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.
653 lines
20 KiB
653 lines
20 KiB
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.errors import UnsupportedFunctionCall
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
DatetimeIndex,
|
|
Index,
|
|
MultiIndex,
|
|
Series,
|
|
isna,
|
|
notna,
|
|
)
|
|
import pandas._testing as tm
|
|
from pandas.core.window import Expanding
|
|
|
|
|
|
def test_doc_string():
|
|
|
|
df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
|
|
df
|
|
df.expanding(2).sum()
|
|
|
|
|
|
@pytest.mark.filterwarnings(
|
|
"ignore:The `center` argument on `expanding` will be removed in the future"
|
|
)
|
|
def test_constructor(frame_or_series):
|
|
# GH 12669
|
|
|
|
c = frame_or_series(range(5)).expanding
|
|
|
|
# valid
|
|
c(min_periods=1)
|
|
c(min_periods=1, center=True)
|
|
c(min_periods=1, center=False)
|
|
|
|
|
|
@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
|
|
@pytest.mark.filterwarnings(
|
|
"ignore:The `center` argument on `expanding` will be removed in the future"
|
|
)
|
|
def test_constructor_invalid(frame_or_series, w):
|
|
# not valid
|
|
|
|
c = frame_or_series(range(5)).expanding
|
|
msg = "min_periods must be an integer"
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(min_periods=w)
|
|
|
|
msg = "center must be a boolean"
|
|
with pytest.raises(ValueError, match=msg):
|
|
c(min_periods=1, center=w)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["std", "mean", "sum", "max", "min", "var"])
|
|
def test_numpy_compat(method):
|
|
# see gh-12811
|
|
e = Expanding(Series([2, 4, 6]))
|
|
|
|
msg = "numpy operations are not valid with window objects"
|
|
|
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
|
getattr(e, method)(1, 2, 3)
|
|
with pytest.raises(UnsupportedFunctionCall, match=msg):
|
|
getattr(e, method)(dtype=np.float64)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"expander",
|
|
[
|
|
1,
|
|
pytest.param(
|
|
"ls",
|
|
marks=pytest.mark.xfail(
|
|
reason="GH#16425 expanding with offset not supported"
|
|
),
|
|
),
|
|
],
|
|
)
|
|
def test_empty_df_expanding(expander):
|
|
# GH 15819 Verifies that datetime and integer expanding windows can be
|
|
# applied to empty DataFrames
|
|
|
|
expected = DataFrame()
|
|
result = DataFrame().expanding(expander).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# Verifies that datetime and integer expanding windows can be applied
|
|
# to empty DataFrames with datetime index
|
|
expected = DataFrame(index=DatetimeIndex([]))
|
|
result = DataFrame(index=DatetimeIndex([])).expanding(expander).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_missing_minp_zero():
|
|
# https://github.com/pandas-dev/pandas/pull/18921
|
|
# minp=0
|
|
x = Series([np.nan])
|
|
result = x.expanding(min_periods=0).sum()
|
|
expected = Series([0.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# minp=1
|
|
result = x.expanding(min_periods=1).sum()
|
|
expected = Series([np.nan])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_expanding_axis(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 + [float(j) for j in range(3, 11)] for i in range(20)}
|
|
)
|
|
else:
|
|
# axis == 1
|
|
expected = DataFrame([[np.nan] * 2 + [float(i) for i in range(3, 21)]] * 10)
|
|
|
|
result = df.expanding(3, axis=axis_frame).sum()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_expanding_count_with_min_periods(frame_or_series):
|
|
# GH 26996
|
|
result = frame_or_series(range(5)).expanding(min_periods=3).count()
|
|
expected = frame_or_series([np.nan, np.nan, 3.0, 4.0, 5.0])
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
def test_expanding_count_default_min_periods_with_null_values(frame_or_series):
|
|
# GH 26996
|
|
values = [1, 2, 3, np.nan, 4, 5, 6]
|
|
expected_counts = [1.0, 2.0, 3.0, 3.0, 4.0, 5.0, 6.0]
|
|
|
|
result = frame_or_series(values).expanding().count()
|
|
expected = frame_or_series(expected_counts)
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
def test_expanding_count_with_min_periods_exceeding_series_length(frame_or_series):
|
|
# GH 25857
|
|
result = frame_or_series(range(5)).expanding(min_periods=6).count()
|
|
expected = frame_or_series([np.nan, np.nan, np.nan, np.nan, np.nan])
|
|
tm.assert_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"df,expected,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,
|
|
),
|
|
(
|
|
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]),
|
|
],
|
|
2,
|
|
),
|
|
(
|
|
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]),
|
|
],
|
|
1,
|
|
),
|
|
(DataFrame({"A": [1], "B": [4]}), [], 2),
|
|
(DataFrame(), [({}, [])], 1),
|
|
(
|
|
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,
|
|
),
|
|
(
|
|
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]),
|
|
],
|
|
2,
|
|
),
|
|
(
|
|
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]),
|
|
],
|
|
1,
|
|
),
|
|
],
|
|
)
|
|
def test_iter_expanding_dataframe(df, expected, min_periods):
|
|
# GH 11704
|
|
expected = [DataFrame(values, index=index) for (values, index) in expected]
|
|
|
|
for (expected, actual) in zip(expected, df.expanding(min_periods)):
|
|
tm.assert_frame_equal(actual, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"ser,expected,min_periods",
|
|
[
|
|
(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 3),
|
|
(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 2),
|
|
(Series([1, 2, 3]), [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])], 1),
|
|
(Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2),
|
|
(Series([np.nan, 2]), [([np.nan], [0]), ([np.nan, 2], [0, 1])], 2),
|
|
(Series([], dtype="int64"), [], 2),
|
|
],
|
|
)
|
|
def test_iter_expanding_series(ser, expected, min_periods):
|
|
# GH 11704
|
|
expected = [Series(values, index=index) for (values, index) in expected]
|
|
|
|
for (expected, actual) in zip(expected, ser.expanding(min_periods)):
|
|
tm.assert_series_equal(actual, expected)
|
|
|
|
|
|
def test_center_deprecate_warning():
|
|
# GH 20647
|
|
df = DataFrame()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.expanding(center=True)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
df.expanding(center=False)
|
|
|
|
with tm.assert_produces_warning(None):
|
|
df.expanding()
|
|
|
|
|
|
def test_expanding_sem(frame_or_series):
|
|
# GH: 26476
|
|
obj = frame_or_series([0, 1, 2])
|
|
result = obj.expanding().sem()
|
|
if isinstance(result, DataFrame):
|
|
result = Series(result[0].values)
|
|
expected = Series([np.nan] + [0.707107] * 2)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("method", ["skew", "kurt"])
|
|
def test_expanding_skew_kurt_numerical_stability(method):
|
|
# GH: 6929
|
|
s = Series(np.random.rand(10))
|
|
expected = getattr(s.expanding(3), method)()
|
|
s = s + 5000
|
|
result = getattr(s.expanding(3), method)()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("window", [1, 3, 10, 20])
|
|
@pytest.mark.parametrize("method", ["min", "max", "average"])
|
|
@pytest.mark.parametrize("pct", [True, False])
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
|
@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"])
|
|
def test_rank(window, method, pct, ascending, test_data):
|
|
length = 20
|
|
if test_data == "default":
|
|
ser = Series(data=np.random.rand(length))
|
|
elif test_data == "duplicates":
|
|
ser = Series(data=np.random.choice(3, length))
|
|
elif test_data == "nans":
|
|
ser = Series(
|
|
data=np.random.choice([1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length)
|
|
)
|
|
|
|
expected = ser.expanding(window).apply(
|
|
lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1]
|
|
)
|
|
result = ser.expanding(window).rank(method=method, pct=pct, ascending=ascending)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_expanding_corr(series):
|
|
A = series.dropna()
|
|
B = (A + np.random.randn(len(A)))[:-5]
|
|
|
|
result = A.expanding().corr(B)
|
|
|
|
rolling_result = A.rolling(window=len(A), min_periods=1).corr(B)
|
|
|
|
tm.assert_almost_equal(rolling_result, result)
|
|
|
|
|
|
def test_expanding_count(series):
|
|
result = series.expanding(min_periods=0).count()
|
|
tm.assert_almost_equal(
|
|
result, series.rolling(window=len(series), min_periods=0).count()
|
|
)
|
|
|
|
|
|
def test_expanding_quantile(series):
|
|
result = series.expanding().quantile(0.5)
|
|
|
|
rolling_result = series.rolling(window=len(series), min_periods=1).quantile(0.5)
|
|
|
|
tm.assert_almost_equal(result, rolling_result)
|
|
|
|
|
|
def test_expanding_cov(series):
|
|
A = series
|
|
B = (A + np.random.randn(len(A)))[:-5]
|
|
|
|
result = A.expanding().cov(B)
|
|
|
|
rolling_result = A.rolling(window=len(A), min_periods=1).cov(B)
|
|
|
|
tm.assert_almost_equal(rolling_result, result)
|
|
|
|
|
|
def test_expanding_cov_pairwise(frame):
|
|
result = frame.expanding().cov()
|
|
|
|
rolling_result = frame.rolling(window=len(frame), min_periods=1).cov()
|
|
|
|
tm.assert_frame_equal(result, rolling_result)
|
|
|
|
|
|
def test_expanding_corr_pairwise(frame):
|
|
result = frame.expanding().corr()
|
|
|
|
rolling_result = frame.rolling(window=len(frame), min_periods=1).corr()
|
|
tm.assert_frame_equal(result, rolling_result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"func,static_comp",
|
|
[
|
|
("sum", np.sum),
|
|
("mean", lambda x: np.mean(x, axis=0)),
|
|
("max", lambda x: np.max(x, axis=0)),
|
|
("min", lambda x: np.min(x, axis=0)),
|
|
],
|
|
ids=["sum", "mean", "max", "min"],
|
|
)
|
|
def test_expanding_func(func, static_comp, frame_or_series):
|
|
data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10))
|
|
result = getattr(data.expanding(min_periods=1, axis=0), func)()
|
|
assert isinstance(result, frame_or_series)
|
|
|
|
expected = static_comp(data[:11])
|
|
if frame_or_series is Series:
|
|
tm.assert_almost_equal(result[10], expected)
|
|
else:
|
|
tm.assert_series_equal(result.iloc[10], expected, check_names=False)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"func,static_comp",
|
|
[("sum", np.sum), ("mean", np.mean), ("max", np.max), ("min", np.min)],
|
|
ids=["sum", "mean", "max", "min"],
|
|
)
|
|
def test_expanding_min_periods(func, static_comp):
|
|
ser = Series(np.random.randn(50))
|
|
|
|
result = getattr(ser.expanding(min_periods=30, axis=0), func)()
|
|
assert result[:29].isna().all()
|
|
tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))
|
|
|
|
# min_periods is working correctly
|
|
result = getattr(ser.expanding(min_periods=15, axis=0), func)()
|
|
assert isna(result.iloc[13])
|
|
assert notna(result.iloc[14])
|
|
|
|
ser2 = Series(np.random.randn(20))
|
|
result = getattr(ser2.expanding(min_periods=5, axis=0), func)()
|
|
assert isna(result[3])
|
|
assert notna(result[4])
|
|
|
|
# min_periods=0
|
|
result0 = getattr(ser.expanding(min_periods=0, axis=0), func)()
|
|
result1 = getattr(ser.expanding(min_periods=1, axis=0), func)()
|
|
tm.assert_almost_equal(result0, result1)
|
|
|
|
result = getattr(ser.expanding(min_periods=1, axis=0), func)()
|
|
tm.assert_almost_equal(result.iloc[-1], static_comp(ser[:50]))
|
|
|
|
|
|
def test_expanding_apply(engine_and_raw, frame_or_series):
|
|
engine, raw = engine_and_raw
|
|
data = frame_or_series(np.array(list(range(10)) + [np.nan] * 10))
|
|
result = data.expanding(min_periods=1).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
assert isinstance(result, frame_or_series)
|
|
|
|
if frame_or_series is Series:
|
|
tm.assert_almost_equal(result[9], np.mean(data[:11], axis=0))
|
|
else:
|
|
tm.assert_series_equal(
|
|
result.iloc[9], np.mean(data[:11], axis=0), check_names=False
|
|
)
|
|
|
|
|
|
def test_expanding_min_periods_apply(engine_and_raw):
|
|
engine, raw = engine_and_raw
|
|
ser = Series(np.random.randn(50))
|
|
|
|
result = ser.expanding(min_periods=30).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
assert result[:29].isna().all()
|
|
tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50]))
|
|
|
|
# min_periods is working correctly
|
|
result = ser.expanding(min_periods=15).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
assert isna(result.iloc[13])
|
|
assert notna(result.iloc[14])
|
|
|
|
ser2 = Series(np.random.randn(20))
|
|
result = ser2.expanding(min_periods=5).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
assert isna(result[3])
|
|
assert notna(result[4])
|
|
|
|
# min_periods=0
|
|
result0 = ser.expanding(min_periods=0).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
result1 = ser.expanding(min_periods=1).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
tm.assert_almost_equal(result0, result1)
|
|
|
|
result = ser.expanding(min_periods=1).apply(
|
|
lambda x: x.mean(), raw=raw, engine=engine
|
|
)
|
|
tm.assert_almost_equal(result.iloc[-1], np.mean(ser[:50]))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"f",
|
|
[
|
|
lambda x: (x.expanding(min_periods=5).cov(x, pairwise=True)),
|
|
lambda x: (x.expanding(min_periods=5).corr(x, pairwise=True)),
|
|
],
|
|
)
|
|
def test_moment_functions_zero_length_pairwise(f):
|
|
|
|
df1 = DataFrame()
|
|
df2 = DataFrame(columns=Index(["a"], name="foo"), index=Index([], name="bar"))
|
|
df2["a"] = df2["a"].astype("float64")
|
|
|
|
df1_expected = DataFrame(
|
|
index=MultiIndex.from_product([df1.index, df1.columns]), columns=Index([])
|
|
)
|
|
df2_expected = DataFrame(
|
|
index=MultiIndex.from_product([df2.index, df2.columns], names=["bar", "foo"]),
|
|
columns=Index(["a"], name="foo"),
|
|
dtype="float64",
|
|
)
|
|
|
|
df1_result = f(df1)
|
|
tm.assert_frame_equal(df1_result, df1_expected)
|
|
|
|
df2_result = f(df2)
|
|
tm.assert_frame_equal(df2_result, df2_expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"f",
|
|
[
|
|
lambda x: x.expanding().count(),
|
|
lambda x: x.expanding(min_periods=5).cov(x, pairwise=False),
|
|
lambda x: x.expanding(min_periods=5).corr(x, pairwise=False),
|
|
lambda x: x.expanding(min_periods=5).max(),
|
|
lambda x: x.expanding(min_periods=5).min(),
|
|
lambda x: x.expanding(min_periods=5).sum(),
|
|
lambda x: x.expanding(min_periods=5).mean(),
|
|
lambda x: x.expanding(min_periods=5).std(),
|
|
lambda x: x.expanding(min_periods=5).var(),
|
|
lambda x: x.expanding(min_periods=5).skew(),
|
|
lambda x: x.expanding(min_periods=5).kurt(),
|
|
lambda x: x.expanding(min_periods=5).quantile(0.5),
|
|
lambda x: x.expanding(min_periods=5).median(),
|
|
lambda x: x.expanding(min_periods=5).apply(sum, raw=False),
|
|
lambda x: x.expanding(min_periods=5).apply(sum, raw=True),
|
|
],
|
|
)
|
|
def test_moment_functions_zero_length(f):
|
|
# GH 8056
|
|
s = Series(dtype=np.float64)
|
|
s_expected = s
|
|
df1 = DataFrame()
|
|
df1_expected = df1
|
|
df2 = DataFrame(columns=["a"])
|
|
df2["a"] = df2["a"].astype("float64")
|
|
df2_expected = df2
|
|
|
|
s_result = f(s)
|
|
tm.assert_series_equal(s_result, s_expected)
|
|
|
|
df1_result = f(df1)
|
|
tm.assert_frame_equal(df1_result, df1_expected)
|
|
|
|
df2_result = f(df2)
|
|
tm.assert_frame_equal(df2_result, df2_expected)
|
|
|
|
|
|
def test_expanding_apply_empty_series(engine_and_raw):
|
|
engine, raw = engine_and_raw
|
|
ser = Series([], dtype=np.float64)
|
|
tm.assert_series_equal(
|
|
ser, ser.expanding().apply(lambda x: x.mean(), raw=raw, engine=engine)
|
|
)
|
|
|
|
|
|
def test_expanding_apply_min_periods_0(engine_and_raw):
|
|
# GH 8080
|
|
engine, raw = engine_and_raw
|
|
s = Series([None, None, None])
|
|
result = s.expanding(min_periods=0).apply(lambda x: len(x), raw=raw, engine=engine)
|
|
expected = Series([1.0, 2.0, 3.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_expanding_cov_diff_index():
|
|
# GH 7512
|
|
s1 = Series([1, 2, 3], index=[0, 1, 2])
|
|
s2 = Series([1, 3], index=[0, 2])
|
|
result = s1.expanding().cov(s2)
|
|
expected = Series([None, None, 2.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s2a = Series([1, None, 3], index=[0, 1, 2])
|
|
result = s1.expanding().cov(s2a)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s1 = Series([7, 8, 10], index=[0, 1, 3])
|
|
s2 = Series([7, 9, 10], index=[0, 2, 3])
|
|
result = s1.expanding().cov(s2)
|
|
expected = Series([None, None, None, 4.5])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_expanding_corr_diff_index():
|
|
# GH 7512
|
|
s1 = Series([1, 2, 3], index=[0, 1, 2])
|
|
s2 = Series([1, 3], index=[0, 2])
|
|
result = s1.expanding().corr(s2)
|
|
expected = Series([None, None, 1.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s2a = Series([1, None, 3], index=[0, 1, 2])
|
|
result = s1.expanding().corr(s2a)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s1 = Series([7, 8, 10], index=[0, 1, 3])
|
|
s2 = Series([7, 9, 10], index=[0, 2, 3])
|
|
result = s1.expanding().corr(s2)
|
|
expected = Series([None, None, None, 1.0])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_expanding_cov_pairwise_diff_length():
|
|
# GH 7512
|
|
df1 = DataFrame([[1, 5], [3, 2], [3, 9]], columns=Index(["A", "B"], name="foo"))
|
|
df1a = DataFrame(
|
|
[[1, 5], [3, 9]], index=[0, 2], columns=Index(["A", "B"], name="foo")
|
|
)
|
|
df2 = DataFrame(
|
|
[[5, 6], [None, None], [2, 1]], columns=Index(["X", "Y"], name="foo")
|
|
)
|
|
df2a = DataFrame(
|
|
[[5, 6], [2, 1]], index=[0, 2], columns=Index(["X", "Y"], name="foo")
|
|
)
|
|
# TODO: xref gh-15826
|
|
# .loc is not preserving the names
|
|
result1 = df1.expanding().cov(df2, pairwise=True).loc[2]
|
|
result2 = df1.expanding().cov(df2a, pairwise=True).loc[2]
|
|
result3 = df1a.expanding().cov(df2, pairwise=True).loc[2]
|
|
result4 = df1a.expanding().cov(df2a, pairwise=True).loc[2]
|
|
expected = DataFrame(
|
|
[[-3.0, -6.0], [-5.0, -10.0]],
|
|
columns=Index(["A", "B"], name="foo"),
|
|
index=Index(["X", "Y"], name="foo"),
|
|
)
|
|
tm.assert_frame_equal(result1, expected)
|
|
tm.assert_frame_equal(result2, expected)
|
|
tm.assert_frame_equal(result3, expected)
|
|
tm.assert_frame_equal(result4, expected)
|
|
|
|
|
|
def test_expanding_corr_pairwise_diff_length():
|
|
# GH 7512
|
|
df1 = DataFrame(
|
|
[[1, 2], [3, 2], [3, 4]], columns=["A", "B"], index=Index(range(3), name="bar")
|
|
)
|
|
df1a = DataFrame(
|
|
[[1, 2], [3, 4]], index=Index([0, 2], name="bar"), columns=["A", "B"]
|
|
)
|
|
df2 = DataFrame(
|
|
[[5, 6], [None, None], [2, 1]],
|
|
columns=["X", "Y"],
|
|
index=Index(range(3), name="bar"),
|
|
)
|
|
df2a = DataFrame(
|
|
[[5, 6], [2, 1]], index=Index([0, 2], name="bar"), columns=["X", "Y"]
|
|
)
|
|
result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
|
|
result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
|
|
result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
|
|
result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
|
|
expected = DataFrame(
|
|
[[-1.0, -1.0], [-1.0, -1.0]], columns=["A", "B"], index=Index(["X", "Y"])
|
|
)
|
|
tm.assert_frame_equal(result1, expected)
|
|
tm.assert_frame_equal(result2, expected)
|
|
tm.assert_frame_equal(result3, expected)
|
|
tm.assert_frame_equal(result4, expected)
|
|
|
|
|
|
def test_expanding_apply_args_kwargs(engine_and_raw):
|
|
def mean_w_arg(x, const):
|
|
return np.mean(x) + const
|
|
|
|
engine, raw = engine_and_raw
|
|
|
|
df = DataFrame(np.random.rand(20, 3))
|
|
|
|
expected = df.expanding().apply(np.mean, engine=engine, raw=raw) + 20.0
|
|
|
|
result = df.expanding().apply(mean_w_arg, engine=engine, raw=raw, args=(20,))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = df.expanding().apply(mean_w_arg, raw=raw, kwargs={"const": 20})
|
|
tm.assert_frame_equal(result, expected)
|
|
|