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import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
Timestamp,
date_range,
to_datetime,
)
import pandas._testing as tm
from pandas.api.indexers import BaseIndexer
from pandas.core.groupby.groupby import get_groupby
class TestRolling:
def setup_method(self):
self.frame = DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
def test_mutated(self):
msg = r"groupby\(\) got an unexpected keyword argument 'foo'"
with pytest.raises(TypeError, match=msg):
self.frame.groupby("A", foo=1)
g = self.frame.groupby("A")
assert not g.mutated
g = get_groupby(self.frame, by="A", mutated=True)
assert g.mutated
def test_getitem(self):
g = self.frame.groupby("A")
g_mutated = get_groupby(self.frame, by="A", mutated=True)
expected = g_mutated.B.apply(lambda x: x.rolling(2).mean())
result = g.rolling(2).mean().B
tm.assert_series_equal(result, expected)
result = g.rolling(2).B.mean()
tm.assert_series_equal(result, expected)
result = g.B.rolling(2).mean()
tm.assert_series_equal(result, expected)
result = self.frame.B.groupby(self.frame.A).rolling(2).mean()
tm.assert_series_equal(result, expected)
def test_getitem_multiple(self):
# GH 13174
g = self.frame.groupby("A")
r = g.rolling(2, min_periods=0)
g_mutated = get_groupby(self.frame, by="A", mutated=True)
expected = g_mutated.B.apply(lambda x: x.rolling(2, min_periods=0).count())
result = r.B.count()
tm.assert_series_equal(result, expected)
result = r.B.count()
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"f",
[
"sum",
"mean",
"min",
"max",
pytest.param(
"count",
marks=pytest.mark.filterwarnings("ignore:min_periods:FutureWarning"),
),
"kurt",
"skew",
],
)
def test_rolling(self, f):
g = self.frame.groupby("A")
r = g.rolling(window=4)
result = getattr(r, f)()
expected = g.apply(lambda x: getattr(x.rolling(4), f)())
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["std", "var"])
def test_rolling_ddof(self, f):
g = self.frame.groupby("A")
r = g.rolling(window=4)
result = getattr(r, f)(ddof=1)
expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_rolling_quantile(self, interpolation):
g = self.frame.groupby("A")
r = g.rolling(window=4)
result = r.quantile(0.4, interpolation=interpolation)
expected = g.apply(
lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation)
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["corr", "cov"])
def test_rolling_corr_cov(self, f):
g = self.frame.groupby("A")
r = g.rolling(window=4)
result = getattr(r, f)(self.frame)
def func(x):
return getattr(x.rolling(4), f)(self.frame)
expected = g.apply(func)
tm.assert_frame_equal(result, expected)
result = getattr(r.B, f)(pairwise=True)
def func(x):
return getattr(x.B.rolling(4), f)(pairwise=True)
expected = g.apply(func)
tm.assert_series_equal(result, expected)
def test_rolling_apply(self, raw):
g = self.frame.groupby("A")
r = g.rolling(window=4)
# reduction
result = r.apply(lambda x: x.sum(), raw=raw)
expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw))
tm.assert_frame_equal(result, expected)
def test_rolling_apply_mutability(self):
# GH 14013
df = DataFrame({"A": ["foo"] * 3 + ["bar"] * 3, "B": [1] * 6})
g = df.groupby("A")
mi = MultiIndex.from_tuples(
[("bar", 3), ("bar", 4), ("bar", 5), ("foo", 0), ("foo", 1), ("foo", 2)]
)
mi.names = ["A", None]
# Grouped column should not be a part of the output
expected = DataFrame([np.nan, 2.0, 2.0] * 2, columns=["B"], index=mi)
result = g.rolling(window=2).sum()
tm.assert_frame_equal(result, expected)
# Call an arbitrary function on the groupby
g.sum()
# Make sure nothing has been mutated
result = g.rolling(window=2).sum()
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("expected_value,raw_value", [[1.0, True], [0.0, False]])
def test_groupby_rolling(self, expected_value, raw_value):
# GH 31754
def foo(x):
return int(isinstance(x, np.ndarray))
df = DataFrame({"id": [1, 1, 1], "value": [1, 2, 3]})
result = df.groupby("id").value.rolling(1).apply(foo, raw=raw_value)
expected = Series(
[expected_value] * 3,
index=MultiIndex.from_tuples(((1, 0), (1, 1), (1, 2)), names=["id", None]),
name="value",
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_center_center(self):
# GH 35552
series = Series(range(1, 6))
result = series.groupby(series).rolling(center=True, window=3).mean()
expected = Series(
[np.nan] * 5,
index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3), (5, 4))),
)
tm.assert_series_equal(result, expected)
series = Series(range(1, 5))
result = series.groupby(series).rolling(center=True, window=3).mean()
expected = Series(
[np.nan] * 4,
index=MultiIndex.from_tuples(((1, 0), (2, 1), (3, 2), (4, 3))),
)
tm.assert_series_equal(result, expected)
df = DataFrame({"a": ["a"] * 5 + ["b"] * 6, "b": range(11)})
result = df.groupby("a").rolling(center=True, window=3).mean()
expected = DataFrame(
[np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, 9, np.nan],
index=MultiIndex.from_tuples(
(
("a", 0),
("a", 1),
("a", 2),
("a", 3),
("a", 4),
("b", 5),
("b", 6),
("b", 7),
("b", 8),
("b", 9),
("b", 10),
),
names=["a", None],
),
columns=["b"],
)
tm.assert_frame_equal(result, expected)
df = DataFrame({"a": ["a"] * 5 + ["b"] * 5, "b": range(10)})
result = df.groupby("a").rolling(center=True, window=3).mean()
expected = DataFrame(
[np.nan, 1, 2, 3, np.nan, np.nan, 6, 7, 8, np.nan],
index=MultiIndex.from_tuples(
(
("a", 0),
("a", 1),
("a", 2),
("a", 3),
("a", 4),
("b", 5),
("b", 6),
("b", 7),
("b", 8),
("b", 9),
),
names=["a", None],
),
columns=["b"],
)
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_center_on(self):
# GH 37141
df = DataFrame(
data={
"Date": date_range("2020-01-01", "2020-01-10"),
"gb": ["group_1"] * 6 + ["group_2"] * 4,
"value": range(10),
}
)
result = (
df.groupby("gb")
.rolling(6, on="Date", center=True, min_periods=1)
.value.mean()
)
expected = Series(
[1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 7.0, 7.5, 7.5, 7.5],
name="value",
index=MultiIndex.from_tuples(
(
("group_1", Timestamp("2020-01-01")),
("group_1", Timestamp("2020-01-02")),
("group_1", Timestamp("2020-01-03")),
("group_1", Timestamp("2020-01-04")),
("group_1", Timestamp("2020-01-05")),
("group_1", Timestamp("2020-01-06")),
("group_2", Timestamp("2020-01-07")),
("group_2", Timestamp("2020-01-08")),
("group_2", Timestamp("2020-01-09")),
("group_2", Timestamp("2020-01-10")),
),
names=["gb", "Date"],
),
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("min_periods", [5, 4, 3])
def test_groupby_rolling_center_min_periods(self, min_periods):
# GH 36040
df = DataFrame({"group": ["A"] * 10 + ["B"] * 10, "data": range(20)})
window_size = 5
result = (
df.groupby("group")
.rolling(window_size, center=True, min_periods=min_periods)
.mean()
)
result = result.reset_index()[["group", "data"]]
grp_A_mean = [1.0, 1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.5, 8.0]
grp_B_mean = [x + 10.0 for x in grp_A_mean]
num_nans = max(0, min_periods - 3) # For window_size of 5
nans = [np.nan] * num_nans
grp_A_expected = nans + grp_A_mean[num_nans : 10 - num_nans] + nans
grp_B_expected = nans + grp_B_mean[num_nans : 10 - num_nans] + nans
expected = DataFrame(
{"group": ["A"] * 10 + ["B"] * 10, "data": grp_A_expected + grp_B_expected}
)
tm.assert_frame_equal(result, expected)
def test_groupby_subselect_rolling(self):
# GH 35486
df = DataFrame(
{"a": [1, 2, 3, 2], "b": [4.0, 2.0, 3.0, 1.0], "c": [10, 20, 30, 20]}
)
result = df.groupby("a")[["b"]].rolling(2).max()
expected = DataFrame(
[np.nan, np.nan, 2.0, np.nan],
columns=["b"],
index=MultiIndex.from_tuples(
((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
),
)
tm.assert_frame_equal(result, expected)
result = df.groupby("a")["b"].rolling(2).max()
expected = Series(
[np.nan, np.nan, 2.0, np.nan],
index=MultiIndex.from_tuples(
((1, 0), (2, 1), (2, 3), (3, 2)), names=["a", None]
),
name="b",
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_custom_indexer(self):
# GH 35557
class SimpleIndexer(BaseIndexer):
def get_window_bounds(
self, num_values=0, min_periods=None, center=None, closed=None
):
min_periods = self.window_size if min_periods is None else 0
end = np.arange(num_values, dtype=np.int64) + 1
start = end.copy() - self.window_size
start[start < 0] = min_periods
return start, end
df = DataFrame(
{"a": [1.0, 2.0, 3.0, 4.0, 5.0] * 3}, index=[0] * 5 + [1] * 5 + [2] * 5
)
result = (
df.groupby(df.index)
.rolling(SimpleIndexer(window_size=3), min_periods=1)
.sum()
)
expected = df.groupby(df.index).rolling(window=3, min_periods=1).sum()
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_subset_with_closed(self):
# GH 35549
df = DataFrame(
{
"column1": range(6),
"column2": range(6),
"group": 3 * ["A", "B"],
"date": [Timestamp("2019-01-01")] * 6,
}
)
result = (
df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum()
)
expected = Series(
[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
index=MultiIndex.from_tuples(
[("A", Timestamp("2019-01-01"))] * 3
+ [("B", Timestamp("2019-01-01"))] * 3,
names=["group", "date"],
),
name="column1",
)
tm.assert_series_equal(result, expected)
def test_groupby_subset_rolling_subset_with_closed(self):
# GH 35549
df = DataFrame(
{
"column1": range(6),
"column2": range(6),
"group": 3 * ["A", "B"],
"date": [Timestamp("2019-01-01")] * 6,
}
)
result = (
df.groupby("group")[["column1", "date"]]
.rolling("1D", on="date", closed="left")["column1"]
.sum()
)
expected = Series(
[np.nan, 0.0, 2.0, np.nan, 1.0, 4.0],
index=MultiIndex.from_tuples(
[("A", Timestamp("2019-01-01"))] * 3
+ [("B", Timestamp("2019-01-01"))] * 3,
names=["group", "date"],
),
name="column1",
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("func", ["max", "min"])
def test_groupby_rolling_index_changed(self, func):
# GH: #36018 nlevels of MultiIndex changed
ds = Series(
[1, 2, 2],
index=MultiIndex.from_tuples(
[("a", "x"), ("a", "y"), ("c", "z")], names=["1", "2"]
),
name="a",
)
result = getattr(ds.groupby(ds).rolling(2), func)()
expected = Series(
[np.nan, np.nan, 2.0],
index=MultiIndex.from_tuples(
[(1, "a", "x"), (2, "a", "y"), (2, "c", "z")], names=["a", "1", "2"]
),
name="a",
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_empty_frame(self):
# GH 36197
expected = DataFrame({"s1": []})
result = expected.groupby("s1").rolling(window=1).sum()
# GH-38057 from_tuples gives empty object dtype, we now get float/int levels
# expected.index = MultiIndex.from_tuples([], names=["s1", None])
expected.index = MultiIndex.from_product(
[Index([], dtype="float64"), Index([], dtype="int64")], names=["s1", None]
)
tm.assert_frame_equal(result, expected)
expected = DataFrame({"s1": [], "s2": []})
result = expected.groupby(["s1", "s2"]).rolling(window=1).sum()
expected.index = MultiIndex.from_product(
[
Index([], dtype="float64"),
Index([], dtype="float64"),
Index([], dtype="int64"),
],
names=["s1", "s2", None],
)
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_string_index(self):
# GH: 36727
df = DataFrame(
[
["A", "group_1", Timestamp(2019, 1, 1, 9)],
["B", "group_1", Timestamp(2019, 1, 2, 9)],
["Z", "group_2", Timestamp(2019, 1, 3, 9)],
["H", "group_1", Timestamp(2019, 1, 6, 9)],
["E", "group_2", Timestamp(2019, 1, 20, 9)],
],
columns=["index", "group", "eventTime"],
).set_index("index")
groups = df.groupby("group")
df["count_to_date"] = groups.cumcount()
rolling_groups = groups.rolling("10d", on="eventTime")
result = rolling_groups.apply(lambda df: df.shape[0])
expected = DataFrame(
[
["A", "group_1", Timestamp(2019, 1, 1, 9), 1.0],
["B", "group_1", Timestamp(2019, 1, 2, 9), 2.0],
["H", "group_1", Timestamp(2019, 1, 6, 9), 3.0],
["Z", "group_2", Timestamp(2019, 1, 3, 9), 1.0],
["E", "group_2", Timestamp(2019, 1, 20, 9), 1.0],
],
columns=["index", "group", "eventTime", "count_to_date"],
).set_index(["group", "index"])
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_no_sort(self):
# GH 36889
result = (
DataFrame({"foo": [2, 1], "bar": [2, 1]})
.groupby("foo", sort=False)
.rolling(1)
.min()
)
expected = DataFrame(
np.array([[2.0, 2.0], [1.0, 1.0]]),
columns=["foo", "bar"],
index=MultiIndex.from_tuples([(2, 0), (1, 1)], names=["foo", None]),
)
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_count_closed_on(self):
# GH 35869
df = DataFrame(
{
"column1": range(6),
"column2": range(6),
"group": 3 * ["A", "B"],
"date": date_range(end="20190101", periods=6),
}
)
result = (
df.groupby("group")
.rolling("3d", on="date", closed="left")["column1"]
.count()
)
expected = Series(
[np.nan, 1.0, 1.0, np.nan, 1.0, 1.0],
name="column1",
index=MultiIndex.from_tuples(
[
("A", Timestamp("2018-12-27")),
("A", Timestamp("2018-12-29")),
("A", Timestamp("2018-12-31")),
("B", Timestamp("2018-12-28")),
("B", Timestamp("2018-12-30")),
("B", Timestamp("2019-01-01")),
],
names=["group", "date"],
),
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
("func", "kwargs"),
[("rolling", {"window": 2, "min_periods": 1}), ("expanding", {})],
)
def test_groupby_rolling_sem(self, func, kwargs):
# GH: 26476
df = DataFrame(
[["a", 1], ["a", 2], ["b", 1], ["b", 2], ["b", 3]], columns=["a", "b"]
)
result = getattr(df.groupby("a"), func)(**kwargs).sem()
expected = DataFrame(
{"a": [np.nan] * 5, "b": [np.nan, 0.70711, np.nan, 0.70711, 0.70711]},
index=MultiIndex.from_tuples(
[("a", 0), ("a", 1), ("b", 2), ("b", 3), ("b", 4)], names=["a", None]
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
("rollings", "key"), [({"on": "a"}, "a"), ({"on": None}, "index")]
)
def test_groupby_rolling_nans_in_index(self, rollings, key):
# GH: 34617
df = DataFrame(
{
"a": to_datetime(["2020-06-01 12:00", "2020-06-01 14:00", np.nan]),
"b": [1, 2, 3],
"c": [1, 1, 1],
}
)
if key == "index":
df = df.set_index("a")
with pytest.raises(ValueError, match=f"{key} must be monotonic"):
df.groupby("c").rolling("60min", **rollings)
def test_groupby_rolling_group_keys(self):
# GH 37641
arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
s = Series([1, 2, 3], index=index)
result = s.groupby(["idx1", "idx2"], group_keys=False).rolling(1).mean()
expected = Series(
[1.0, 2.0, 3.0],
index=MultiIndex.from_tuples(
[("val1", "val1"), ("val1", "val1"), ("val2", "val2")],
names=["idx1", "idx2"],
),
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_index_level_and_column_label(self):
arrays = [["val1", "val1", "val2"], ["val1", "val1", "val2"]]
index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2"))
df = DataFrame({"A": [1, 1, 2], "B": range(3)}, index=index)
result = df.groupby(["idx1", "A"]).rolling(1).mean()
expected = DataFrame(
{"B": [0.0, 1.0, 2.0]},
index=MultiIndex.from_tuples(
[("val1", 1), ("val1", 1), ("val2", 2)], names=["idx1", "A"]
),
)
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_resulting_multiindex(self):
# a few different cases checking the created MultiIndex of the result
# https://github.com/pandas-dev/pandas/pull/38057
# grouping by 1 columns -> 2-level MI as result
df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4})
result = df.groupby("b").rolling(3).mean()
expected_index = MultiIndex.from_tuples(
[(1, 0), (1, 2), (1, 4), (1, 6), (2, 1), (2, 3), (2, 5), (2, 7)],
names=["b", None],
)
tm.assert_index_equal(result.index, expected_index)
# grouping by 2 columns -> 3-level MI as result
df = DataFrame({"a": np.arange(12.0), "b": [1, 2] * 6, "c": [1, 2, 3, 4] * 3})
result = df.groupby(["b", "c"]).rolling(2).sum()
expected_index = MultiIndex.from_tuples(
[
(1, 1, 0),
(1, 1, 4),
(1, 1, 8),
(1, 3, 2),
(1, 3, 6),
(1, 3, 10),
(2, 2, 1),
(2, 2, 5),
(2, 2, 9),
(2, 4, 3),
(2, 4, 7),
(2, 4, 11),
],
names=["b", "c", None],
)
tm.assert_index_equal(result.index, expected_index)
# grouping with 1 level on dataframe with 2-level MI -> 3-level MI as result
df = DataFrame({"a": np.arange(8.0), "b": [1, 2] * 4, "c": [1, 2, 3, 4] * 2})
df = df.set_index("c", append=True)
result = df.groupby("b").rolling(3).mean()
expected_index = MultiIndex.from_tuples(
[
(1, 0, 1),
(1, 2, 3),
(1, 4, 1),
(1, 6, 3),
(2, 1, 2),
(2, 3, 4),
(2, 5, 2),
(2, 7, 4),
],
names=["b", None, "c"],
)
tm.assert_index_equal(result.index, expected_index)
class TestExpanding:
def setup_method(self):
self.frame = DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
@pytest.mark.parametrize(
"f", ["sum", "mean", "min", "max", "count", "kurt", "skew"]
)
def test_expanding(self, f):
g = self.frame.groupby("A")
r = g.expanding()
result = getattr(r, f)()
expected = g.apply(lambda x: getattr(x.expanding(), f)())
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["std", "var"])
def test_expanding_ddof(self, f):
g = self.frame.groupby("A")
r = g.expanding()
result = getattr(r, f)(ddof=0)
expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_expanding_quantile(self, interpolation):
g = self.frame.groupby("A")
r = g.expanding()
result = r.quantile(0.4, interpolation=interpolation)
expected = g.apply(
lambda x: x.expanding().quantile(0.4, interpolation=interpolation)
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["corr", "cov"])
def test_expanding_corr_cov(self, f):
g = self.frame.groupby("A")
r = g.expanding()
result = getattr(r, f)(self.frame)
def func(x):
return getattr(x.expanding(), f)(self.frame)
expected = g.apply(func)
tm.assert_frame_equal(result, expected)
result = getattr(r.B, f)(pairwise=True)
def func(x):
return getattr(x.B.expanding(), f)(pairwise=True)
expected = g.apply(func)
tm.assert_series_equal(result, expected)
def test_expanding_apply(self, raw):
g = self.frame.groupby("A")
r = g.expanding()
# reduction
result = r.apply(lambda x: x.sum(), raw=raw)
expected = g.apply(lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw))
tm.assert_frame_equal(result, expected)
class TestEWM:
@pytest.mark.parametrize(
"method, expected_data",
[
["mean", [0.0, 0.6666666666666666, 1.4285714285714286, 2.2666666666666666]],
["std", [np.nan, 0.707107, 0.963624, 1.177164]],
["var", [np.nan, 0.5, 0.9285714285714286, 1.3857142857142857]],
],
)
def test_methods(self, method, expected_data):
# GH 16037
df = DataFrame({"A": ["a"] * 4, "B": range(4)})
result = getattr(df.groupby("A").ewm(com=1.0), method)()
expected = DataFrame(
{"B": expected_data},
index=MultiIndex.from_tuples(
[
("a", 0),
("a", 1),
("a", 2),
("a", 3),
],
names=["A", None],
),
)
tm.assert_frame_equal(result, expected)
expected = df.groupby("A").apply(lambda x: getattr(x.ewm(com=1.0), method)())
# There may be a bug in the above statement; not returning the correct index
tm.assert_frame_equal(result.reset_index(drop=True), expected)
@pytest.mark.parametrize(
"method, expected_data",
[["corr", [np.nan, 1.0, 1.0, 1]], ["cov", [np.nan, 0.5, 0.928571, 1.385714]]],
)
def test_pairwise_methods(self, method, expected_data):
# GH 16037
df = DataFrame({"A": ["a"] * 4, "B": range(4)})
result = getattr(df.groupby("A").ewm(com=1.0), method)()
expected = DataFrame(
{"B": expected_data},
index=MultiIndex.from_tuples(
[
("a", 0, "B"),
("a", 1, "B"),
("a", 2, "B"),
("a", 3, "B"),
],
names=["A", None, None],
),
)
tm.assert_frame_equal(result, expected)
expected = df.groupby("A").apply(lambda x: getattr(x.ewm(com=1.0), method)())
tm.assert_frame_equal(result, expected)