import math
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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Series,
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isna,
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)
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import pandas._testing as tm
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class TestSeriesCov:
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def test_cov(self, datetime_series):
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# full overlap
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tm.assert_almost_equal(
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datetime_series.cov(datetime_series), datetime_series.std() ** 2
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)
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# partial overlap
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tm.assert_almost_equal(
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datetime_series[:15].cov(datetime_series[5:]),
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datetime_series[5:15].std() ** 2,
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)
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# No overlap
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assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))
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# all NA
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cp = datetime_series[:10].copy()
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cp[:] = np.nan
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assert isna(cp.cov(cp))
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# min_periods
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assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12))
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ts1 = datetime_series[:15].reindex(datetime_series.index)
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ts2 = datetime_series[5:].reindex(datetime_series.index)
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assert isna(ts1.cov(ts2, min_periods=12))
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@pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
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def test_cov_ddof(self, test_ddof):
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# GH#34611
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np_array1 = np.random.rand(10)
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np_array2 = np.random.rand(10)
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s1 = Series(np_array1)
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s2 = Series(np_array2)
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result = s1.cov(s2, ddof=test_ddof)
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expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1]
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assert math.isclose(expected, result)
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class TestSeriesCorr:
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@td.skip_if_no_scipy
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def test_corr(self, datetime_series):
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from scipy import stats
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# full overlap
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tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)
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# partial overlap
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tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1)
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assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12))
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ts1 = datetime_series[:15].reindex(datetime_series.index)
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ts2 = datetime_series[5:].reindex(datetime_series.index)
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assert isna(ts1.corr(ts2, min_periods=12))
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# No overlap
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assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))
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# all NA
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cp = datetime_series[:10].copy()
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cp[:] = np.nan
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assert isna(cp.corr(cp))
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A = tm.makeTimeSeries()
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B = tm.makeTimeSeries()
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result = A.corr(B)
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expected, _ = stats.pearsonr(A, B)
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tm.assert_almost_equal(result, expected)
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@td.skip_if_no_scipy
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def test_corr_rank(self):
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from scipy import stats
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# kendall and spearman
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A = tm.makeTimeSeries()
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B = tm.makeTimeSeries()
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A[-5:] = A[:5]
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result = A.corr(B, method="kendall")
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expected = stats.kendalltau(A, B)[0]
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tm.assert_almost_equal(result, expected)
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result = A.corr(B, method="spearman")
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expected = stats.spearmanr(A, B)[0]
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tm.assert_almost_equal(result, expected)
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# results from R
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A = Series(
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[
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-0.89926396,
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0.94209606,
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-1.03289164,
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-0.95445587,
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0.76910310,
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-0.06430576,
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-2.09704447,
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0.40660407,
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-0.89926396,
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0.94209606,
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]
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)
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B = Series(
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[
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-1.01270225,
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-0.62210117,
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-1.56895827,
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0.59592943,
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-0.01680292,
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1.17258718,
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-1.06009347,
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-0.10222060,
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-0.89076239,
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0.89372375,
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]
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)
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kexp = 0.4319297
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sexp = 0.5853767
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tm.assert_almost_equal(A.corr(B, method="kendall"), kexp)
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tm.assert_almost_equal(A.corr(B, method="spearman"), sexp)
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def test_corr_invalid_method(self):
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# GH PR #22298
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s1 = Series(np.random.randn(10))
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s2 = Series(np.random.randn(10))
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msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
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with pytest.raises(ValueError, match=msg):
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s1.corr(s2, method="____")
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def test_corr_callable_method(self, datetime_series):
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# simple correlation example
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# returns 1 if exact equality, 0 otherwise
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my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0
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# simple example
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s1 = Series([1, 2, 3, 4, 5])
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s2 = Series([5, 4, 3, 2, 1])
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expected = 0
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tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)
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# full overlap
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tm.assert_almost_equal(
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datetime_series.corr(datetime_series, method=my_corr), 1.0
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)
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# partial overlap
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tm.assert_almost_equal(
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datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0
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)
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# No overlap
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assert np.isnan(
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datetime_series[::2].corr(datetime_series[1::2], method=my_corr)
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)
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# dataframe example
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df = pd.DataFrame([s1, s2])
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expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}])
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tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected)
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