diff --git a/Lib/random.py b/Lib/random.py
index 1a0b8f3..76dc416 100644
--- a/Lib/random.py
+++ b/Lib/random.py
@@ -45,8 +45,8 @@
 
 __all__ = ["Random","seed","random","uniform","randint","choice","sample",
            "randrange","shuffle","normalvariate","lognormvariate",
-           "cunifvariate","expovariate","vonmisesvariate","gammavariate",
-           "stdgamma","gauss","betavariate","paretovariate","weibullvariate",
+           "expovariate","vonmisesvariate","gammavariate",
+           "gauss","betavariate","paretovariate","weibullvariate",
            "getstate","setstate","jumpahead"]
 
 NV_MAGICCONST = 4 * _exp(-0.5)/_sqrt(2.0)
@@ -308,29 +308,6 @@
         """
         return _exp(self.normalvariate(mu, sigma))
 
-## -------------------- circular uniform --------------------
-
-    def cunifvariate(self, mean, arc):
-        """Circular uniform distribution.
-
-        mean is the mean angle, and arc is the range of the distribution,
-        centered around the mean angle.  Both values must be expressed in
-        radians.  Returned values range between mean - arc/2 and
-        mean + arc/2 and are normalized to between 0 and pi.
-
-        Deprecated in version 2.3.  Use:
-            (mean + arc * (Random.random() - 0.5)) % Math.pi
-
-        """
-        # mean: mean angle (in radians between 0 and pi)
-        # arc:  range of distribution (in radians between 0 and pi)
-        import warnings
-        warnings.warn("The cunifvariate function is deprecated; Use (mean "
-                      "+ arc * (Random.random() - 0.5)) % Math.pi instead",
-                      DeprecationWarning)
-
-        return (mean + arc * (self.random() - 0.5)) % _pi
-
 ## -------------------- exponential distribution --------------------
 
     def expovariate(self, lambd):
@@ -465,27 +442,6 @@
                     break
             return x * beta
 
-
-    def stdgamma(self, alpha, ainv, bbb, ccc):
-        # This method was (and shall remain) undocumented.
-        # This method is deprecated
-        # for the following reasons:
-        # 1. Returns same as .gammavariate(alpha, 1.0)
-        # 2. Requires caller to provide 3 extra arguments
-        #    that are functions of alpha anyway
-        # 3. Can't be used for alpha < 0.5
-
-        # ainv = sqrt(2 * alpha - 1)
-        # bbb = alpha - log(4)
-        # ccc = alpha + ainv
-        import warnings
-        warnings.warn("The stdgamma function is deprecated; "
-                      "use gammavariate() instead",
-                      DeprecationWarning)
-        return self.gammavariate(alpha, 1.0)
-
-
-
 ## -------------------- Gauss (faster alternative) --------------------
 
     def gauss(self, mu, sigma):
@@ -755,7 +711,6 @@
     _test_generator(N, 'random()')
     _test_generator(N, 'normalvariate(0.0, 1.0)')
     _test_generator(N, 'lognormvariate(0.0, 1.0)')
-    _test_generator(N, 'cunifvariate(0.0, 1.0)')
     _test_generator(N, 'vonmisesvariate(0.0, 1.0)')
     _test_generator(N, 'gammavariate(0.01, 1.0)')
     _test_generator(N, 'gammavariate(0.1, 1.0)')
@@ -786,11 +741,9 @@
 shuffle = _inst.shuffle
 normalvariate = _inst.normalvariate
 lognormvariate = _inst.lognormvariate
-cunifvariate = _inst.cunifvariate
 expovariate = _inst.expovariate
 vonmisesvariate = _inst.vonmisesvariate
 gammavariate = _inst.gammavariate
-stdgamma = _inst.stdgamma
 gauss = _inst.gauss
 betavariate = _inst.betavariate
 paretovariate = _inst.paretovariate