diff --git a/Doc/library/random.rst b/Doc/library/random.rst
index 098684d7270ffa..c192919ac62e54 100644
--- a/Doc/library/random.rst
+++ b/Doc/library/random.rst
@@ -404,8 +404,8 @@ Alternative Generator
    Class that implements the default pseudo-random number generator used by the
    :mod:`random` module.
 
-   .. deprecated:: 3.9
-      In the future, the *seed* must be one of the following types:
+   .. deprecated-removed:: 3.9 3.11
+      Formerly the *seed* could be any hashable object.  Now it is limited to:
       :class:`NoneType`, :class:`int`, :class:`float`, :class:`str`,
       :class:`bytes`, or :class:`bytearray`.
 
@@ -423,7 +423,7 @@ Notes on Reproducibility
 ------------------------
 
 Sometimes it is useful to be able to reproduce the sequences given by a
-pseudo-random number generator.  By re-using a seed value, the same sequence should be
+pseudo-random number generator.  By reusing a seed value, the same sequence should be
 reproducible from run to run as long as multiple threads are not running.
 
 Most of the random module's algorithms and seeding functions are subject to
diff --git a/Lib/random.py b/Lib/random.py
index 3c4291f6a652a0..586c3f7f9da938 100644
--- a/Lib/random.py
+++ b/Lib/random.py
@@ -24,7 +24,6 @@
            negative exponential
            gamma
            beta
-           binomial
            pareto
            Weibull
 
@@ -33,6 +32,11 @@
            circular uniform
            von Mises
 
+    discrete distributions
+    ----------------------
+           binomial
+
+
 General notes on the underlying Mersenne Twister core generator:
 
 * The period is 2**19937-1.
@@ -731,6 +735,26 @@ def betavariate(self, alpha, beta):
             return y / (y + self.gammavariate(beta, 1.0))
         return 0.0
 
+    def paretovariate(self, alpha):
+        """Pareto distribution.  alpha is the shape parameter."""
+        # Jain, pg. 495
+
+        u = 1.0 - self.random()
+        return u ** (-1.0 / alpha)
+
+    def weibullvariate(self, alpha, beta):
+        """Weibull distribution.
+
+        alpha is the scale parameter and beta is the shape parameter.
+
+        """
+        # Jain, pg. 499; bug fix courtesy Bill Arms
+
+        u = 1.0 - self.random()
+        return alpha * (-_log(u)) ** (1.0 / beta)
+
+
+    ## -------------------- discrete  distributions  ---------------------
 
     def binomialvariate(self, n=1, p=0.5):
         """Binomial random variable.
@@ -816,25 +840,6 @@ def binomialvariate(self, n=1, p=0.5):
                 return k
 
 
-    def paretovariate(self, alpha):
-        """Pareto distribution.  alpha is the shape parameter."""
-        # Jain, pg. 495
-
-        u = 1.0 - self.random()
-        return u ** (-1.0 / alpha)
-
-    def weibullvariate(self, alpha, beta):
-        """Weibull distribution.
-
-        alpha is the scale parameter and beta is the shape parameter.
-
-        """
-        # Jain, pg. 499; bug fix courtesy Bill Arms
-
-        u = 1.0 - self.random()
-        return alpha * (-_log(u)) ** (1.0 / beta)
-
-
 ## ------------------------------------------------------------------
 ## --------------- Operating System Random Source  ------------------