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Comma cleanup for Issue #35925 #35970

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84 changes: 42 additions & 42 deletions pandas/tests/frame/test_analytics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
isna,
notna,
to_datetime,
to_timedelta,
to_timedelta
)
import pandas._testing as tm
import pandas.core.algorithms as algorithms
Expand All @@ -34,7 +34,7 @@ def assert_stat_op_calc(
check_dates=False,
rtol=1e-5,
atol=1e-8,
skipna_alternative=None,
skipna_alternative=None
):
"""
Check that operator opname works as advertised on frame
Expand Down Expand Up @@ -90,15 +90,15 @@ def wrapper(x):
frame.apply(wrapper),
check_dtype=check_dtype,
rtol=rtol,
atol=atol,
atol=atol
)
# HACK: win32
tm.assert_series_equal(
result1,
frame.apply(wrapper, axis=1),
check_dtype=False,
rtol=rtol,
atol=atol,
atol=atol
)
else:
skipna_wrapper = alternative
Expand All @@ -110,13 +110,13 @@ def wrapper(x):
frame.apply(skipna_wrapper),
check_dtype=check_dtype,
rtol=rtol,
atol=atol,
atol=atol
)

if opname in ["sum", "prod"]:
expected = frame.apply(skipna_wrapper, axis=1)
tm.assert_series_equal(
result1, expected, check_dtype=False, rtol=rtol, atol=atol,
result1, expected, check_dtype=False, rtol=rtol, atol=atol
)

# check dtypes
Expand Down Expand Up @@ -333,7 +333,7 @@ def kurt(x):
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
check_dates=True
)

# GH#32571 check_less_precise is needed on apparently-random
Expand All @@ -344,7 +344,7 @@ def kurt(x):
np.sum,
mixed_float_frame.astype("float32"),
check_dtype=False,
rtol=1e-3,
rtol=1e-3
)

assert_stat_op_calc(
Expand All @@ -366,7 +366,7 @@ def kurt(x):
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
check_dates=True
)

try:
Expand Down Expand Up @@ -399,14 +399,14 @@ def test_stat_operators_attempt_obj_array(self, method):
"a": [
-0.00049987540199591344,
-0.0016467257772919831,
0.00067695870775883013,
0.00067695870775883013
],
"b": [-0, -0, 0.0],
"c": [
0.00031111847529610595,
0.0014902627951905339,
-0.00094099200035979691,
],
-0.00094099200035979691
]
}
df1 = DataFrame(data, index=["foo", "bar", "baz"], dtype="O")

Expand All @@ -427,7 +427,7 @@ def test_mixed_ops(self, op):
{
"int": [1, 2, 3, 4],
"float": [1.0, 2.0, 3.0, 4.0],
"str": ["a", "b", "c", "d"],
"str": ["a", "b", "c", "d"]
}
)

Expand All @@ -444,7 +444,7 @@ def test_reduce_mixed_frame(self):
{
"bool_data": [True, True, False, False, False],
"int_data": [10, 20, 30, 40, 50],
"string_data": ["a", "b", "c", "d", "e"],
"string_data": ["a", "b", "c", "d", "e"]
}
)
df.reindex(columns=["bool_data", "int_data", "string_data"])
Expand Down Expand Up @@ -500,7 +500,7 @@ def test_mean_mixed_string_decimal(self):
{"A": 2, "B": None, "C": Decimal("572.00")},
{"A": 4, "B": None, "C": Decimal("609.00")},
{"A": 3, "B": None, "C": Decimal("820.00")},
{"A": 5, "B": None, "C": Decimal("1223.00")},
{"A": 5, "B": None, "C": Decimal("1223.00")}
]

df = pd.DataFrame(d)
Expand Down Expand Up @@ -570,7 +570,7 @@ def test_sem(self, datetime_frame):
def test_kurt(self):
index = MultiIndex(
levels=[["bar"], ["one", "two", "three"], [0, 1]],
codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]]
)
df = DataFrame(np.random.randn(6, 3), index=index)

Expand All @@ -592,8 +592,8 @@ def test_kurt(self):
"D": ["a"],
"E": Categorical(["a"], categories=["a"]),
"F": to_datetime(["2000-1-2"]),
"G": to_timedelta(["1 days"]),
},
"G": to_timedelta(["1 days"])
}
),
(
False,
Expand All @@ -604,8 +604,8 @@ def test_kurt(self):
"D": np.array([np.nan], dtype=object),
"E": Categorical([np.nan], categories=["a"]),
"F": [pd.NaT],
"G": to_timedelta([pd.NaT]),
},
"G": to_timedelta([pd.NaT])
}
),
(
True,
Expand All @@ -616,8 +616,8 @@ def test_kurt(self):
"K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
"L": to_datetime(["2000-1-2", "NaT", "NaT", "NaT"]),
"M": to_timedelta(["1 days", "nan", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
"N": [0, 1, 2, 3]
}
),
(
False,
Expand All @@ -628,10 +628,10 @@ def test_kurt(self):
"K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
"L": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
"M": to_timedelta(["nan", "1 days", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
),
],
"N": [0, 1, 2, 3]
}
)
]
)
def test_mode_dropna(self, dropna, expected):

Expand All @@ -650,7 +650,7 @@ def test_mode_dropna(self, dropna, expected):
"K": Categorical(["a", np.nan, "a", np.nan]),
"L": to_datetime(["2000-1-2", "2000-1-2", "NaT", "NaT"]),
"M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
"N": np.arange(4, dtype="int64"),
"N": np.arange(4, dtype="int64")
}
)

Expand All @@ -676,7 +676,7 @@ def test_operators_timedelta64(self):
dict(
A=date_range("2012-1-1", periods=3, freq="D"),
B=date_range("2012-1-2", periods=3, freq="D"),
C=Timestamp("20120101") - timedelta(minutes=5, seconds=5),
C=Timestamp("20120101") - timedelta(minutes=5, seconds=5)
)
)

Expand Down Expand Up @@ -721,9 +721,9 @@ def test_operators_timedelta64(self):
"foo",
1,
1.0,
Timestamp("20130101"),
Timestamp("20130101")
],
index=mixed.columns,
index=mixed.columns
)
tm.assert_series_equal(result, expected)

Expand All @@ -747,7 +747,7 @@ def test_operators_timedelta64(self):
df = DataFrame(
{
"time": date_range("20130102", periods=5),
"time2": date_range("20130105", periods=5),
"time2": date_range("20130105", periods=5)
}
)
df["off1"] = df["time2"] - df["time"]
Expand Down Expand Up @@ -871,7 +871,7 @@ def test_mean_datetimelike(self):
"A": np.arange(3),
"B": pd.date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
"D": pd.period_range("2016", periods=3, freq="A"),
"D": pd.period_range("2016", periods=3, freq="A")
}
)
result = df.mean(numeric_only=True)
Expand All @@ -889,7 +889,7 @@ def test_mean_datetimelike_numeric_only_false(self):
{
"A": np.arange(3),
"B": pd.date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
"C": pd.timedelta_range("1D", periods=3)
}
)

Expand Down Expand Up @@ -974,9 +974,9 @@ def test_any_all_extra(self):
{
"A": [True, False, False],
"B": [True, True, False],
"C": [True, True, True],
"C": [True, True, True]
},
index=["a", "b", "c"],
index=["a", "b", "c"]
)
result = df[["A", "B"]].any(1)
expected = Series([True, True, False], index=["a", "b", "c"])
Expand Down Expand Up @@ -1010,7 +1010,7 @@ def test_any_datetime(self):
pd.Timestamp("1960-02-15"),
pd.Timestamp("1960-02-16"),
pd.NaT,
pd.NaT,
pd.NaT
]
df = DataFrame({"A": float_data, "B": datetime_data})

Expand All @@ -1034,7 +1034,7 @@ def test_any_all_bool_only(self):
"col1": [1, 2, 3],
"col2": [4, 5, 6],
"col3": [None, None, None],
"col4": [False, False, True],
"col4": [False, False, True]
}
)

Expand Down Expand Up @@ -1125,9 +1125,9 @@ def test_any_all_bool_only(self):
},
True,
# In 1.13.3 and 1.14 np.all(df) returns a Timedelta here
marks=[td.skip_if_np_lt("1.15")],
),
],
marks=[td.skip_if_np_lt("1.15")]
)
]
)
def test_any_all_np_func(self, func, data, expected):
# GH 19976
Expand Down Expand Up @@ -1155,7 +1155,7 @@ def test_any_all_level_axis_none_raises(self, method):
{"A": 1},
index=MultiIndex.from_product(
[["A", "B"], ["a", "b"]], names=["out", "in"]
),
)
)
xpr = "Must specify 'axis' when aggregating by level."
with pytest.raises(ValueError, match=xpr):
Expand Down Expand Up @@ -1293,7 +1293,7 @@ def test_min_max_dt64_api_consistency_empty_df(self):

@pytest.mark.parametrize(
"initial",
["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"], # Non-UTC timezone
["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"] # Non-UTC timezone
)
@pytest.mark.parametrize("method", ["min", "max"])
def test_preserve_timezone(self, initial: str, method):
Expand Down
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