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ENH: Add axis argument to Dataframe.corr #35984

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Added axis argument to Dataframe.corr
kc611 Aug 30, 2020
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Minor Linting Issues
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Debug Modified Dataframe.corr function
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Improved tests
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Update doc/source/whatsnew/v1.2.0.rst
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Merge branch 'issue_35002' of https://github.com/kc611/pandas into is…
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20 changes: 17 additions & 3 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8116,9 +8116,9 @@ def _series_round(s, decimals):
# ----------------------------------------------------------------------
# Statistical methods, etc.

def corr(self, method="pearson", min_periods=1) -> "DataFrame":
def corr(self, method="pearson", min_periods=1, axis=0) -> "DataFrame":
"""
Compute pairwise correlation of columns, excluding NA/null values.
Compute pairwise correlation of rows or columns, excluding NA/null values.

Parameters
----------
Expand All @@ -8140,6 +8140,10 @@ def corr(self, method="pearson", min_periods=1) -> "DataFrame":
to have a valid result. Currently only available for Pearson
and Spearman correlation.

axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' for
row-wise.

Returns
-------
DataFrame
Expand All @@ -8162,12 +8166,22 @@ def corr(self, method="pearson", min_periods=1) -> "DataFrame":
dogs cats
dogs 1.0 0.3
cats 0.3 1.0
>>> df.corr(method=histogram_intersection, axis=1)
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blank line before

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add a comment as well

0 1 2 3
0 1.0 0.3 0.2 0.3
1 0.3 1.0 0.0 0.1
2 0.2 0.0 1.0 0.2
3 0.3 0.1 0.2 1.0
"""
numeric_df = self._get_numeric_data()
cols = numeric_df.columns
axis = numeric_df._get_axis_number(axis)
cols = numeric_df._get_agg_axis(axis)
idx = cols.copy()
mat = numeric_df.to_numpy(dtype=float, na_value=np.nan, copy=False)

if axis == 1:
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don't we have to transpose the results?

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I don't think we do since the result is symmetric

mat = mat.transpose()

if method == "pearson":
correl = libalgos.nancorr(mat, minp=min_periods)
elif method == "spearman":
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9 changes: 9 additions & 0 deletions pandas/tests/frame/methods/test_cov_corr.py
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Expand Up @@ -175,6 +175,15 @@ def test_corr_int(self):
df3.cov()
df3.corr()

@td.skip_if_no_scipy
@pytest.mark.parametrize("meth", ["pearson", "spearman"])
def test_corr_axes(self, meth):
# https://github.com/pandas-dev/pandas/issues/35002
df = pd.DataFrame(np.random.normal(size=(10, 2)))
expected = df.T.corr(meth, axis=0)
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I think it's usually encouraged to explicitly write out the expected DataFrame so that expected doesn't go down the same code path as result

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Yeah I could do that but, wouldn't it just be a test for Dataframe.corr function itself. Since the original operations to be done on matrix itself are left unchanged.

Personally I don't think explicitly writing Dataframe in this case is needed, unless (as you suggested) instead of taking a transpose we implement a workaround involving changing the main function itself.

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OK sure, perhaps wait for others' comments then

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The test comes close to being circular but I think it's probably okay here. In this case it's hard to explicitly construct the expected DataFrame for all methods "from scratch" without either trivial input data or messy juggling of different scipy functions.

result = df.corr(meth, axis=1)
tm.assert_frame_equal(result, expected)

@td.skip_if_no_scipy
@pytest.mark.parametrize(
"nullable_column", [pd.array([1, 2, 3]), pd.array([1, 2, None])]
Expand Down