, box)
1096-
exp = pd.Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
1104+
exp = Float64Index([0.5, 1.0, 1.5, 2.0, 2.5], name="x")
10971105
exp = tm.box_expected(exp, box)
10981106
tm.assert_equal(result, exp)
10991107

1100-
@pytest.mark.parametrize(
1101-
"holder", [pd.Int64Index, pd.UInt64Index, pd.Float64Index, Series]
1102-
)
1108+
@pytest.mark.parametrize("holder", [Int64Index, UInt64Index, Float64Index, Series])
11031109
def test_ufunc_multiple_return_values(self, holder):
11041110
obj = holder([1, 2, 3], name="x")
11051111
box = Series if holder is Series else Index
11061112

11071113
result = np.modf(obj)
11081114
assert isinstance(result, tuple)
1109-
exp1 = pd.Float64Index([0.0, 0.0, 0.0], name="x")
1110-
exp2 = pd.Float64Index([1.0, 2.0, 3.0], name="x")
1115+
exp1 = Float64Index([0.0, 0.0, 0.0], name="x")
1116+
exp2 = Float64Index([1.0, 2.0, 3.0], name="x")
11111117
tm.assert_equal(result[0], tm.box_expected(exp1, box))
11121118
tm.assert_equal(result[1], tm.box_expected(exp2, box))
11131119

@@ -1173,12 +1179,12 @@ def check_binop(self, ops, scalars, idxs):
11731179
for op in ops:
11741180
for a, b in combinations(idxs, 2):
11751181
result = op(a, b)
1176-
expected = op(pd.Int64Index(a), pd.Int64Index(b))
1182+
expected = op(Int64Index(a), Int64Index(b))
11771183
tm.assert_index_equal(result, expected)
11781184
for idx in idxs:
11791185
for scalar in scalars:
11801186
result = op(idx, scalar)
1181-
expected = op(pd.Int64Index(idx), scalar)
1187+
expected = op(Int64Index(idx), scalar)
11821188
tm.assert_index_equal(result, expected)
11831189

11841190
def test_binops(self):
@@ -1191,10 +1197,10 @@ def test_binops(self):
11911197
]
11921198
scalars = [-1, 1, 2]
11931199
idxs = [
1194-
pd.RangeIndex(0, 10, 1),
1195-
pd.RangeIndex(0, 20, 2),
1196-
pd.RangeIndex(-10, 10, 2),
1197-
pd.RangeIndex(5, -5, -1),
1200+
RangeIndex(0, 10, 1),
1201+
RangeIndex(0, 20, 2),
1202+
RangeIndex(-10, 10, 2),
1203+
RangeIndex(5, -5, -1),
11981204
]
11991205
self.check_binop(ops, scalars, idxs)
12001206

@@ -1203,7 +1209,7 @@ def test_binops_pow(self):
12031209
# https://github.com/numpy/numpy/pull/8127
12041210
ops = [pow]
12051211
scalars = [1, 2]
1206-
idxs = [pd.RangeIndex(0, 10, 1), pd.RangeIndex(0, 20, 2)]
1212+
idxs = [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]
12071213
self.check_binop(ops, scalars, idxs)
12081214

12091215
# TODO: mod, divmod?
@@ -1221,7 +1227,7 @@ def test_binops_pow(self):
12211227
def test_arithmetic_with_frame_or_series(self, op):
12221228
# check that we return NotImplemented when operating with Series
12231229
# or DataFrame
1224-
index = pd.RangeIndex(5)
1230+
index = RangeIndex(5)
12251231
other = Series(np.random.randn(5))
12261232

12271233
expected = op(Series(index), other)
@@ -1237,26 +1243,26 @@ def test_numeric_compat2(self):
12371243
# validate that we are handling the RangeIndex overrides to numeric ops
12381244
# and returning RangeIndex where possible
12391245

1240-
idx = pd.RangeIndex(0, 10, 2)
1246+
idx = RangeIndex(0, 10, 2)
12411247

12421248
result = idx * 2
1243-
expected = pd.RangeIndex(0, 20, 4)
1249+
expected = RangeIndex(0, 20, 4)
12441250
tm.assert_index_equal(result, expected, exact=True)
12451251

12461252
result = idx + 2
1247-
expected = pd.RangeIndex(2, 12, 2)
1253+
expected = RangeIndex(2, 12, 2)
12481254
tm.assert_index_equal(result, expected, exact=True)
12491255

12501256
result = idx - 2
1251-
expected = pd.RangeIndex(-2, 8, 2)
1257+
expected = RangeIndex(-2, 8, 2)
12521258
tm.assert_index_equal(result, expected, exact=True)
12531259

12541260
result = idx / 2
1255-
expected = pd.RangeIndex(0, 5, 1).astype("float64")
1261+
expected = RangeIndex(0, 5, 1).astype("float64")
12561262
tm.assert_index_equal(result, expected, exact=True)
12571263

12581264
result = idx / 4
1259-
expected = pd.RangeIndex(0, 10, 2) / 4
1265+
expected = RangeIndex(0, 10, 2) / 4
12601266
tm.assert_index_equal(result, expected, exact=True)
12611267

12621268
result = idx // 1
@@ -1269,25 +1275,25 @@ def test_numeric_compat2(self):
12691275
tm.assert_index_equal(result, expected, exact=True)
12701276

12711277
# __pow__
1272-
idx = pd.RangeIndex(0, 1000, 2)
1278+
idx = RangeIndex(0, 1000, 2)
12731279
result = idx ** 2
12741280
expected = idx._int64index ** 2
12751281
tm.assert_index_equal(Index(result.values), expected, exact=True)
12761282

12771283
# __floordiv__
12781284
cases_exact = [
1279-
(pd.RangeIndex(0, 1000, 2), 2, pd.RangeIndex(0, 500, 1)),
1280-
(pd.RangeIndex(-99, -201, -3), -3, pd.RangeIndex(33, 67, 1)),
1281-
(pd.RangeIndex(0, 1000, 1), 2, pd.RangeIndex(0, 1000, 1)._int64index // 2),
1285+
(RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)),
1286+
(RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)),
1287+
(RangeIndex(0, 1000, 1), 2, RangeIndex(0, 1000, 1)._int64index // 2),
12821288
(
1283-
pd.RangeIndex(0, 100, 1),
1289+
RangeIndex(0, 100, 1),
12841290
2.0,
1285-
pd.RangeIndex(0, 100, 1)._int64index // 2.0,
1291+
RangeIndex(0, 100, 1)._int64index // 2.0,
12861292
),
1287-
(pd.RangeIndex(0), 50, pd.RangeIndex(0)),
1288-
(pd.RangeIndex(2, 4, 2), 3, pd.RangeIndex(0, 1, 1)),
1289-
(pd.RangeIndex(-5, -10, -6), 4, pd.RangeIndex(-2, -1, 1)),
1290-
(pd.RangeIndex(-100, -200, 3), 2, pd.RangeIndex(0)),
1293+
(RangeIndex(0), 50, RangeIndex(0)),
1294+
(RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)),
1295+
(RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)),
1296+
(RangeIndex(-100, -200, 3), 2, RangeIndex(0)),
12911297
]
12921298
for idx, div, expected in cases_exact:
12931299
tm.assert_index_equal(idx // div, expected, exact=True)

‎pandas/tests/dtypes/test_inference.py

Lines changed: 9 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -45,6 +45,7 @@
4545
Index,
4646
Interval,
4747
Period,
48+
PeriodIndex,
4849
Series,
4950
Timedelta,
5051
TimedeltaIndex,
@@ -884,30 +885,30 @@ def test_infer_dtype_timedelta_with_na(self, na_value, delta):
884885

885886
def test_infer_dtype_period(self):
886887
# GH 13664
887-
arr = np.array([pd.Period("2011-01", freq="D"), pd.Period("2011-02", freq="D")])
888+
arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="D")])
888889
assert lib.infer_dtype(arr, skipna=True) == "period"
889890

890-
arr = np.array([pd.Period("2011-01", freq="D"), pd.Period("2011-02", freq="M")])
891+
arr = np.array([Period("2011-01", freq="D"), Period("2011-02", freq="M")])
891892
assert lib.infer_dtype(arr, skipna=True) == "period"
892893

893894
def test_infer_dtype_period_mixed(self):
894895
arr = np.array(
895-
[pd.Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
896+
[Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
896897
)
897898
assert lib.infer_dtype(arr, skipna=False) == "mixed"
898899

899900
arr = np.array(
900-
[np.datetime64("nat"), pd.Period("2011-01", freq="M")], dtype=object
901+
[np.datetime64("nat"), Period("2011-01", freq="M")], dtype=object
901902
)
902903
assert lib.infer_dtype(arr, skipna=False) == "mixed"
903904

904905
@pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
905906
def test_infer_dtype_period_with_na(self, na_value):
906907
# starts with nan
907-
arr = np.array([na_value, pd.Period("2011-01", freq="D")])
908+
arr = np.array([na_value, Period("2011-01", freq="D")])
908909
assert lib.infer_dtype(arr, skipna=True) == "period"
909910

910-
arr = np.array([na_value, pd.Period("2011-01", freq="D"), na_value])
911+
arr = np.array([na_value, Period("2011-01", freq="D"), na_value])
911912
assert lib.infer_dtype(arr, skipna=True) == "period"
912913

913914
@pytest.mark.parametrize(
@@ -1192,8 +1193,8 @@ def test_to_object_array_width(self):
11921193
tm.assert_numpy_array_equal(out, expected)
11931194

11941195
def test_is_period(self):
1195-
assert lib.is_period(pd.Period("2011-01", freq="M"))
1196-
assert not lib.is_period(pd.PeriodIndex(["2011-01"], freq="M"))
1196+
assert lib.is_period(Period("2011-01", freq="M"))
1197+
assert not lib.is_period(PeriodIndex(["2011-01"], freq="M"))
11971198
assert not lib.is_period(Timestamp("2011-01"))
11981199
assert not lib.is_period(1)
11991200
assert not lib.is_period(np.nan)

‎pandas/tests/groupby/test_groupby.py

Lines changed: 15 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -8,15 +8,24 @@
88
from pandas.errors import PerformanceWarning
99

1010
import pandas as pd
11-
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, read_csv
11+
from pandas import (
12+
DataFrame,
13+
Grouper,
14+
Index,
15+
MultiIndex,
16+
Series,
17+
Timestamp,
18+
date_range,
19+
read_csv,
20+
)
1221
import pandas._testing as tm
1322
from pandas.core.base import SpecificationError
1423
import pandas.core.common as com
1524

1625

1726
def test_repr():
1827
# GH18203
19-
result = repr(pd.Grouper(key="A", level="B"))
28+
result = repr(Grouper(key="A", level="B"))
2029
expected = "Grouper(key='A', level='B', axis=0, sort=False)"
2130
assert result == expected
2231

@@ -1218,7 +1227,7 @@ def test_groupby_keys_same_size_as_index():
12181227
start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq
12191228
)
12201229
df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index)
1221-
result = df.groupby([pd.Grouper(level=0, freq=freq), "metric"]).mean()
1230+
result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean()
12221231
expected = df.set_index([df.index, "metric"])
12231232

12241233
tm.assert_frame_equal(result, expected)
@@ -1815,7 +1824,7 @@ def test_groupby_agg_ohlc_non_first():
18151824
index=pd.date_range("2018-01-01", periods=2, freq="D"),
18161825
)
18171826

1818-
result = df.groupby(pd.Grouper(freq="D")).agg(["sum", "ohlc"])
1827+
result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"])
18191828

18201829
tm.assert_frame_equal(result, expected)
18211830

@@ -1866,11 +1875,11 @@ def test_groupby_groups_in_BaseGrouper():
18661875
# Test if DataFrame grouped with a pandas.Grouper has correct groups
18671876
mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"])
18681877
df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
1869-
result = df.groupby([pd.Grouper(level="alpha"), "beta"])
1878+
result = df.groupby([Grouper(level="alpha"), "beta"])
18701879
expected = df.groupby(["alpha", "beta"])
18711880
assert result.groups == expected.groups
18721881

1873-
result = df.groupby(["beta", pd.Grouper(level="alpha")])
1882+
result = df.groupby(["beta", Grouper(level="alpha")])
18741883
expected = df.groupby(["beta", "alpha"])
18751884
assert result.groups == expected.groups
18761885

‎pandas/tests/groupby/test_timegrouper.py

Lines changed: 28 additions & 30 deletions
Original file line numberDiff line numberDiff line change
@@ -61,10 +61,10 @@ def test_groupby_with_timegrouper(self):
6161
tm.assert_frame_equal(result1, expected)
6262

6363
df_sorted = df.sort_index()
64-
result2 = df_sorted.groupby(pd.Grouper(freq="5D")).sum()
64+
result2 = df_sorted.groupby(Grouper(freq="5D")).sum()
6565
tm.assert_frame_equal(result2, expected)
6666

67-
result3 = df.groupby(pd.Grouper(freq="5D")).sum()
67+
result3 = df.groupby(Grouper(freq="5D")).sum()
6868
tm.assert_frame_equal(result3, expected)
6969

7070
@pytest.mark.parametrize("should_sort", [True, False])
@@ -92,7 +92,7 @@ def test_groupby_with_timegrouper_methods(self, should_sort):
9292
df = df.sort_values(by="Quantity", ascending=False)
9393

9494
df = df.set_index("Date", drop=False)
95-
g = df.groupby(pd.Grouper(freq="6M"))
95+
g = df.groupby(Grouper(freq="6M"))
9696
assert g.group_keys
9797

9898
assert isinstance(g.grouper, BinGrouper)
@@ -138,7 +138,7 @@ def test_timegrouper_with_reg_groups(self):
138138
}
139139
).set_index(["Date", "Buyer"])
140140

141-
result = df.groupby([pd.Grouper(freq="A"), "Buyer"]).sum()
141+
result = df.groupby([Grouper(freq="A"), "Buyer"]).sum()
142142
tm.assert_frame_equal(result, expected)
143143

144144
expected = DataFrame(
@@ -153,7 +153,7 @@ def test_timegrouper_with_reg_groups(self):
153153
],
154154
}
155155
).set_index(["Date", "Buyer"])
156-
result = df.groupby([pd.Grouper(freq="6MS"), "Buyer"]).sum()
156+
result = df.groupby([Grouper(freq="6MS"), "Buyer"]).sum()
157157
tm.assert_frame_equal(result, expected)
158158

159159
df_original = DataFrame(
@@ -191,10 +191,10 @@ def test_timegrouper_with_reg_groups(self):
191191
}
192192
).set_index(["Date", "Buyer"])
193193

194-
result = df.groupby([pd.Grouper(freq="1D"), "Buyer"]).sum()
194+
result = df.groupby([Grouper(freq="1D"), "Buyer"]).sum()
195195
tm.assert_frame_equal(result, expected)
196196

197-
result = df.groupby([pd.Grouper(freq="1M"), "Buyer"]).sum()
197+
result = df.groupby([Grouper(freq="1M"), "Buyer"]).sum()
198198
expected = DataFrame(
199199
{
200200
"Buyer": "Carl Joe Mark".split(),
@@ -210,26 +210,26 @@ def test_timegrouper_with_reg_groups(self):
210210

211211
# passing the name
212212
df = df.reset_index()
213-
result = df.groupby([pd.Grouper(freq="1M", key="Date"), "Buyer"]).sum()
213+
result = df.groupby([Grouper(freq="1M", key="Date"), "Buyer"]).sum()
214214
tm.assert_frame_equal(result, expected)
215215

216216
with pytest.raises(KeyError, match="'The grouper name foo is not found'"):
217-
df.groupby([pd.Grouper(freq="1M", key="foo"), "Buyer"]).sum()
217+
df.groupby([Grouper(freq="1M", key="foo"), "Buyer"]).sum()
218218

219219
# passing the level
220220
df = df.set_index("Date")
221-
result = df.groupby([pd.Grouper(freq="1M", level="Date"), "Buyer"]).sum()
221+
result = df.groupby([Grouper(freq="1M", level="Date"), "Buyer"]).sum()
222222
tm.assert_frame_equal(result, expected)
223-
result = df.groupby([pd.Grouper(freq="1M", level=0), "Buyer"]).sum()
223+
result = df.groupby([Grouper(freq="1M", level=0), "Buyer"]).sum()
224224
tm.assert_frame_equal(result, expected)
225225

226226
with pytest.raises(ValueError, match="The level foo is not valid"):
227-
df.groupby([pd.Grouper(freq="1M", level="foo"), "Buyer"]).sum()
227+
df.groupby([Grouper(freq="1M", level="foo"), "Buyer"]).sum()
228228

229229
# multi names
230230
df = df.copy()
231231
df["Date"] = df.index + pd.offsets.MonthEnd(2)
232-
result = df.groupby([pd.Grouper(freq="1M", key="Date"), "Buyer"]).sum()
232+
result = df.groupby([Grouper(freq="1M", key="Date"), "Buyer"]).sum()
233233
expected = DataFrame(
234234
{
235235
"Buyer": "Carl Joe Mark".split(),
@@ -247,7 +247,7 @@ def test_timegrouper_with_reg_groups(self):
247247
msg = "The Grouper cannot specify both a key and a level!"
248248
with pytest.raises(ValueError, match=msg):
249249
df.groupby(
250-
[pd.Grouper(freq="1M", key="Date", level="Date"), "Buyer"]
250+
[Grouper(freq="1M", key="Date", level="Date"), "Buyer"]
251251
).sum()
252252

253253
# single groupers
@@ -258,18 +258,18 @@ def test_timegrouper_with_reg_groups(self):
258258
[datetime(2013, 10, 31, 0, 0)], freq=offsets.MonthEnd(), name="Date"
259259
),
260260
)
261-
result = df.groupby(pd.Grouper(freq="1M")).sum()
261+
result = df.groupby(Grouper(freq="1M")).sum()
262262
tm.assert_frame_equal(result, expected)
263263

264-
result = df.groupby([pd.Grouper(freq="1M")]).sum()
264+
result = df.groupby([Grouper(freq="1M")]).sum()
265265
tm.assert_frame_equal(result, expected)
266266

267267
expected.index = expected.index.shift(1)
268268
assert expected.index.freq == offsets.MonthEnd()
269-
result = df.groupby(pd.Grouper(freq="1M", key="Date")).sum()
269+
result = df.groupby(Grouper(freq="1M", key="Date")).sum()
270270
tm.assert_frame_equal(result, expected)
271271

272-
result = df.groupby([pd.Grouper(freq="1M", key="Date")]).sum()
272+
result = df.groupby([Grouper(freq="1M", key="Date")]).sum()
273273
tm.assert_frame_equal(result, expected)
274274

275275
@pytest.mark.parametrize("freq", ["D", "M", "A", "Q-APR"])
@@ -324,13 +324,11 @@ def test_timegrouper_with_reg_groups_freq(self, freq):
324324
expected.name = "whole_cost"
325325

326326
result1 = (
327-
df.sort_index()
328-
.groupby([pd.Grouper(freq=freq), "user_id"])["whole_cost"]
329-
.sum()
327+
df.sort_index().groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
330328
)
331329
tm.assert_series_equal(result1, expected)
332330

333-
result2 = df.groupby([pd.Grouper(freq=freq), "user_id"])["whole_cost"].sum()
331+
result2 = df.groupby([Grouper(freq=freq), "user_id"])["whole_cost"].sum()
334332
tm.assert_series_equal(result2, expected)
335333

336334
def test_timegrouper_get_group(self):
@@ -361,7 +359,7 @@ def test_timegrouper_get_group(self):
361359
dt_list = ["2013-09-30", "2013-10-31", "2013-12-31"]
362360

363361
for df in [df_original, df_reordered]:
364-
grouped = df.groupby(pd.Grouper(freq="M", key="Date"))
362+
grouped = df.groupby(Grouper(freq="M", key="Date"))
365363
for t, expected in zip(dt_list, expected_list):
366364
dt = Timestamp(t)
367365
result = grouped.get_group(dt)
@@ -376,7 +374,7 @@ def test_timegrouper_get_group(self):
376374
g_list = [("Joe", "2013-09-30"), ("Carl", "2013-10-31"), ("Joe", "2013-12-31")]
377375

378376
for df in [df_original, df_reordered]:
379-
grouped = df.groupby(["Buyer", pd.Grouper(freq="M", key="Date")])
377+
grouped = df.groupby(["Buyer", Grouper(freq="M", key="Date")])
380378
for (b, t), expected in zip(g_list, expected_list):
381379
dt = Timestamp(t)
382380
result = grouped.get_group((b, dt))
@@ -393,7 +391,7 @@ def test_timegrouper_get_group(self):
393391
]
394392

395393
for df in [df_original, df_reordered]:
396-
grouped = df.groupby(pd.Grouper(freq="M"))
394+
grouped = df.groupby(Grouper(freq="M"))
397395
for t, expected in zip(dt_list, expected_list):
398396
dt = Timestamp(t)
399397
result = grouped.get_group(dt)
@@ -410,8 +408,8 @@ def test_timegrouper_apply_return_type_series(self):
410408
def sumfunc_series(x):
411409
return Series([x["value"].sum()], ("sum",))
412410

413-
expected = df.groupby(pd.Grouper(key="date")).apply(sumfunc_series)
414-
result = df_dt.groupby(pd.Grouper(freq="M", key="date")).apply(sumfunc_series)
411+
expected = df.groupby(Grouper(key="date")).apply(sumfunc_series)
412+
result = df_dt.groupby(Grouper(freq="M", key="date")).apply(sumfunc_series)
415413
tm.assert_frame_equal(
416414
result.reset_index(drop=True), expected.reset_index(drop=True)
417415
)
@@ -427,7 +425,7 @@ def test_timegrouper_apply_return_type_value(self):
427425
def sumfunc_value(x):
428426
return x.value.sum()
429427

430-
expected = df.groupby(pd.Grouper(key="date")).apply(sumfunc_value)
428+
expected = df.groupby(Grouper(key="date")).apply(sumfunc_value)
431429
result = df_dt.groupby(Grouper(freq="M", key="date")).apply(sumfunc_value)
432430
tm.assert_series_equal(
433431
result.reset_index(drop=True), expected.reset_index(drop=True)
@@ -744,7 +742,7 @@ def test_nunique_with_timegrouper_and_nat(self):
744742
}
745743
)
746744

747-
grouper = pd.Grouper(key="time", freq="h")
745+
grouper = Grouper(key="time", freq="h")
748746
result = test.groupby(grouper)["data"].nunique()
749747
expected = test[test.time.notnull()].groupby(grouper)["data"].nunique()
750748
expected.index = expected.index._with_freq(None)
@@ -761,7 +759,7 @@ def test_scalar_call_versus_list_call(self):
761759
"value": [1, 2, 3],
762760
}
763761
data_frame = DataFrame(data_frame).set_index("time")
764-
grouper = pd.Grouper(freq="D")
762+
grouper = Grouper(freq="D")
765763

766764
grouped = data_frame.groupby(grouper)
767765
result = grouped.count()

‎pandas/tests/indexes/categorical/test_map.py

Lines changed: 6 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -25,16 +25,16 @@ def test_map_str(self, data, categories, ordered):
2525
tm.assert_index_equal(result, expected)
2626

2727
def test_map(self):
28-
ci = pd.CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
28+
ci = CategoricalIndex(list("ABABC"), categories=list("CBA"), ordered=True)
2929
result = ci.map(lambda x: x.lower())
30-
exp = pd.CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
30+
exp = CategoricalIndex(list("ababc"), categories=list("cba"), ordered=True)
3131
tm.assert_index_equal(result, exp)
3232

33-
ci = pd.CategoricalIndex(
33+
ci = CategoricalIndex(
3434
list("ABABC"), categories=list("BAC"), ordered=False, name="XXX"
3535
)
3636
result = ci.map(lambda x: x.lower())
37-
exp = pd.CategoricalIndex(
37+
exp = CategoricalIndex(
3838
list("ababc"), categories=list("bac"), ordered=False, name="XXX"
3939
)
4040
tm.assert_index_equal(result, exp)
@@ -45,13 +45,13 @@ def test_map(self):
4545
)
4646

4747
# change categories dtype
48-
ci = pd.CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
48+
ci = CategoricalIndex(list("ABABC"), categories=list("BAC"), ordered=False)
4949

5050
def f(x):
5151
return {"A": 10, "B": 20, "C": 30}.get(x)
5252

5353
result = ci.map(f)
54-
exp = pd.CategoricalIndex(
54+
exp = CategoricalIndex(
5555
[10, 20, 10, 20, 30], categories=[20, 10, 30], ordered=False
5656
)
5757
tm.assert_index_equal(result, exp)

‎pandas/tests/indexes/interval/test_interval.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -869,7 +869,7 @@ def test_set_closed_errors(self, bad_closed):
869869

870870
def test_is_all_dates(self):
871871
# GH 23576
872-
year_2017 = pd.Interval(
872+
year_2017 = Interval(
873873
Timestamp("2017-01-01 00:00:00"), Timestamp("2018-01-01 00:00:00")
874874
)
875875
year_2017_index = pd.IntervalIndex([year_2017])

‎pandas/tests/indexes/period/test_ops.py

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -270,17 +270,17 @@ def test_order(self):
270270
assert ordered.freq == "D"
271271

272272
def test_nat(self):
273-
assert pd.PeriodIndex._na_value is NaT
274-
assert pd.PeriodIndex([], freq="M")._na_value is NaT
273+
assert PeriodIndex._na_value is NaT
274+
assert PeriodIndex([], freq="M")._na_value is NaT
275275

276-
idx = pd.PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
276+
idx = PeriodIndex(["2011-01-01", "2011-01-02"], freq="D")
277277
assert idx._can_hold_na
278278

279279
tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
280280
assert idx.hasnans is False
281281
tm.assert_numpy_array_equal(idx._nan_idxs, np.array([], dtype=np.intp))
282282

283-
idx = pd.PeriodIndex(["2011-01-01", "NaT"], freq="D")
283+
idx = PeriodIndex(["2011-01-01", "NaT"], freq="D")
284284
assert idx._can_hold_na
285285

286286
tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
@@ -290,7 +290,7 @@ def test_nat(self):
290290
@pytest.mark.parametrize("freq", ["D", "M"])
291291
def test_equals(self, freq):
292292
# GH#13107
293-
idx = pd.PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq=freq)
293+
idx = PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq=freq)
294294
assert idx.equals(idx)
295295
assert idx.equals(idx.copy())
296296
assert idx.equals(idx.astype(object))
@@ -299,7 +299,7 @@ def test_equals(self, freq):
299299
assert not idx.equals(list(idx))
300300
assert not idx.equals(Series(idx))
301301

302-
idx2 = pd.PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq="H")
302+
idx2 = PeriodIndex(["2011-01-01", "2011-01-02", "NaT"], freq="H")
303303
assert not idx.equals(idx2)
304304
assert not idx.equals(idx2.copy())
305305
assert not idx.equals(idx2.astype(object))
@@ -308,7 +308,7 @@ def test_equals(self, freq):
308308
assert not idx.equals(Series(idx2))
309309

310310
# same internal, different tz
311-
idx3 = pd.PeriodIndex._simple_new(
311+
idx3 = PeriodIndex._simple_new(
312312
idx._values._simple_new(idx._values.asi8, freq="H")
313313
)
314314
tm.assert_numpy_array_equal(idx.asi8, idx3.asi8)

‎pandas/tests/series/test_constructors.py

Lines changed: 5 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -27,6 +27,7 @@
2727
MultiIndex,
2828
NaT,
2929
Period,
30+
RangeIndex,
3031
Series,
3132
Timestamp,
3233
date_range,
@@ -267,7 +268,7 @@ def test_constructor_index_dtype(self, dtype):
267268
(["1", "2"]),
268269
(list(pd.date_range("1/1/2011", periods=2, freq="H"))),
269270
(list(pd.date_range("1/1/2011", periods=2, freq="H", tz="US/Eastern"))),
270-
([pd.Interval(left=0, right=5)]),
271+
([Interval(left=0, right=5)]),
271272
],
272273
)
273274
def test_constructor_list_str(self, input_vals, string_dtype):
@@ -624,7 +625,7 @@ def test_constructor_copy(self):
624625
pd.period_range("2012Q1", periods=3, freq="Q"),
625626
Index(list("abc")),
626627
pd.Int64Index([1, 2, 3]),
627-
pd.RangeIndex(0, 3),
628+
RangeIndex(0, 3),
628629
],
629630
ids=lambda x: type(x).__name__,
630631
)
@@ -1004,7 +1005,7 @@ def test_construction_interval(self, interval_constructor):
10041005
)
10051006
def test_constructor_infer_interval(self, data_constructor):
10061007
# GH 23563: consistent closed results in interval dtype
1007-
data = [pd.Interval(0, 1), pd.Interval(0, 2), None]
1008+
data = [Interval(0, 1), Interval(0, 2), None]
10081009
result = Series(data_constructor(data))
10091010
expected = Series(IntervalArray(data))
10101011
assert result.dtype == "interval[float64]"
@@ -1015,7 +1016,7 @@ def test_constructor_infer_interval(self, data_constructor):
10151016
)
10161017
def test_constructor_interval_mixed_closed(self, data_constructor):
10171018
# GH 23563: mixed closed results in object dtype (not interval dtype)
1018-
data = [pd.Interval(0, 1, closed="both"), pd.Interval(0, 2, closed="neither")]
1019+
data = [Interval(0, 1, closed="both"), Interval(0, 2, closed="neither")]
10191020
result = Series(data_constructor(data))
10201021
assert result.dtype == object
10211022
assert result.tolist() == data

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