Neaten-up random module docs.
diff --git a/Doc/library/random.rst b/Doc/library/random.rst
index 10c2f3c..ba369e1 100644
--- a/Doc/library/random.rst
+++ b/Doc/library/random.rst
@@ -233,41 +233,18 @@
parameter.
-Alternative Generators:
+Alternative Generator:
.. class:: SystemRandom([seed])
Class that uses the :func:`os.urandom` function for generating random numbers
from sources provided by the operating system. Not available on all systems.
- Does not rely on software state and sequences are not reproducible. Accordingly,
+ Does not rely on software state, and sequences are not reproducible. Accordingly,
the :meth:`seed` method has no effect and is ignored.
The :meth:`getstate` and :meth:`setstate` methods raise
:exc:`NotImplementedError` if called.
-Examples of basic usage::
-
- >>> random.random() # Random float x, 0.0 <= x < 1.0
- 0.37444887175646646
- >>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0
- 1.1800146073117523
- >>> random.randint(1, 10) # Integer from 1 to 10, endpoints included
- 7
- >>> random.randrange(0, 101, 2) # Even integer from 0 to 100
- 26
- >>> random.choice('abcdefghij') # Choose a random element
- 'c'
-
- >>> items = [1, 2, 3, 4, 5, 6, 7]
- >>> random.shuffle(items)
- >>> items
- [7, 3, 2, 5, 6, 4, 1]
-
- >>> random.sample([1, 2, 3, 4, 5], 3) # Choose 3 elements
- [4, 1, 5]
-
-
-
.. seealso::
M. Matsumoto and T. Nishimura, "Mersenne Twister: A 623-dimensionally
@@ -280,6 +257,7 @@
random number generator with a long period and comparatively simple update
operations.
+
Notes on Reproducibility
========================
@@ -297,11 +275,34 @@
sequence when the compatible seeder is given the same seed.
-.. _random-examples:
-
Examples and Recipes
====================
+Basic usage::
+
+ >>> random.random() # Random float x, 0.0 <= x < 1.0
+ 0.37444887175646646
+
+ >>> random.uniform(1, 10) # Random float x, 1.0 <= x < 10.0
+ 1.1800146073117523
+
+ >>> random.randrange(10) # Integer from 0 to 9
+ 7
+
+ >>> random.randrange(0, 101, 2) # Even integer from 0 to 100
+ 26
+
+ >>> random.choice('abcdefghij') # Single random element
+ 'c'
+
+ >>> items = [1, 2, 3, 4, 5, 6, 7]
+ >>> random.shuffle(items)
+ >>> items
+ [7, 3, 2, 5, 6, 4, 1]
+
+ >>> random.sample([1, 2, 3, 4, 5], 3) # Three samples without replacement
+ [4, 1, 5]
+
A common task is to make a :func:`random.choice` with weighted probababilites.
If the weights are small integer ratios, a simple technique is to build a sample
@@ -312,9 +313,9 @@
>>> random.choice(population)
'Green'
-A more general approach is to arrange the weights in a cumulative probability
-distribution with :func:`itertools.accumulate`, and then locate the random value
-with :func:`bisect.bisect`::
+A more general approach is to arrange the weights in a cumulative distribution
+with :func:`itertools.accumulate`, and then locate the random value with
+:func:`bisect.bisect`::
>>> choices, weights = zip(*weighted_choices)
>>> cumdist = list(itertools.accumulate(weights))