Merge branch 'numpy:main' into crosslink-gitpod

3 files changed, 11 insertions, 7 deletions

diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py
index 72d8e9de4..5140ffa61 100644
--- a/numpy/lib/index_tricks.py
+++ b/numpy/lib/index_tricks.py
@@ -631,7 +631,8 @@ class ndindex:
 
     Examples
     --------
-    # dimensions as individual arguments
+    Dimensions as individual arguments
+    
     >>> for index in np.ndindex(3, 2, 1):
     ...     print(index)
     (0, 0, 0)
@@ -641,7 +642,8 @@ class ndindex:
     (2, 0, 0)
     (2, 1, 0)
 
-    # same dimensions - but in a tuple (3, 2, 1)
+    Same dimensions - but in a tuple ``(3, 2, 1)``
+    
     >>> for index in np.ndindex((3, 2, 1)):
     ...     print(index)
     (0, 0, 0)
diff --git a/numpy/lib/polynomial.py b/numpy/lib/polynomial.py
index 56fcce621..23021cafa 100644
--- a/numpy/lib/polynomial.py
+++ b/numpy/lib/polynomial.py
@@ -489,8 +489,11 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
         default) just the coefficients are returned, when True diagnostic
         information from the singular value decomposition is also returned.
     w : array_like, shape (M,), optional
-        Weights to apply to the y-coordinates of the sample points. For
-        gaussian uncertainties, use 1/sigma (not 1/sigma**2).
+        Weights. If not None, the weight ``w[i]`` applies to the unsquared
+        residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are
+        chosen so that the errors of the products ``w[i]*y[i]`` all have the
+        same variance.  When using inverse-variance weighting, use
+        ``w[i] = 1/sigma(y[i])``.  The default value is None.
     cov : bool or str, optional
         If given and not `False`, return not just the estimate but also its
         covariance matrix. By default, the covariance are scaled by
@@ -498,7 +501,7 @@ def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
         to be unreliable except in a relative sense and everything is scaled
         such that the reduced chi2 is unity. This scaling is omitted if
         ``cov='unscaled'``, as is relevant for the case that the weights are
-        1/sigma**2, with sigma known to be a reliable estimate of the
+        w = 1/sigma, with sigma known to be a reliable estimate of the
         uncertainty.
 
     Returns
diff --git a/numpy/lib/tests/test_regression.py b/numpy/lib/tests/test_regression.py
index 94fac7ef0..373226277 100644
--- a/numpy/lib/tests/test_regression.py
+++ b/numpy/lib/tests/test_regression.py
@@ -64,8 +64,7 @@ class TestRegression:
     def test_mem_string_concat(self):
         # Ticket #469
         x = np.array([])
-        with pytest.warns(FutureWarning):
-            np.append(x, 'asdasd\tasdasd')
+        np.append(x, 'asdasd\tasdasd')
 
     def test_poly_div(self):
         # Ticket #553