gh-149244: Support iterator inputs in covariance, correlation, and linear_regression

Doc/library/statistics.rst

@@ -716,8 +716,8 @@ However, for reading convenience, most of the examples show sorted sequences.
    Return the sample covariance of two inputs *x* and *y*. Covariance
    is a measure of the joint variability of two inputs.
 
-   Both inputs must be of the same length (no less than two), otherwise
-   :exc:`StatisticsError` is raised.
+   Both inputs must be sequences or iterables of the same length (no less
+   than two), otherwise :exc:`StatisticsError` is raised.
 
    Examples:
 
@@ -753,8 +753,9 @@ However, for reading convenience, most of the examples show sorted sequences.
    continuous data that doesn't meet the linear proportion requirement for
    Pearson's correlation coefficient.
 
-   Both inputs must be of the same length (no less than two), and need
-   not to be constant, otherwise :exc:`StatisticsError` is raised.
+   Both inputs must be sequences or iterables of the same length (no less
+   than two), and need not to be constant, otherwise :exc:`StatisticsError`
+   is raised.
 
    Example with `Kepler's laws of planetary motion
    <https://en.wikipedia.org/wiki/Kepler's_laws_of_planetary_motion>`_:
@@ -802,8 +803,8 @@ However, for reading convenience, most of the examples show sorted sequences.
    (it is equal to the difference between predicted and actual values
    of the dependent variable).
 
-   Both inputs must be of the same length (no less than two), and
-   the independent variable *x* cannot be constant;
+   Both inputs must be sequences or iterables of the same length (no less
+   than two), and the independent variable *x* cannot be constant;
    otherwise a :exc:`StatisticsError` is raised.
 
    For example, we can use the `release dates of the Monty

Lib/statistics.py

@@ -673,6 +673,8 @@ def covariance(x, y, /):
 
     """
     # https://en.wikipedia.org/wiki/Covariance
+    x = list(x)
+    y = list(y)
     n = len(x)
     if len(y) != n:
         raise StatisticsError('covariance requires that both inputs have same number of data points')
@@ -710,6 +712,8 @@ def correlation(x, y, /, *, method='linear'):
     """
     # https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
     # https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
+    x = list(x)
+    y = list(y)
     n = len(x)
     if len(y) != n:
         raise StatisticsError('correlation requires that both inputs have same number of data points')
@@ -781,6 +785,8 @@ def linear_regression(x, y, /, *, proportional=False):
 
     """
     # https://en.wikipedia.org/wiki/Simple_linear_regression
+    x = list(x)
+    y = list(y)
     n = len(x)
     if len(y) != n:
         raise StatisticsError('linear regression requires that both inputs have same number of data points')

Lib/test/test_statistics.py

@@ -2842,6 +2842,32 @@ def test_correlation_spearman(self):
         with self.assertRaises(ValueError):
             statistics.correlation(reading, mathematics, method='bad_method')
 
+    def test_iterator_inputs(self):
+        x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+        y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
+        expected_cov = statistics.covariance(x, y)
+        expected_cor = statistics.correlation(x, y)
+        # iter() inputs should give same results as list inputs
+        self.assertAlmostEqual(statistics.covariance(iter(x), iter(y)), expected_cov)
+        self.assertAlmostEqual(statistics.correlation(iter(x), iter(y)), expected_cor)
+        # generator expressions should also work
+        self.assertAlmostEqual(
+            statistics.covariance((v for v in x), (v for v in y)), expected_cov
+        )
+        self.assertAlmostEqual(
+            statistics.correlation((v for v in x), (v for v in y)), expected_cor
+        )
+        # ranked method should also accept iterators
+        expected_ranked = statistics.correlation(x, y, method='ranked')
+        self.assertAlmostEqual(
+            statistics.correlation(iter(x), iter(y), method='ranked'), expected_ranked
+        )
+        # mismatched lengths should still raise StatisticsError
+        with self.assertRaises(statistics.StatisticsError):
+            statistics.covariance(iter([1, 2, 3]), iter([1, 2]))
+        with self.assertRaises(statistics.StatisticsError):
+            statistics.correlation(iter([1, 2, 3]), iter([1, 2]))
+
 class TestLinearRegression(unittest.TestCase):
 
     def test_constant_input_error(self):
@@ -2881,6 +2907,26 @@ def test_float_output(self):
         self.assertTrue(isinstance(slope, float))
         self.assertTrue(isinstance(intercept, float))
 
+    def test_iterator_inputs(self):
+        x = [1, 2, 3, 4, 5]
+        y = [2, 4, 6, 8, 10]
+        expected = statistics.linear_regression(x, y)
+        # iter() inputs should give same results as list inputs
+        result = statistics.linear_regression(iter(x), iter(y))
+        self.assertAlmostEqual(result.slope, expected.slope)
+        self.assertAlmostEqual(result.intercept, expected.intercept)
+        # generator expressions should also work
+        result = statistics.linear_regression((v for v in x), (v for v in y))
+        self.assertAlmostEqual(result.slope, expected.slope)
+        self.assertAlmostEqual(result.intercept, expected.intercept)
+        # proportional=True should also accept iterators
+        expected_prop = statistics.linear_regression(x, y, proportional=True)
+        result_prop = statistics.linear_regression(
+            iter(x), iter(y), proportional=True
+        )
+        self.assertAlmostEqual(result_prop.slope, expected_prop.slope)
+        self.assertEqual(result_prop.intercept, 0.0)
+
 class TestNormalDist:
 
     # General note on precision: The pdf(), cdf(), and overlap() methods

Misc/NEWS.d/next/Library/2026-05-01-17-20-47.gh-issue-149244.Rk3mXp.rst

@@ -0,0 +1,3 @@
+:func:`statistics.covariance`, :func:`statistics.correlation`, and
+:func:`statistics.linear_regression` now accept any iterable input,
+consistent with other functions in the :mod:`statistics` module.