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/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/

/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_align.py	5359	0644	edit dl rm
test_append.py	9600	0644	edit dl rm
test_argsort.py	2249	0644	edit dl rm
test_asfreq.py	3616	0644	edit dl rm
test_asof.py	5515	0644	edit dl rm
test_astype.py	2628	0644	edit dl rm
test_at_time.py	2629	0644	edit dl rm
test_autocorr.py	999	0644	edit dl rm
test_between.py	1197	0644	edit dl rm
test_between_time.py	5086	0644	edit dl rm
test_clip.py	3384	0644	edit dl rm
test_combine.py	627	0644	edit dl rm
test_combine_first.py	3411	0644	edit dl rm
test_compare.py	3734	0644	edit dl rm
test_convert_dtypes.py	9914	0644	edit dl rm
test_count.py	1123	0644	edit dl rm
test_cov_corr.py	5215	0644	edit dl rm
test_describe.py	4640	0644	edit dl rm
test_diff.py	2331	0644	edit dl rm
test_drop.py	2986	0644	edit dl rm
test_droplevel.py	629	0644	edit dl rm
test_drop_duplicates.py	6850	0644	edit dl rm
test_duplicated.py	996	0644	edit dl rm
test_equals.py	1295	0644	edit dl rm
test_explode.py	3538	0644	edit dl rm
test_fillna.py	6513	0644	edit dl rm
test_first_and_last.py	2042	0644	edit dl rm
test_head_tail.py	343	0644	edit dl rm
test_interpolate.py	27757	0644	edit dl rm
test_isin.py	3445	0644	edit dl rm
test_nlargest.py	7121	0644	edit dl rm
test_pct_change.py	2968	0644	edit dl rm
test_quantile.py	6810	0644	edit dl rm
test_rank.py	20287	0644	edit dl rm
test_reindex_like.py	1245	0644	edit dl rm
test_rename.py	3378	0644	edit dl rm
test_rename_axis.py	1503	0644	edit dl rm
test_replace.py	15559	0644	edit dl rm
test_reset_index.py	4636	0644	edit dl rm
test_round.py	1970	0644	edit dl rm
test_searchsorted.py	2121	0644	edit dl rm
test_shift.py	12595	0644	edit dl rm
test_sort_index.py	12277	0644	edit dl rm
test_sort_values.py	7981	0644	edit dl rm
test_to_dict.py	742	0644	edit dl rm
test_to_period.py	1755	0644	edit dl rm
test_to_timestamp.py	2616	0644	edit dl rm
test_truncate.py	5557	0644	edit dl rm
test_tz_convert.py	1008	0644	edit dl rm
test_tz_localize.py	3108	0644	edit dl rm
test_unstack.py	4208	0644	edit dl rm
test_update.py	4213	0644	edit dl rm
test_value_counts.py	8136	0644	edit dl rm
__init__.py	225	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/test_cov_corr.py (5215B)

import math

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import Series, isna
import pandas._testing as tm

class TestSeriesCov:
    def test_cov(self, datetime_series):
        # full overlap
        tm.assert_almost_equal(
            datetime_series.cov(datetime_series), datetime_series.std() ** 2
        )

# partial overlap
        tm.assert_almost_equal(
            datetime_series[:15].cov(datetime_series[5:]),
            datetime_series[5:15].std() ** 2,
        )

# No overlap
        assert np.isnan(datetime_series[::2].cov(datetime_series[1::2]))

# all NA
        cp = datetime_series[:10].copy()
        cp[:] = np.nan
        assert isna(cp.cov(cp))

# min_periods
        assert isna(datetime_series[:15].cov(datetime_series[5:], min_periods=12))

ts1 = datetime_series[:15].reindex(datetime_series.index)
        ts2 = datetime_series[5:].reindex(datetime_series.index)
        assert isna(ts1.cov(ts2, min_periods=12))

@pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
    def test_cov_ddof(self, test_ddof):
        # GH#34611
        np_array1 = np.random.rand(10)
        np_array2 = np.random.rand(10)

s1 = Series(np_array1)
        s2 = Series(np_array2)

result = s1.cov(s2, ddof=test_ddof)
        expected = np.cov(np_array1, np_array2, ddof=test_ddof)[0][1]
        assert math.isclose(expected, result)

class TestSeriesCorr:
    @td.skip_if_no_scipy
    def test_corr(self, datetime_series):
        import scipy.stats as stats

# full overlap
        tm.assert_almost_equal(datetime_series.corr(datetime_series), 1)

# partial overlap
        tm.assert_almost_equal(datetime_series[:15].corr(datetime_series[5:]), 1)

assert isna(datetime_series[:15].corr(datetime_series[5:], min_periods=12))

ts1 = datetime_series[:15].reindex(datetime_series.index)
        ts2 = datetime_series[5:].reindex(datetime_series.index)
        assert isna(ts1.corr(ts2, min_periods=12))

# No overlap
        assert np.isnan(datetime_series[::2].corr(datetime_series[1::2]))

# all NA
        cp = datetime_series[:10].copy()
        cp[:] = np.nan
        assert isna(cp.corr(cp))

A = tm.makeTimeSeries()
        B = tm.makeTimeSeries()
        result = A.corr(B)
        expected, _ = stats.pearsonr(A, B)
        tm.assert_almost_equal(result, expected)

@td.skip_if_no_scipy
    def test_corr_rank(self):
        import scipy.stats as stats

# kendall and spearman
        A = tm.makeTimeSeries()
        B = tm.makeTimeSeries()
        A[-5:] = A[:5]
        result = A.corr(B, method="kendall")
        expected = stats.kendalltau(A, B)[0]
        tm.assert_almost_equal(result, expected)

result = A.corr(B, method="spearman")
        expected = stats.spearmanr(A, B)[0]
        tm.assert_almost_equal(result, expected)

# results from R
        A = Series(
            [
                -0.89926396,
                0.94209606,
                -1.03289164,
                -0.95445587,
                0.76910310,
                -0.06430576,
                -2.09704447,
                0.40660407,
                -0.89926396,
                0.94209606,
            ]
        )
        B = Series(
            [
                -1.01270225,
                -0.62210117,
                -1.56895827,
                0.59592943,
                -0.01680292,
                1.17258718,
                -1.06009347,
                -0.10222060,
                -0.89076239,
                0.89372375,
            ]
        )
        kexp = 0.4319297
        sexp = 0.5853767
        tm.assert_almost_equal(A.corr(B, method="kendall"), kexp)
        tm.assert_almost_equal(A.corr(B, method="spearman"), sexp)

def test_corr_invalid_method(self):
        # GH PR #22298
        s1 = pd.Series(np.random.randn(10))
        s2 = pd.Series(np.random.randn(10))
        msg = "method must be either 'pearson', 'spearman', 'kendall', or a callable, "
        with pytest.raises(ValueError, match=msg):
            s1.corr(s2, method="____")

def test_corr_callable_method(self, datetime_series):
        # simple correlation example
        # returns 1 if exact equality, 0 otherwise
        my_corr = lambda a, b: 1.0 if (a == b).all() else 0.0

# simple example
        s1 = Series([1, 2, 3, 4, 5])
        s2 = Series([5, 4, 3, 2, 1])
        expected = 0
        tm.assert_almost_equal(s1.corr(s2, method=my_corr), expected)

# full overlap
        tm.assert_almost_equal(
            datetime_series.corr(datetime_series, method=my_corr), 1.0
        )

# partial overlap
        tm.assert_almost_equal(
            datetime_series[:15].corr(datetime_series[5:], method=my_corr), 1.0
        )

# No overlap
        assert np.isnan(
            datetime_series[::2].corr(datetime_series[1::2], method=my_corr)
        )

# dataframe example
        df = pd.DataFrame([s1, s2])
        expected = pd.DataFrame([{0: 1.0, 1: 0}, {0: 0, 1: 1.0}])
        tm.assert_almost_equal(df.transpose().corr(method=my_corr), expected)

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