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PERF: Series.corr/Series.cov for EA dtypes #52502

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Merged
merged 2 commits into from
Apr 7, 2023
Merged

PERF: Series.corr/Series.cov for EA dtypes #52502

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PERF: Series.corr/Series.cov for EA dtypes
lukemanley Apr 6, 2023
4c9aad0
whatsnew
lukemanley Apr 6, 2023
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v2.1.0.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,7 @@ Performance improvements
- Performance improvement in :func:`factorize` for object columns not containing strings (:issue:`51921`)
- Performance improvement in :class:`Series` reductions (:issue:`52341`)
- Performance improvement in :meth:`Series.to_numpy` when dtype is a numpy float dtype and ``na_value`` is ``np.nan`` (:issue:`52430`)
- Performance improvement in :meth:`Series.corr` and :meth:`Series.cov` for extension dtypes (:issue:`52502`)
-

.. ---------------------------------------------------------------------------
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8 changes: 4 additions & 4 deletions pandas/core/series.py
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Expand Up @@ -2701,8 +2701,8 @@ def corr(
if len(this) == 0:
return np.nan

this_values = np.asarray(this._values)
other_values = np.asarray(other._values)
this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
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any chance this gets weird if we have non-numeric?

is this the right thing to do if we have NAs?

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any chance this gets weird if we have non-numeric?

This raises for non-numeric that cannot be cast to float (same as behavior as main branch):

ser = pd.Series(["a", "b"])
ser.corr(ser)

# ValueError: could not convert string to float: 'a'

It allows non-numeric that can be cast to float (same behavior as main branch):

ser = pd.Series(["1", "2"])
ser.corr(ser)

# 0.9999999999999999

is this the right thing to do if we have NAs?

NAs get dropped either way in the underlying nanops.py functions nancorr and nancov.

FYI, this is the same approach used in DataFrame.corr:

pandas/pandas/core/frame.py

Line 10381 in 0e39016

mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)

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FYI, this is the same approach used in DataFrame.corr:

good point. works for me

other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)

if method in ["pearson", "spearman", "kendall"] or callable(method):
return nanops.nancorr(
Expand Down Expand Up @@ -2759,8 +2759,8 @@ def cov(
this, other = self.align(other, join="inner", copy=False)
if len(this) == 0:
return np.nan
this_values = np.asarray(this._values)
other_values = np.asarray(other._values)
this_values = this.to_numpy(dtype=float, na_value=np.nan, copy=False)
other_values = other.to_numpy(dtype=float, na_value=np.nan, copy=False)
return nanops.nancov(
this_values, other_values, min_periods=min_periods, ddof=ddof
)
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4 changes: 2 additions & 2 deletions pandas/tests/frame/methods/test_cov_corr.py
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Expand Up @@ -365,8 +365,8 @@ def test_corrwith_mixed_dtypes(self, numeric_only):
tm.assert_series_equal(result, expected)
else:
with pytest.raises(
TypeError,
match=r"Could not convert \['a' 'b' 'c' 'd'\] to numeric",
ValueError,
match="could not convert string to float",
):
df.corrwith(s, numeric_only=numeric_only)

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