import numpy as np
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import pytest
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from pandas import Series
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import pandas._testing as tm
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def no_nans(x):
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return x.notna().all().all()
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def all_na(x):
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return x.isnull().all().all()
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@pytest.mark.parametrize("f", [lambda v: Series(v).sum(), np.nansum, np.sum])
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def test_expanding_apply_consistency_sum_nans(request, all_data, min_periods, f):
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if f is np.sum:
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if not no_nans(all_data) and not (
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all_na(all_data) and not all_data.empty and min_periods > 0
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):
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request.node.add_marker(
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pytest.mark.xfail(reason="np.sum has different behavior with NaNs")
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)
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expanding_f_result = all_data.expanding(min_periods=min_periods).sum()
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expanding_apply_f_result = all_data.expanding(min_periods=min_periods).apply(
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func=f, raw=True
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)
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tm.assert_equal(expanding_f_result, expanding_apply_f_result)
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@pytest.mark.parametrize("ddof", [0, 1])
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def test_moments_consistency_var(all_data, min_periods, ddof):
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var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof)
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assert not (var_x < 0).any().any()
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if ddof == 0:
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# check that biased var(x) == mean(x^2) - mean(x)^2
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mean_x2 = (all_data * all_data).expanding(min_periods=min_periods).mean()
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mean_x = all_data.expanding(min_periods=min_periods).mean()
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tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x))
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@pytest.mark.parametrize("ddof", [0, 1])
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def test_moments_consistency_var_constant(consistent_data, min_periods, ddof):
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count_x = consistent_data.expanding(min_periods=min_periods).count()
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var_x = consistent_data.expanding(min_periods=min_periods).var(ddof=ddof)
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# check that variance of constant series is identically 0
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assert not (var_x > 0).any().any()
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expected = consistent_data * np.nan
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expected[count_x >= max(min_periods, 1)] = 0.0
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if ddof == 1:
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expected[count_x < 2] = np.nan
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tm.assert_equal(var_x, expected)
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@pytest.mark.parametrize("ddof", [0, 1])
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def test_expanding_consistency_var_std_cov(all_data, min_periods, ddof):
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var_x = all_data.expanding(min_periods=min_periods).var(ddof=ddof)
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assert not (var_x < 0).any().any()
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std_x = all_data.expanding(min_periods=min_periods).std(ddof=ddof)
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assert not (std_x < 0).any().any()
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# check that var(x) == std(x)^2
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tm.assert_equal(var_x, std_x * std_x)
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cov_x_x = all_data.expanding(min_periods=min_periods).cov(all_data, ddof=ddof)
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assert not (cov_x_x < 0).any().any()
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# check that var(x) == cov(x, x)
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tm.assert_equal(var_x, cov_x_x)
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@pytest.mark.parametrize("ddof", [0, 1])
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def test_expanding_consistency_series_cov_corr(series_data, min_periods, ddof):
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var_x_plus_y = (
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(series_data + series_data).expanding(min_periods=min_periods).var(ddof=ddof)
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)
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var_x = series_data.expanding(min_periods=min_periods).var(ddof=ddof)
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var_y = series_data.expanding(min_periods=min_periods).var(ddof=ddof)
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cov_x_y = series_data.expanding(min_periods=min_periods).cov(series_data, ddof=ddof)
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# check that cov(x, y) == (var(x+y) - var(x) -
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# var(y)) / 2
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tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y))
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# check that corr(x, y) == cov(x, y) / (std(x) *
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# std(y))
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corr_x_y = series_data.expanding(min_periods=min_periods).corr(series_data)
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std_x = series_data.expanding(min_periods=min_periods).std(ddof=ddof)
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std_y = series_data.expanding(min_periods=min_periods).std(ddof=ddof)
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tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y))
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if ddof == 0:
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# check that biased cov(x, y) == mean(x*y) -
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# mean(x)*mean(y)
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mean_x = series_data.expanding(min_periods=min_periods).mean()
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mean_y = series_data.expanding(min_periods=min_periods).mean()
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mean_x_times_y = (
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(series_data * series_data).expanding(min_periods=min_periods).mean()
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)
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tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y))
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def test_expanding_consistency_mean(all_data, min_periods):
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result = all_data.expanding(min_periods=min_periods).mean()
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expected = (
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all_data.expanding(min_periods=min_periods).sum()
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/ all_data.expanding(min_periods=min_periods).count()
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)
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tm.assert_equal(result, expected.astype("float64"))
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def test_expanding_consistency_constant(consistent_data, min_periods):
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count_x = consistent_data.expanding().count()
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mean_x = consistent_data.expanding(min_periods=min_periods).mean()
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# check that correlation of a series with itself is either 1 or NaN
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corr_x_x = consistent_data.expanding(min_periods=min_periods).corr(consistent_data)
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exp = (
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consistent_data.max()
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if isinstance(consistent_data, Series)
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else consistent_data.max().max()
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)
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# check mean of constant series
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expected = consistent_data * np.nan
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expected[count_x >= max(min_periods, 1)] = exp
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tm.assert_equal(mean_x, expected)
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# check correlation of constant series with itself is NaN
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expected[:] = np.nan
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tm.assert_equal(corr_x_x, expected)
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def test_expanding_consistency_var_debiasing_factors(all_data, min_periods):
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# check variance debiasing factors
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var_unbiased_x = all_data.expanding(min_periods=min_periods).var()
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var_biased_x = all_data.expanding(min_periods=min_periods).var(ddof=0)
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var_debiasing_factors_x = all_data.expanding().count() / (
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all_data.expanding().count() - 1.0
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).replace(0.0, np.nan)
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tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
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