import numpy as np
import pytest
from pandas import (
DataFrame,
DatetimeIndex,
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
@pytest.fixture
def times_frame():
"""Frame for testing times argument in EWM groupby."""
return DataFrame(
{
"A": ["a", "b", "c", "a", "b", "c", "a", "b", "c", "a"],
"B": [0, 0, 0, 1, 1, 1, 2, 2, 2, 3],
"C": to_datetime(
[
"2020-01-01",
"2020-01-01",
"2020-01-01",
"2020-01-02",
"2020-01-10",
"2020-01-22",
"2020-01-03",
"2020-01-23",
"2020-01-23",
"2020-01-04",
]
),
}
)
@pytest.fixture
def roll_frame():
return DataFrame({"A": [1] * 20 + [2] * 12 + [3] * 8, "B": np.arange(40)})
class TestRolling:
def test_groupby_unsupported_argument(self, roll_frame):
msg = r"groupby\(\) got an unexpected keyword argument 'foo'"
with pytest.raises(TypeError, match=msg):
roll_frame.groupby("A", foo=1)
def test_getitem(self, roll_frame):
g = roll_frame.groupby("A")
g_mutated = get_groupby(roll_frame, by="A")
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 = roll_frame.B.groupby(roll_frame.A).rolling(2).mean()
tm.assert_series_equal(result, expected)
def test_getitem_multiple(self, roll_frame):
# GH 13174
g = roll_frame.groupby("A")
r = g.rolling(2, min_periods=0)
g_mutated = get_groupby(roll_frame, by="A")
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",
"count",
"kurt",
"skew",
],
)
def test_rolling(self, f, roll_frame):
g = roll_frame.groupby("A", group_keys=False)
r = g.rolling(window=4)
result = getattr(r, f)()
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: getattr(x.rolling(4), f)())
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["std", "var"])
def test_rolling_ddof(self, f, roll_frame):
g = roll_frame.groupby("A", group_keys=False)
r = g.rolling(window=4)
result = getattr(r, f)(ddof=1)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: getattr(x.rolling(4), f)(ddof=1))
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_rolling_quantile(self, interpolation, roll_frame):
g = roll_frame.groupby("A", group_keys=False)
r = g.rolling(window=4)
result = r.quantile(0.4, interpolation=interpolation)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(
lambda x: x.rolling(4).quantile(0.4, interpolation=interpolation)
)
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f, expected_val", [["corr", 1], ["cov", 0.5]])
def test_rolling_corr_cov_other_same_size_as_groups(self, f, expected_val):
# GH 42915
df = DataFrame(
{"value": range(10), "idx1": [1] * 5 + [2] * 5, "idx2": [1, 2, 3, 4, 5] * 2}
).set_index(["idx1", "idx2"])
other = DataFrame({"value": range(5), "idx2": [1, 2, 3, 4, 5]}).set_index(
"idx2"
)
result = getattr(df.groupby(level=0).rolling(2), f)(other)
expected_data = ([np.nan] + [expected_val] * 4) * 2
expected = DataFrame(
expected_data,
columns=["value"],
index=MultiIndex.from_arrays(
[
[1] * 5 + [2] * 5,
[1] * 5 + [2] * 5,
list(range(1, 6)) * 2,
],
names=["idx1", "idx1", "idx2"],
),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["corr", "cov"])
def test_rolling_corr_cov_other_diff_size_as_groups(self, f, roll_frame):
g = roll_frame.groupby("A")
r = g.rolling(window=4)
result = getattr(r, f)(roll_frame)
def func(x):
return getattr(x.rolling(4), f)(roll_frame)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(func)
# GH 39591: The grouped column should be all np.nan
# (groupby.apply inserts 0s for cov)
expected["A"] = np.nan
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["corr", "cov"])
def test_rolling_corr_cov_pairwise(self, f, roll_frame):
g = roll_frame.groupby("A")
r = g.rolling(window=4)
result = getattr(r.B, f)(pairwise=True)
def func(x):
return getattr(x.B.rolling(4), f)(pairwise=True)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(func)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"func, expected_values",
[("cov", [[1.0, 1.0], [1.0, 4.0]]), ("corr", [[1.0, 0.5], [0.5, 1.0]])],
)
def test_rolling_corr_cov_unordered(self, func, expected_values):
# GH 43386
df = DataFrame(
{
"a": ["g1", "g2", "g1", "g1"],
"b": [0, 0, 1, 2],
"c": [2, 0, 6, 4],
}
)
rol = df.groupby("a").rolling(3)
result = getattr(rol, func)()
expected = DataFrame(
{
"b": 4 * [np.nan] + expected_values[0] + 2 * [np.nan],
"c": 4 * [np.nan] + expected_values[1] + 2 * [np.nan],
},
index=MultiIndex.from_tuples(
[
("g1", 0, "b"),
("g1", 0, "c"),
("g1", 2, "b"),
("g1", 2, "c"),
("g1", 3, "b"),
("g1", 3, "c"),
("g2", 1, "b"),
("g2", 1, "c"),
],
names=["a", None, None],
),
)
tm.assert_frame_equal(result, expected)
def test_rolling_apply(self, raw, roll_frame):
g = roll_frame.groupby("A", group_keys=False)
r = g.rolling(window=4)
# reduction
result = r.apply(lambda x: x.sum(), raw=raw)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: x.rolling(4).apply(lambda y: y.sum(), raw=raw))
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([roll_frame["A"], range(40)])
expected.index = expected_index
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 isnumpyarray(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(isnumpyarray, 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()
)
mi = MultiIndex.from_arrays([df["gb"], df["Date"]], names=["gb", "Date"])
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=mi,
)
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,
step=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(8),
"column2": range(8),
"group": ["A"] * 4 + ["B"] * 4,
"date": [
Timestamp(date)
for date in ["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02"]
]
* 2,
}
)
result = (
df.groupby("group").rolling("1D", on="date", closed="left")["column1"].sum()
)
expected = Series(
[np.nan, np.nan, 1.0, 1.0, np.nan, np.nan, 9.0, 9.0],
index=MultiIndex.from_frame(
df[["group", "date"]],
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(8),
"column2": range(8),
"group": ["A"] * 4 + ["B"] * 4,
"date": [
Timestamp(date)
for date in ["2019-01-01", "2019-01-01", "2019-01-02", "2019-01-02"]
]
* 2,
}
)
result = (
df.groupby("group")[["column1", "date"]]
.rolling("1D", on="date", closed="left")["column1"]
.sum()
)
expected = Series(
[np.nan, np.nan, 1.0, 1.0, np.nan, np.nan, 9.0, 9.0],
index=MultiIndex.from_frame(
df[["group", "date"]],
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 32262
expected = expected.drop(columns="s1")
# 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()
# GH 32262
expected = expected.drop(columns=["s1", "s2"])
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]),
)
# GH 32262
expected = expected.drop(columns="foo")
tm.assert_frame_equal(result, expected)
def test_groupby_rolling_count_closed_on(self, unit):
# GH 35869
df = DataFrame(
{
"column1": range(6),
"column2": range(6),
"group": 3 * ["A", "B"],
"date": date_range(end="20190101", periods=6, unit=unit),
}
)
result = (
df.groupby("group")
.rolling("3d", on="date", closed="left")["column1"]
.count()
)
dti = DatetimeIndex(
[
"2018-12-27",
"2018-12-29",
"2018-12-31",
"2018-12-28",
"2018-12-30",
"2019-01-01",
],
dtype=f"M8[{unit}]",
)
mi = MultiIndex.from_arrays(
[
["A", "A", "A", "B", "B", "B"],
dti,
],
names=["group", "date"],
)
expected = Series(
[np.nan, 1.0, 1.0, np.nan, 1.0, 1.0],
name="column1",
index=mi,
)
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]
),
)
# GH 32262
expected = expected.drop(columns="a")
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} values must not have NaT"):
df.groupby("c").rolling("60min", **rollings)
@pytest.mark.parametrize("group_keys", [True, False])
def test_groupby_rolling_group_keys(self, group_keys):
# GH 37641
# GH 38523: GH 37641 actually was not a bug.
# group_keys only applies to groupby.apply directly
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=group_keys).rolling(1).mean()
expected = Series(
[1.0, 2.0, 3.0],
index=MultiIndex.from_tuples(
[
("val1", "val1", "val1", "val1"),
("val1", "val1", "val1", "val1"),
("val2", "val2", "val2", "val2"),
],
names=["idx1", "idx2", "idx1", "idx2"],
),
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_index_level_and_column_label(self):
# The groupby keys should not appear as a resulting column
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", "val1"),
("val1", 1, "val1", "val1"),
("val2", 2, "val2", "val2"),
],
names=["idx1", "A", "idx1", "idx2"],
),
)
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)
def test_groupby_rolling_resulting_multiindex2(self):
# 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)
def test_groupby_rolling_resulting_multiindex3(self):
# 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, exact="equiv")
def test_groupby_rolling_object_doesnt_affect_groupby_apply(self, roll_frame):
# GH 39732
g = roll_frame.groupby("A", group_keys=False)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: x.rolling(4).sum()).index
_ = g.rolling(window=4)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
result = g.apply(lambda x: x.rolling(4).sum()).index
tm.assert_index_equal(result, expected)
@pytest.mark.parametrize(
("window", "min_periods", "closed", "expected"),
[
(2, 0, "left", [None, 0.0, 1.0, 1.0, None, 0.0, 1.0, 1.0]),
(2, 2, "left", [None, None, 1.0, 1.0, None, None, 1.0, 1.0]),
(4, 4, "left", [None, None, None, None, None, None, None, None]),
(4, 4, "right", [None, None, None, 5.0, None, None, None, 5.0]),
],
)
def test_groupby_rolling_var(self, window, min_periods, closed, expected):
df = DataFrame([1, 2, 3, 4, 5, 6, 7, 8])
result = (
df.groupby([1, 2, 1, 2, 1, 2, 1, 2])
.rolling(window=window, min_periods=min_periods, closed=closed)
.var(0)
)
expected_result = DataFrame(
np.array(expected, dtype="float64"),
index=MultiIndex(
levels=[np.array([1, 2]), [0, 1, 2, 3, 4, 5, 6, 7]],
codes=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 2, 4, 6, 1, 3, 5, 7]],
),
)
tm.assert_frame_equal(result, expected_result)
@pytest.mark.parametrize(
"columns", [MultiIndex.from_tuples([("A", ""), ("B", "C")]), ["A", "B"]]
)
def test_by_column_not_in_values(self, columns):
# GH 32262
df = DataFrame([[1, 0]] * 20 + [[2, 0]] * 12 + [[3, 0]] * 8, columns=columns)
g = df.groupby("A")
original_obj = g.obj.copy(deep=True)
r = g.rolling(4)
result = r.sum()
assert "A" not in result.columns
tm.assert_frame_equal(g.obj, original_obj)
def test_groupby_level(self):
# GH 38523, 38787
arrays = [
["Falcon", "Falcon", "Parrot", "Parrot"],
["Captive", "Wild", "Captive", "Wild"],
]
index = MultiIndex.from_arrays(arrays, names=("Animal", "Type"))
df = DataFrame({"Max Speed": [390.0, 350.0, 30.0, 20.0]}, index=index)
result = df.groupby(level=0)["Max Speed"].rolling(2).sum()
expected = Series(
[np.nan, 740.0, np.nan, 50.0],
index=MultiIndex.from_tuples(
[
("Falcon", "Falcon", "Captive"),
("Falcon", "Falcon", "Wild"),
("Parrot", "Parrot", "Captive"),
("Parrot", "Parrot", "Wild"),
],
names=["Animal", "Animal", "Type"],
),
name="Max Speed",
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"by, expected_data",
[
[["id"], {"num": [100.0, 150.0, 150.0, 200.0]}],
[
["id", "index"],
{
"date": [
Timestamp("2018-01-01"),
Timestamp("2018-01-02"),
Timestamp("2018-01-01"),
Timestamp("2018-01-02"),
],
"num": [100.0, 200.0, 150.0, 250.0],
},
],
],
)
def test_as_index_false(self, by, expected_data, unit):
# GH 39433
data = [
["A", "2018-01-01", 100.0],
["A", "2018-01-02", 200.0],
["B", "2018-01-01", 150.0],
["B", "2018-01-02", 250.0],
]
df = DataFrame(data, columns=["id", "date", "num"])
df["date"] = df["date"].astype(f"M8[{unit}]")
df = df.set_index(["date"])
gp_by = [getattr(df, attr) for attr in by]
result = (
df.groupby(gp_by, as_index=False).rolling(window=2, min_periods=1).mean()
)
expected = {"id": ["A", "A", "B", "B"]}
expected.update(expected_data)
expected = DataFrame(
expected,
index=df.index,
)
if "date" in expected_data:
expected["date"] = expected["date"].astype(f"M8[{unit}]")
tm.assert_frame_equal(result, expected)
def test_nan_and_zero_endpoints(self, any_int_numpy_dtype):
# https://github.com/twosigma/pandas/issues/53
typ = np.dtype(any_int_numpy_dtype).type
size = 1000
idx = np.repeat(typ(0), size)
idx[-1] = 1
val = 5e25
arr = np.repeat(val, size)
arr[0] = np.nan
arr[-1] = 0
df = DataFrame(
{
"index": idx,
"adl2": arr,
}
).set_index("index")
result = df.groupby("index")["adl2"].rolling(window=10, min_periods=1).mean()
expected = Series(
arr,
name="adl2",
index=MultiIndex.from_arrays(
[
Index([0] * 999 + [1], dtype=typ, name="index"),
Index([0] * 999 + [1], dtype=typ, name="index"),
],
),
)
tm.assert_series_equal(result, expected)
def test_groupby_rolling_non_monotonic(self):
# GH 43909
shuffled = [3, 0, 1, 2]
sec = 1_000
df = DataFrame(
[{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled]
)
with pytest.raises(ValueError, match=r".* must be monotonic"):
df.groupby("c").rolling(on="t", window="3s")
def test_groupby_monotonic(self):
# GH 15130
# we don't need to validate monotonicity when grouping
# GH 43909 we should raise an error here to match
# behaviour of non-groupby rolling.
data = [
["David", "1/1/2015", 100],
["David", "1/5/2015", 500],
["David", "5/30/2015", 50],
["David", "7/25/2015", 50],
["Ryan", "1/4/2014", 100],
["Ryan", "1/19/2015", 500],
["Ryan", "3/31/2016", 50],
["Joe", "7/1/2015", 100],
["Joe", "9/9/2015", 500],
["Joe", "10/15/2015", 50],
]
df = DataFrame(data=data, columns=["name", "date", "amount"])
df["date"] = to_datetime(df["date"])
df = df.sort_values("date")
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = (
df.set_index("date")
.groupby("name")
.apply(lambda x: x.rolling("180D")["amount"].sum())
)
result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
tm.assert_series_equal(result, expected)
def test_datelike_on_monotonic_within_each_group(self):
# GH 13966 (similar to #15130, closed by #15175)
# superseded by 43909
# GH 46061: OK if the on is monotonic relative to each each group
dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
df = DataFrame(
{
"A": [1] * 20 + [2] * 12 + [3] * 8,
"B": np.concatenate((dates, dates)),
"C": np.arange(40),
}
)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = (
df.set_index("B")
.groupby("A")
.apply(lambda x: x.rolling("4s")["C"].mean())
)
result = df.groupby("A").rolling("4s", on="B").C.mean()
tm.assert_series_equal(result, expected)
def test_datelike_on_not_monotonic_within_each_group(self):
# GH 46061
df = DataFrame(
{
"A": [1] * 3 + [2] * 3,
"B": [Timestamp(year, 1, 1) for year in [2020, 2021, 2019]] * 2,
"C": range(6),
}
)
with pytest.raises(ValueError, match="Each group within B must be monotonic."):
df.groupby("A").rolling("365D", on="B")
class TestExpanding:
@pytest.fixture
def frame(self):
return 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, frame):
g = frame.groupby("A", group_keys=False)
r = g.expanding()
result = getattr(r, f)()
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: getattr(x.expanding(), f)())
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["std", "var"])
def test_expanding_ddof(self, f, frame):
g = frame.groupby("A", group_keys=False)
r = g.expanding()
result = getattr(r, f)(ddof=0)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(lambda x: getattr(x.expanding(), f)(ddof=0))
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"interpolation", ["linear", "lower", "higher", "midpoint", "nearest"]
)
def test_expanding_quantile(self, interpolation, frame):
g = frame.groupby("A", group_keys=False)
r = g.expanding()
result = r.quantile(0.4, interpolation=interpolation)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(
lambda x: x.expanding().quantile(0.4, interpolation=interpolation)
)
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
expected.index = expected_index
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("f", ["corr", "cov"])
def test_expanding_corr_cov(self, f, frame):
g = frame.groupby("A")
r = g.expanding()
result = getattr(r, f)(frame)
def func_0(x):
return getattr(x.expanding(), f)(frame)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(func_0)
# GH 39591: groupby.apply returns 1 instead of nan for windows
# with all nan values
null_idx = list(range(20, 61)) + list(range(72, 113))
expected.iloc[null_idx, 1] = np.nan
# GH 39591: The grouped column should be all np.nan
# (groupby.apply inserts 0s for cov)
expected["A"] = np.nan
tm.assert_frame_equal(result, expected)
result = getattr(r.B, f)(pairwise=True)
def func_1(x):
return getattr(x.B.expanding(), f)(pairwise=True)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(func_1)
tm.assert_series_equal(result, expected)
def test_expanding_apply(self, raw, frame):
g = frame.groupby("A", group_keys=False)
r = g.expanding()
# reduction
result = r.apply(lambda x: x.sum(), raw=raw)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
expected = g.apply(
lambda x: x.expanding().apply(lambda y: y.sum(), raw=raw)
)
# groupby.apply doesn't drop the grouped-by column
expected = expected.drop("A", axis=1)
# GH 39732
expected_index = MultiIndex.from_arrays([frame["A"], range(40)])
expected.index = expected_index
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)
@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")[["B"]].apply(
lambda x: getattr(x.ewm(com=1.0), method)()
)
tm.assert_frame_equal(result, expected)
def test_times(self, times_frame):
# GH 40951
halflife = "23 days"
# GH#42738
times = times_frame.pop("C")
result = times_frame.groupby("A").ewm(halflife=halflife, times=times).mean()
expected = DataFrame(
{
"B": [
0.0,
0.507534,
1.020088,
1.537661,
0.0,
0.567395,
1.221209,
0.0,
0.653141,
1.195003,
]
},
index=MultiIndex.from_tuples(
[
("a", 0),
("a", 3),
("a", 6),
("a", 9),
("b", 1),
("b", 4),
("b", 7),
("c", 2),
("c", 5),
("c", 8),
],
names=["A", None],
),
)
tm.assert_frame_equal(result, expected)
def test_times_array(self, times_frame):
# GH 40951
halflife = "23 days"
times = times_frame.pop("C")
gb = times_frame.groupby("A")
result = gb.ewm(halflife=halflife, times=times).mean()
expected = gb.ewm(halflife=halflife, times=times.values).mean()
tm.assert_frame_equal(result, expected)
def test_dont_mutate_obj_after_slicing(self):
# GH 43355
df = DataFrame(
{
"id": ["a", "a", "b", "b", "b"],
"timestamp": date_range("2021-9-1", periods=5, freq="h"),
"y": range(5),
}
)
grp = df.groupby("id").rolling("1h", on="timestamp")
result = grp.count()
expected_df = DataFrame(
{
"timestamp": date_range("2021-9-1", periods=5, freq="h"),
"y": [1.0] * 5,
},
index=MultiIndex.from_arrays(
[["a", "a", "b", "b", "b"], list(range(5))], names=["id", None]
),
)
tm.assert_frame_equal(result, expected_df)
result = grp["y"].count()
expected_series = Series(
[1.0] * 5,
index=MultiIndex.from_arrays(
[
["a", "a", "b", "b", "b"],
date_range("2021-9-1", periods=5, freq="h"),
],
names=["id", "timestamp"],
),
name="y",
)
tm.assert_series_equal(result, expected_series)
# This is the key test
result = grp.count()
tm.assert_frame_equal(result, expected_df)
def test_rolling_corr_with_single_integer_in_index():
# GH 44078
df = DataFrame({"a": [(1,), (1,), (1,)], "b": [4, 5, 6]})
gb = df.groupby(["a"])
result = gb.rolling(2).corr(other=df)
index = MultiIndex.from_tuples([((1,), 0), ((1,), 1), ((1,), 2)], names=["a", None])
expected = DataFrame(
{"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index
)
tm.assert_frame_equal(result, expected)
def test_rolling_corr_with_tuples_in_index():
# GH 44078
df = DataFrame(
{
"a": [
(
1,
2,
),
(
1,
2,
),
(
1,
2,
),
],
"b": [4, 5, 6],
}
)
gb = df.groupby(["a"])
result = gb.rolling(2).corr(other=df)
index = MultiIndex.from_tuples(
[((1, 2), 0), ((1, 2), 1), ((1, 2), 2)], names=["a", None]
)
expected = DataFrame(
{"a": [np.nan, np.nan, np.nan], "b": [np.nan, 1.0, 1.0]}, index=index
)
tm.assert_frame_equal(result, expected)