Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
698 lines
21 KiB
698 lines
21 KiB
from datetime import datetime, timedelta
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.errors import UnsupportedFunctionCall
|
|
import pandas.util._test_decorators as td
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Series, date_range
|
|
import pandas._testing as tm
|
|
from pandas.core.window import Rolling
|
|
|
|
|
|
def test_doc_string():
|
|
|
|
df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
|
|
df
|
|
df.rolling(2).sum()
|
|
df.rolling(2, min_periods=1).sum()
|
|
|
|
|
|
def test_constructor(which):
|
|
# GH 12669
|
|
|
|
c = which.rolling
|
|
|
|
# valid
|
|
c(0)
|
|
c(window=2)
|
|
c(window=2, min_periods=1)
|
|
c(window=2, min_periods=1, center=True)
|
|
c(window=2, min_periods=1, center=False)
|
|
|
|
# GH 13383
|
|
|
|
msg = "window must be non-negative"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(-1)
|
|
|
|
# not valid
|
|
for w in [2.0, "foo", np.array([2])]:
|
|
msg = (
|
|
"window must be an integer|"
|
|
"passed window foo is not compatible with a datetimelike index"
|
|
)
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(window=w)
|
|
|
|
msg = "min_periods must be an integer"
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(window=2, min_periods=w)
|
|
|
|
msg = "center must be a boolean"
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(window=2, min_periods=1, center=w)
|
|
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_constructor_with_win_type(which):
|
|
# GH 13383
|
|
c = which.rolling
|
|
|
|
msg = "window must be > 0"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(-1, win_type="boxcar")
|
|
|
|
|
|
@pytest.mark.parametrize("window", [timedelta(days=3), pd.Timedelta(days=3)])
|
|
def test_constructor_with_timedelta_window(window):
|
|
# GH 15440
|
|
n = 10
|
|
df = DataFrame(
|
|
{"value": np.arange(n)}, index=pd.date_range("2015-12-24", periods=n, freq="D"),
|
|
)
|
|
expected_data = np.append([0.0, 1.0], np.arange(3.0, 27.0, 3))
|
|
|
|
result = df.rolling(window=window).sum()
|
|
expected = DataFrame(
|
|
{"value": expected_data},
|
|
index=pd.date_range("2015-12-24", periods=n, freq="D"),
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
expected = df.rolling("3D").sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("window", [timedelta(days=3), pd.Timedelta(days=3), "3D"])
|
|
def test_constructor_timedelta_window_and_minperiods(window, raw):
|
|
# GH 15305
|
|
n = 10
|
|
df = DataFrame(
|
|
{"value": np.arange(n)}, index=pd.date_range("2017-08-08", periods=n, freq="D"),
|
|
)
|
|
expected = DataFrame(
|
|
{"value": np.append([np.NaN, 1.0], np.arange(3.0, 27.0, 3))},
|
|
index=pd.date_range("2017-08-08", periods=n, freq="D"),
|
|
)
|
|
result_roll_sum = df.rolling(window=window, min_periods=2).sum()
|
|
result_roll_generic = df.rolling(window=window, min_periods=2).apply(sum, raw=raw)
|
|
tm.assert_frame_equal(result_roll_sum, expected)
|
|
tm.assert_frame_equal(result_roll_generic, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["std", "mean", "sum", "max", "min", "var"])
|
|
def test_numpy_compat(method):
|
|
# see gh-12811
|
|
r = Rolling(Series([2, 4, 6]), window=2)
|
|
|
|
msg = "numpy operations are not valid with window objects"
|
|
|
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
|
getattr(r, method)(1, 2, 3)
|
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
|
getattr(r, method)(dtype=np.float64)
|
|
|
|
|
|
def test_closed():
|
|
df = DataFrame({"A": [0, 1, 2, 3, 4]})
|
|
# closed only allowed for datetimelike
|
|
|
|
msg = "closed only implemented for datetimelike and offset based windows"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
df.rolling(window=3, closed="neither")
|
|
|
|
|
|
@pytest.mark.parametrize("closed", ["neither", "left"])
|
|
def test_closed_empty(closed, arithmetic_win_operators):
|
|
# GH 26005
|
|
func_name = arithmetic_win_operators
|
|
ser = pd.Series(
|
|
data=np.arange(5), index=pd.date_range("2000", periods=5, freq="2D")
|
|
)
|
|
roll = ser.rolling("1D", closed=closed)
|
|
|
|
result = getattr(roll, func_name)()
|
|
expected = pd.Series([np.nan] * 5, index=ser.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("func", ["min", "max"])
|
|
def test_closed_one_entry(func):
|
|
# GH24718
|
|
ser = pd.Series(data=[2], index=pd.date_range("2000", periods=1))
|
|
result = getattr(ser.rolling("10D", closed="left"), func)()
|
|
tm.assert_series_equal(result, pd.Series([np.nan], index=ser.index))
|
|
|
|
|
|
@pytest.mark.parametrize("func", ["min", "max"])
|
|
def test_closed_one_entry_groupby(func):
|
|
# GH24718
|
|
ser = pd.DataFrame(
|
|
data={"A": [1, 1, 2], "B": [3, 2, 1]}, index=pd.date_range("2000", periods=3),
|
|
)
|
|
result = getattr(
|
|
ser.groupby("A", sort=False)["B"].rolling("10D", closed="left"), func
|
|
)()
|
|
exp_idx = pd.MultiIndex.from_arrays(
|
|
arrays=[[1, 1, 2], ser.index], names=("A", None)
|
|
)
|
|
expected = pd.Series(data=[np.nan, 3, np.nan], index=exp_idx, name="B")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("input_dtype", ["int", "float"])
|
|
@pytest.mark.parametrize(
|
|
"func,closed,expected",
|
|
[
|
|
("min", "right", [0.0, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
|
|
("min", "both", [0.0, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
|
|
("min", "neither", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
|
|
("min", "left", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
|
|
("max", "right", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
|
|
("max", "both", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
|
|
("max", "neither", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
|
|
("max", "left", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
|
|
],
|
|
)
|
|
def test_closed_min_max_datetime(input_dtype, func, closed, expected):
|
|
# see gh-21704
|
|
ser = pd.Series(
|
|
data=np.arange(10).astype(input_dtype), index=pd.date_range("2000", periods=10),
|
|
)
|
|
|
|
result = getattr(ser.rolling("3D", closed=closed), func)()
|
|
expected = pd.Series(expected, index=ser.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_closed_uneven():
|
|
# see gh-21704
|
|
ser = pd.Series(data=np.arange(10), index=pd.date_range("2000", periods=10))
|
|
|
|
# uneven
|
|
ser = ser.drop(index=ser.index[[1, 5]])
|
|
result = ser.rolling("3D", closed="left").min()
|
|
expected = pd.Series([np.nan, 0, 0, 2, 3, 4, 6, 6], index=ser.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"func,closed,expected",
|
|
[
|
|
("min", "right", [np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
|
|
("min", "both", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, np.nan]),
|
|
("min", "neither", [np.nan, np.nan, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
|
|
("min", "left", [np.nan, np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan]),
|
|
("max", "right", [np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan, np.nan]),
|
|
("max", "both", [np.nan, 1, 2, 3, 4, 5, 6, 6, 6, np.nan]),
|
|
("max", "neither", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, np.nan, np.nan]),
|
|
("max", "left", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan]),
|
|
],
|
|
)
|
|
def test_closed_min_max_minp(func, closed, expected):
|
|
# see gh-21704
|
|
ser = pd.Series(data=np.arange(10), index=pd.date_range("2000", periods=10))
|
|
ser[ser.index[-3:]] = np.nan
|
|
result = getattr(ser.rolling("3D", min_periods=2, closed=closed), func)()
|
|
expected = pd.Series(expected, index=ser.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"closed,expected",
|
|
[
|
|
("right", [0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8]),
|
|
("both", [0, 0.5, 1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
|
|
("neither", [np.nan, 0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
|
|
("left", [np.nan, 0, 0.5, 1, 2, 3, 4, 5, 6, 7]),
|
|
],
|
|
)
|
|
def test_closed_median_quantile(closed, expected):
|
|
# GH 26005
|
|
ser = pd.Series(data=np.arange(10), index=pd.date_range("2000", periods=10))
|
|
roll = ser.rolling("3D", closed=closed)
|
|
expected = pd.Series(expected, index=ser.index)
|
|
|
|
result = roll.median()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = roll.quantile(0.5)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("roller", ["1s", 1])
|
|
def tests_empty_df_rolling(roller):
|
|
# GH 15819 Verifies that datetime and integer rolling windows can be
|
|
# applied to empty DataFrames
|
|
expected = DataFrame()
|
|
result = DataFrame().rolling(roller).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# Verifies that datetime and integer rolling windows can be applied to
|
|
# empty DataFrames with datetime index
|
|
expected = DataFrame(index=pd.DatetimeIndex([]))
|
|
result = DataFrame(index=pd.DatetimeIndex([])).rolling(roller).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_empty_window_median_quantile():
|
|
# GH 26005
|
|
expected = pd.Series([np.nan, np.nan, np.nan])
|
|
roll = pd.Series(np.arange(3)).rolling(0)
|
|
|
|
result = roll.median()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = roll.quantile(0.1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_missing_minp_zero():
|
|
# https://github.com/pandas-dev/pandas/pull/18921
|
|
# minp=0
|
|
x = pd.Series([np.nan])
|
|
result = x.rolling(1, min_periods=0).sum()
|
|
expected = pd.Series([0.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# minp=1
|
|
result = x.rolling(1, min_periods=1).sum()
|
|
expected = pd.Series([np.nan])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_missing_minp_zero_variable():
|
|
# https://github.com/pandas-dev/pandas/pull/18921
|
|
x = pd.Series(
|
|
[np.nan] * 4,
|
|
index=pd.DatetimeIndex(
|
|
["2017-01-01", "2017-01-04", "2017-01-06", "2017-01-07"]
|
|
),
|
|
)
|
|
result = x.rolling(pd.Timedelta("2d"), min_periods=0).sum()
|
|
expected = pd.Series(0.0, index=x.index)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_multi_index_names():
|
|
|
|
# GH 16789, 16825
|
|
cols = pd.MultiIndex.from_product([["A", "B"], ["C", "D", "E"]], names=["1", "2"])
|
|
df = DataFrame(np.ones((10, 6)), columns=cols)
|
|
result = df.rolling(3).cov()
|
|
|
|
tm.assert_index_equal(result.columns, df.columns)
|
|
assert result.index.names == [None, "1", "2"]
|
|
|
|
|
|
def test_rolling_axis_sum(axis_frame):
|
|
# see gh-23372.
|
|
df = DataFrame(np.ones((10, 20)))
|
|
axis = df._get_axis_number(axis_frame)
|
|
|
|
if axis == 0:
|
|
expected = DataFrame({i: [np.nan] * 2 + [3.0] * 8 for i in range(20)})
|
|
else:
|
|
# axis == 1
|
|
expected = DataFrame([[np.nan] * 2 + [3.0] * 18] * 10)
|
|
|
|
result = df.rolling(3, axis=axis_frame).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_axis_count(axis_frame):
|
|
# see gh-26055
|
|
df = DataFrame({"x": range(3), "y": range(3)})
|
|
|
|
axis = df._get_axis_number(axis_frame)
|
|
|
|
if axis in [0, "index"]:
|
|
expected = DataFrame({"x": [1.0, 2.0, 2.0], "y": [1.0, 2.0, 2.0]})
|
|
else:
|
|
expected = DataFrame({"x": [1.0, 1.0, 1.0], "y": [2.0, 2.0, 2.0]})
|
|
|
|
result = df.rolling(2, axis=axis_frame, min_periods=0).count()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_readonly_array():
|
|
# GH-27766
|
|
arr = np.array([1, 3, np.nan, 3, 5])
|
|
arr.setflags(write=False)
|
|
result = pd.Series(arr).rolling(2).mean()
|
|
expected = pd.Series([np.nan, 2, np.nan, np.nan, 4])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_rolling_datetime(axis_frame, tz_naive_fixture):
|
|
# GH-28192
|
|
tz = tz_naive_fixture
|
|
df = pd.DataFrame(
|
|
{i: [1] * 2 for i in pd.date_range("2019-8-01", "2019-08-03", freq="D", tz=tz)}
|
|
)
|
|
if axis_frame in [0, "index"]:
|
|
result = df.T.rolling("2D", axis=axis_frame).sum().T
|
|
else:
|
|
result = df.rolling("2D", axis=axis_frame).sum()
|
|
expected = pd.DataFrame(
|
|
{
|
|
**{
|
|
i: [1.0] * 2
|
|
for i in pd.date_range("2019-8-01", periods=1, freq="D", tz=tz)
|
|
},
|
|
**{
|
|
i: [2.0] * 2
|
|
for i in pd.date_range("2019-8-02", "2019-8-03", freq="D", tz=tz)
|
|
},
|
|
}
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_rolling_window_as_string():
|
|
# see gh-22590
|
|
date_today = datetime.now()
|
|
days = pd.date_range(date_today, date_today + timedelta(365), freq="D")
|
|
|
|
npr = np.random.RandomState(seed=421)
|
|
|
|
data = npr.randint(1, high=100, size=len(days))
|
|
df = DataFrame({"DateCol": days, "metric": data})
|
|
|
|
df.set_index("DateCol", inplace=True)
|
|
result = df.rolling(window="21D", min_periods=2, closed="left")["metric"].agg("max")
|
|
|
|
expData = (
|
|
[np.nan] * 2
|
|
+ [88.0] * 16
|
|
+ [97.0] * 9
|
|
+ [98.0]
|
|
+ [99.0] * 21
|
|
+ [95.0] * 16
|
|
+ [93.0] * 5
|
|
+ [89.0] * 5
|
|
+ [96.0] * 21
|
|
+ [94.0] * 14
|
|
+ [90.0] * 13
|
|
+ [88.0] * 2
|
|
+ [90.0] * 9
|
|
+ [96.0] * 21
|
|
+ [95.0] * 6
|
|
+ [91.0]
|
|
+ [87.0] * 6
|
|
+ [92.0] * 21
|
|
+ [83.0] * 2
|
|
+ [86.0] * 10
|
|
+ [87.0] * 5
|
|
+ [98.0] * 21
|
|
+ [97.0] * 14
|
|
+ [93.0] * 7
|
|
+ [87.0] * 4
|
|
+ [86.0] * 4
|
|
+ [95.0] * 21
|
|
+ [85.0] * 14
|
|
+ [83.0] * 2
|
|
+ [76.0] * 5
|
|
+ [81.0] * 2
|
|
+ [98.0] * 21
|
|
+ [95.0] * 14
|
|
+ [91.0] * 7
|
|
+ [86.0]
|
|
+ [93.0] * 3
|
|
+ [95.0] * 20
|
|
)
|
|
|
|
expected = Series(
|
|
expData, index=days.rename("DateCol")._with_freq(None), name="metric"
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_min_periods1():
|
|
# GH#6795
|
|
df = pd.DataFrame([0, 1, 2, 1, 0], columns=["a"])
|
|
result = df["a"].rolling(3, center=True, min_periods=1).max()
|
|
expected = pd.Series([1.0, 2.0, 2.0, 2.0, 1.0], name="a")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("constructor", [Series, DataFrame])
|
|
def test_rolling_count_with_min_periods(constructor):
|
|
# GH 26996
|
|
result = constructor(range(5)).rolling(3, min_periods=3).count()
|
|
expected = constructor([np.nan, np.nan, 3.0, 3.0, 3.0])
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("constructor", [Series, DataFrame])
|
|
def test_rolling_count_default_min_periods_with_null_values(constructor):
|
|
# GH 26996
|
|
values = [1, 2, 3, np.nan, 4, 5, 6]
|
|
expected_counts = [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 3.0]
|
|
|
|
result = constructor(values).rolling(3).count()
|
|
expected = constructor(expected_counts)
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"df,expected,window,min_periods",
|
|
[
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
3,
|
|
None,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
2,
|
|
1,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
2,
|
|
3,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
1,
|
|
1,
|
|
),
|
|
(
|
|
DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
1,
|
|
2,
|
|
),
|
|
(DataFrame({"A": [1], "B": [4]}), [], 2, None),
|
|
(DataFrame({"A": [1], "B": [4]}), [], 2, 1),
|
|
(DataFrame(), [({}, [])], 2, None),
|
|
(
|
|
DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
|
|
[
|
|
({"A": [1.0], "B": [np.nan]}, [0]),
|
|
({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
|
|
({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
3,
|
|
2,
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_dataframe(df, expected, window, min_periods):
|
|
# GH 11704
|
|
expected = [DataFrame(values, index=index) for (values, index) in expected]
|
|
|
|
for (expected, actual) in zip(
|
|
expected, df.rolling(window, min_periods=min_periods)
|
|
):
|
|
tm.assert_frame_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"expected,window",
|
|
[
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [2, 3], "B": [5, 6]}, [1, 2]),
|
|
],
|
|
"2D",
|
|
),
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [1, 2], "B": [4, 5]}, [0, 1]),
|
|
({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
|
|
],
|
|
"3D",
|
|
),
|
|
(
|
|
[
|
|
({"A": [1], "B": [4]}, [0]),
|
|
({"A": [2], "B": [5]}, [1]),
|
|
({"A": [3], "B": [6]}, [2]),
|
|
],
|
|
"1D",
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_on_dataframe(expected, window):
|
|
# GH 11704
|
|
df = DataFrame(
|
|
{
|
|
"A": [1, 2, 3, 4, 5],
|
|
"B": [4, 5, 6, 7, 8],
|
|
"C": date_range(start="2016-01-01", periods=5, freq="D"),
|
|
}
|
|
)
|
|
|
|
expected = [DataFrame(values, index=index) for (values, index) in expected]
|
|
for (expected, actual) in zip(expected, df.rolling(window, on="C")):
|
|
tm.assert_frame_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ser,expected,window, min_periods",
|
|
[
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
|
|
3,
|
|
None,
|
|
),
|
|
(
|
|
Series([1, 2, 3]),
|
|
[([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
|
|
3,
|
|
1,
|
|
),
|
|
(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])], 2, 1),
|
|
(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])], 2, 3),
|
|
(Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 0),
|
|
(Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 2),
|
|
(Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2, 0),
|
|
(Series([], dtype="int64"), [], 2, 1),
|
|
],
|
|
)
|
|
def test_iter_rolling_series(ser, expected, window, min_periods):
|
|
# GH 11704
|
|
expected = [Series(values, index=index) for (values, index) in expected]
|
|
|
|
for (expected, actual) in zip(
|
|
expected, ser.rolling(window, min_periods=min_periods)
|
|
):
|
|
tm.assert_series_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"expected,expected_index,window",
|
|
[
|
|
(
|
|
[[0], [1], [2], [3], [4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-02", periods=1, freq="D"),
|
|
date_range("2020-01-03", periods=1, freq="D"),
|
|
date_range("2020-01-04", periods=1, freq="D"),
|
|
date_range("2020-01-05", periods=1, freq="D"),
|
|
],
|
|
"1D",
|
|
),
|
|
(
|
|
[[0], [0, 1], [1, 2], [2, 3], [3, 4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-01", periods=2, freq="D"),
|
|
date_range("2020-01-02", periods=2, freq="D"),
|
|
date_range("2020-01-03", periods=2, freq="D"),
|
|
date_range("2020-01-04", periods=2, freq="D"),
|
|
],
|
|
"2D",
|
|
),
|
|
(
|
|
[[0], [0, 1], [0, 1, 2], [1, 2, 3], [2, 3, 4]],
|
|
[
|
|
date_range("2020-01-01", periods=1, freq="D"),
|
|
date_range("2020-01-01", periods=2, freq="D"),
|
|
date_range("2020-01-01", periods=3, freq="D"),
|
|
date_range("2020-01-02", periods=3, freq="D"),
|
|
date_range("2020-01-03", periods=3, freq="D"),
|
|
],
|
|
"3D",
|
|
),
|
|
],
|
|
)
|
|
def test_iter_rolling_datetime(expected, expected_index, window):
|
|
# GH 11704
|
|
ser = Series(range(5), index=date_range(start="2020-01-01", periods=5, freq="D"))
|
|
|
|
expected = [
|
|
Series(values, index=idx) for (values, idx) in zip(expected, expected_index)
|
|
]
|
|
|
|
for (expected, actual) in zip(expected, ser.rolling(window)):
|
|
tm.assert_series_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"grouping,_index",
|
|
[
|
|
(
|
|
{"level": 0},
|
|
pd.MultiIndex.from_tuples(
|
|
[(0, 0), (0, 0), (1, 1), (1, 1), (1, 1)], names=[None, None]
|
|
),
|
|
),
|
|
(
|
|
{"by": "X"},
|
|
pd.MultiIndex.from_tuples(
|
|
[(0, 0), (1, 0), (2, 1), (3, 1), (4, 1)], names=["X", None]
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_rolling_positional_argument(grouping, _index, raw):
|
|
# GH 34605
|
|
|
|
def scaled_sum(*args):
|
|
if len(args) < 2:
|
|
raise ValueError("The function needs two arguments")
|
|
array, scale = args
|
|
return array.sum() / scale
|
|
|
|
df = DataFrame(data={"X": range(5)}, index=[0, 0, 1, 1, 1])
|
|
|
|
expected = DataFrame(data={"X": [0.0, 0.5, 1.0, 1.5, 2.0]}, index=_index)
|
|
result = df.groupby(**grouping).rolling(1).apply(scaled_sum, raw=raw, args=(2,))
|
|
tm.assert_frame_equal(result, expected)
|
|
|