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Added 'pearson' to methods list in pandas/core/nanops.py #30603

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Merged
merged 10 commits into from
Jan 7, 2020
Merged

Added 'pearson' to methods list in pandas/core/nanops.py #30603

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6804926
Added 'pearson' to methods list
Jan 1, 2020
0f1a2f2
Added 'scipy' as a dependency for azure-macos
Jan 1, 2020
a7decf4
Removed 'scipy' dependency for azure-macos
Jan 1, 2020
ed6f9c9
Added skip if no scipy decorator
Jan 1, 2020
51d5901
Fixes for review #1
Jan 2, 2020
a2cc03e
Merge remote-tracking branch 'upstream/master' into nanops-pearson
Jan 2, 2020
866c70e
Removed all added skips
Jan 2, 2020
c3350db
Merge remote-tracking branch 'upstream/master' into nanops-pearson
Jan 5, 2020
260a826
TST: Added test case
Jan 5, 2020
9571c3b
Merge remote-tracking branch 'upstream/master' into nanops-pearson
Jan 6, 2020
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2 changes: 1 addition & 1 deletion pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1241,7 +1241,7 @@ def nancorr(a, b, method="pearson", min_periods=None):


def get_corr_func(method):
if method in ["kendall", "spearman"]:
if method in ["kendall", "spearman", "pearson"]:
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actrually, why is this necessary at all? this just uses numpy and not scipy

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You asked me to:)

ref - #30461 (comment)

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ahh right, but this is not actually the correct check, instead do like

if method in ['kendall', 'spearman']:
   .....
elif method in ['pearson']:
    pass
elif callable(method):
   ....
else:
     raise ValueError("....in valid method passed')

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raise ValueError(f"Unrecognized method '{method}'")

Sounds good? (probably not, my English is not so good)

And also, should I remove all the "Skip" decorators Iv'e added in ed6f9c9 ?

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sure & yes remove the skips

from scipy.stats import kendalltau, spearmanr
elif callable(method):
return method
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3 changes: 3 additions & 0 deletions pandas/tests/frame/methods/test_cov_corr.py
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Expand Up @@ -155,6 +155,7 @@ def test_corr_int(self):


class TestDataFrameCorrWith:
@td.skip_if_no_scipy
def test_corrwith(self, datetime_frame):
a = datetime_frame
noise = Series(np.random.randn(len(a)), index=a.index)
Expand Down Expand Up @@ -203,6 +204,7 @@ def test_corrwith_with_objects(self):
expected = df1.loc[:, cols].corrwith(df2.loc[:, cols], axis=1)
tm.assert_series_equal(result, expected)

@td.skip_if_no_scipy
def test_corrwith_series(self, datetime_frame):
result = datetime_frame.corrwith(datetime_frame["A"])
expected = datetime_frame.apply(datetime_frame["A"].corr)
Expand All @@ -218,6 +220,7 @@ def test_corrwith_matches_corrcoef(self):
tm.assert_almost_equal(c1, c2)
assert c1 < 1

@td.skip_if_no_scipy
def test_corrwith_mixed_dtypes(self):
# GH#18570
df = pd.DataFrame(
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1 change: 1 addition & 0 deletions pandas/tests/series/test_timeseries.py
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Expand Up @@ -76,6 +76,7 @@ def test_asfreq_datetimeindex_empty_series(self):
result = Series([3], index=index.copy()).asfreq("H")
tm.assert_index_equal(expected.index, result.index)

@td.skip_if_no_scipy
def test_autocorr(self, datetime_series):
# Just run the function
corr1 = datetime_series.autocorr()
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2 changes: 2 additions & 0 deletions pandas/tests/test_nanops.py
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Expand Up @@ -560,6 +560,7 @@ def check_nancorr_nancov_1d(self, checkfun, targ0, targ1, **kwargs):
tm.assert_almost_equal(targ2, res24)
tm.assert_almost_equal(targ2, res25)

@td.skip_if_no_scipy
def test_nancorr(self):
targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
Expand All @@ -568,6 +569,7 @@ def test_nancorr(self):
targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson")

@td.skip_if_no_scipy
def test_nancorr_pearson(self):
targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
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1 change: 1 addition & 0 deletions pandas/tests/window/moments/test_moments_rolling.py
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Expand Up @@ -1097,6 +1097,7 @@ def test_rolling_cov(self):
def test_rolling_cov_pairwise(self):
self._check_pairwise_moment("rolling", "cov", window=10, min_periods=5)

@td.skip_if_no_scipy
def test_rolling_corr(self):
A = self.series
B = A + randn(len(A))
Expand Down