craftbeerpi4-pione/venv3/lib/python3.7/site-packages/pandas/tests/window/test_expanding.py

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2021-03-03 23:49:41 +01:00
import numpy as np
import pytest
from pandas.errors import UnsupportedFunctionCall
import pandas as pd
from pandas import DataFrame, Series
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(which):
# GH 12669
c = which.expanding
# valid
c(min_periods=1)
c(min_periods=1, center=True)
c(min_periods=1, center=False)
# not valid
for w in [2.0, "foo", np.array([2])]:
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]), window=2)
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=pd.DatetimeIndex([]))
result = DataFrame(index=pd.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 = pd.Series([np.nan])
result = x.expanding(min_periods=0).sum()
expected = pd.Series([0.0])
tm.assert_series_equal(result, expected)
# minp=1
result = x.expanding(min_periods=1).sum()
expected = pd.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)
@pytest.mark.parametrize("constructor", [Series, DataFrame])
def test_expanding_count_with_min_periods(constructor):
# GH 26996
result = constructor(range(5)).expanding(min_periods=3).count()
expected = constructor([np.nan, np.nan, 3.0, 4.0, 5.0])
tm.assert_equal(result, expected)
@pytest.mark.parametrize("constructor", [Series, DataFrame])
def test_expanding_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, 3.0, 4.0, 5.0, 6.0]
result = constructor(values).expanding().count()
expected = constructor(expected_counts)
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 = pd.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()