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CLN: Remove trailing commas #36295

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7900c42
removed trailing commas from test_algos.py
Sep 11, 2020
767884b
Removed all trailing commas from pandas/tests/series/methods/test_int…
Sep 11, 2020
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84 changes: 14 additions & 70 deletions pandas/tests/series/methods/test_interpolate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,8 +26,7 @@
"from_derivatives",
"pchip",
"akima",
"cubicspline",
]
"cubicspline"]
)
def nontemporal_method(request):
"""Fixture that returns an (method name, required kwargs) pair.
Expand Down Expand Up @@ -56,8 +55,7 @@ def nontemporal_method(request):
"from_derivatives",
"pchip",
"akima",
"cubicspline",
]
"cubicspline"]
)
def interp_methods_ind(request):
"""Fixture that returns a (method name, required kwargs) pair to
Expand Down Expand Up @@ -104,10 +102,7 @@ def test_interpolate_cubicspline(self):

ser = Series([10, 11, 12, 13])

expected = Series(
[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
)
expected = Series([11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
# interpolate at new_index
new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
float
Expand All @@ -134,21 +129,15 @@ def test_interpolate_akima(self):
ser = Series([10, 11, 12, 13])

# interpolate at new_index where `der` is zero
expected = Series(
[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
)
expected = Series([11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
float
)
interp_s = ser.reindex(new_index).interpolate(method="akima")
tm.assert_series_equal(interp_s[1:3], expected)

# interpolate at new_index where `der` is a non-zero int
expected = Series(
[11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0],
index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
)
expected = Series([11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0], index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
float
)
Expand All @@ -159,10 +148,7 @@ def test_interpolate_akima(self):
def test_interpolate_piecewise_polynomial(self):
ser = Series([10, 11, 12, 13])

expected = Series(
[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
)
expected = Series([11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
# interpolate at new_index
new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
float
Expand All @@ -174,10 +160,7 @@ def test_interpolate_piecewise_polynomial(self):
def test_interpolate_from_derivatives(self):
ser = Series([10, 11, 12, 13])

expected = Series(
[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
)
expected = Series([11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00], index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]))
# interpolate at new_index
new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
float
Expand All @@ -187,13 +170,7 @@ def test_interpolate_from_derivatives(self):

@pytest.mark.parametrize(
"kwargs",
[
{},
pytest.param(
{"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
),
],
)
[{}, pytest.param({"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy)])
def test_interpolate_corners(self, kwargs):
s = Series([np.nan, np.nan])
tm.assert_series_equal(s.interpolate(**kwargs), s)
Expand Down Expand Up @@ -233,15 +210,7 @@ def test_interpolate_non_ts(self):
with pytest.raises(ValueError, match=msg):
s.interpolate(method="time")

@pytest.mark.parametrize(
"kwargs",
[
{},
pytest.param(
{"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
),
],
)
@pytest.mark.parametrize("kwargs",[{}, pytest.param({"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy)])
def test_nan_interpolate(self, kwargs):
s = Series([0, 1, np.nan, 3])
result = s.interpolate(**kwargs)
Expand Down Expand Up @@ -431,17 +400,7 @@ def test_interp_limit_area(self):

@pytest.mark.parametrize(
"method, limit_direction, expected",
[
("pad", "backward", "forward"),
("ffill", "backward", "forward"),
("backfill", "forward", "backward"),
("bfill", "forward", "backward"),
("pad", "both", "forward"),
("ffill", "both", "forward"),
("backfill", "both", "backward"),
("bfill", "both", "backward"),
],
)
[("pad", "backward", "forward"), ("ffill", "backward", "forward"), ("backfill", "forward", "backward"), ("bfill", "forward", "backward"), ("pad", "both", "forward"), ("ffill", "both", "forward"), ("backfill", "both", "backward"), ("bfill", "both", "backward")])
def test_interp_limit_direction_raises(self, method, limit_direction, expected):
# https://github.com/pandas-dev/pandas/pull/34746
s = Series([1, 2, 3])
Expand Down Expand Up @@ -540,14 +499,9 @@ def test_interp_nonmono_raise(self):
@td.skip_if_no_scipy
@pytest.mark.parametrize("method", ["nearest", "pad"])
def test_interp_datetime64(self, method, tz_naive_fixture):
df = Series(
[1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
)
df = Series([1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture))
result = df.interpolate(method=method)
expected = Series(
[1.0, 1.0, 3.0],
index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
)
expected = Series([1.0, 1.0, 3.0], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture))
tm.assert_series_equal(result, expected)

def test_interp_pad_datetime64tz_values(self):
Expand Down Expand Up @@ -658,14 +612,7 @@ def test_series_interpolate_intraday(self):

tm.assert_numpy_array_equal(result.values, exp.values)

@pytest.mark.parametrize(
"ind",
[
["a", "b", "c", "d"],
pd.period_range(start="2019-01-01", periods=4),
pd.interval_range(start=0, end=4),
],
)
@pytest.mark.parametrize("ind",[["a", "b", "c", "d"], pd.period_range(start="2019-01-01", periods=4), pd.interval_range(start=0, end=4)])
def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
# gh 21662
df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
Expand Down Expand Up @@ -711,10 +658,7 @@ def test_interpolate_timedelta_index(self, interp_methods_ind):
"This interpolation method is not supported for Timedelta Index yet."
)

@pytest.mark.parametrize(
"ascending, expected_values",
[(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
)
@pytest.mark.parametrize("ascending, expected_values", [(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])])
def test_interpolate_unsorted_index(self, ascending, expected_values):
# GH 21037
ts = pd.Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
Expand Down
56 changes: 12 additions & 44 deletions pandas/tests/series/methods/test_unstack.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,34 +7,21 @@


def test_unstack():
index = MultiIndex(
levels=[["bar", "foo"], ["one", "three", "two"]],
codes=[[1, 1, 0, 0], [0, 1, 0, 2]],
)
index = MultiIndex(levels=[["bar", "foo"], ["one", "three", "two"]], codes=[[1, 1, 0, 0], [0, 1, 0, 2]])

s = Series(np.arange(4.0), index=index)
unstacked = s.unstack()

expected = DataFrame(
[[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]],
index=["bar", "foo"],
columns=["one", "three", "two"],
)
expected = DataFrame([[2.0, np.nan, 3.0], [0.0, 1.0, np.nan]], index=["bar", "foo"], columns=["one", "three", "two"])

tm.assert_frame_equal(unstacked, expected)

unstacked = s.unstack(level=0)
tm.assert_frame_equal(unstacked, expected.T)

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]],
)
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]])
s = Series(np.random.randn(6), index=index)
exp_index = MultiIndex(
levels=[["one", "two", "three"], [0, 1]],
codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
)
exp_index = MultiIndex(levels=[["one", "two", "three"], [0, 1]], codes=[[0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]])
expected = DataFrame({"bar": s.values}, index=exp_index).sort_index(level=0)
unstacked = s.unstack(0).sort_index()
tm.assert_frame_equal(unstacked, expected)
Expand All @@ -48,18 +35,9 @@ def test_unstack():
)
tm.assert_frame_equal(left, right)

idx = pd.MultiIndex.from_arrays(
[
["cat", "cat", "cat", "dog", "dog"],
["a", "a", "b", "a", "b"],
[1, 2, 1, 1, np.nan],
]
)
idx = pd.MultiIndex.from_arrays([["cat", "cat", "cat", "dog", "dog"], ["a", "a", "b", "a", "b"], [1, 2, 1, 1, np.nan]])
ts = pd.Series([1.0, 1.1, 1.2, 1.3, 1.4], index=idx)
right = DataFrame(
[[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]],
columns=["cat", "dog"],
)
right = DataFrame([[1.0, 1.3], [1.1, np.nan], [np.nan, 1.4], [1.2, np.nan]], columns=["cat", "dog"])
tpls = [("a", 1), ("a", 2), ("b", np.nan), ("b", 1)]
right.index = pd.MultiIndex.from_tuples(tpls)
tm.assert_frame_equal(ts.unstack(level=0), right)
Expand All @@ -73,11 +51,7 @@ def test_unstack_tuplename_in_multiindex():
ser = pd.Series(1, index=idx)
result = ser.unstack(("A", "a"))

expected = pd.DataFrame(
[[1, 1, 1], [1, 1, 1], [1, 1, 1]],
columns=pd.MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]),
index=pd.Index([1, 2, 3], name=("B", "b")),
)
expected = pd.DataFrame([[1, 1, 1], [1, 1, 1], [1, 1, 1]], columns=pd.MultiIndex.from_tuples([("a",), ("b",), ("c",)], names=[("A", "a")]), index=pd.Index([1, 2, 3], name=("B", "b")))
tm.assert_frame_equal(result, expected)


Expand All @@ -96,12 +70,10 @@ def test_unstack_tuplename_in_multiindex():
(("A", "a"), "B"),
[[1, 1, 1, 1], [1, 1, 1, 1]],
pd.Index([3, 4], name="C"),
pd.MultiIndex.from_tuples(
[("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]
),
),
],
)
pd.MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)], names=[("A", "a"), "B"]))]
)


def test_unstack_mixed_type_name_in_multiindex(
unstack_idx, expected_values, expected_index, expected_columns
):
Expand All @@ -127,9 +99,5 @@ def test_unstack_multi_index_categorical_values():

dti = ser.index.levels[0]
c = pd.Categorical(["foo"] * len(dti))
expected = DataFrame(
{"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
columns=pd.Index(list("ABCD"), name="minor"),
index=dti.rename("major"),
)
expected = DataFrame({"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()}, columns=pd.Index(list("ABCD"), name="minor"), index=dti.rename("major"))
tm.assert_frame_equal(result, expected)
17 changes: 3 additions & 14 deletions pandas/tests/series/test_cumulative.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -38,10 +38,7 @@ def test_cumprod(self, datetime_series):
_check_accum_op("cumprod", datetime_series)

def test_cummin(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummin().values,
np.minimum.accumulate(np.array(datetime_series)),
)
tm.assert_numpy_array_equal(datetime_series.cummin().values, np.minimum.accumulate(np.array(datetime_series)))
ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummin()[1::2]
Expand All @@ -51,10 +48,7 @@ def test_cummin(self, datetime_series):
tm.assert_series_equal(result, expected)

def test_cummax(self, datetime_series):
tm.assert_numpy_array_equal(
datetime_series.cummax().values,
np.maximum.accumulate(np.array(datetime_series)),
)
tm.assert_numpy_array_equal(datetime_series.cummax().values, np.maximum.accumulate(np.array(datetime_series)))
ts = datetime_series.copy()
ts[::2] = np.NaN
result = ts.cummax()[1::2]
Expand Down Expand Up @@ -148,12 +142,7 @@ def test_cummethods_bool(self):
b = ~a
c = pd.Series([False] * len(b))
d = ~c
methods = {
"cumsum": np.cumsum,
"cumprod": np.cumprod,
"cummin": np.minimum.accumulate,
"cummax": np.maximum.accumulate,
}
methods = {"cumsum": np.cumsum, "cumprod": np.cumprod, "cummin": np.minimum.accumulate, "cummax": np.maximum.accumulate}
args = product((a, b, c, d), methods)
for s, method in args:
expected = pd.Series(methods[method](s.values))
Expand Down
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