Merge pull request #133 from jwilk-forks/spelling

Fix a few typos in docs

Author: grantjenks

docs/performance-scale.rst

@@ -248,7 +248,7 @@ winnings which would conclude incorrectly an expected value of zero.
 More practically, consider the list data type in CPython. Lists grow and shrink
 as necessary but the underlying implementation is restricted to static
 allocations. For this reason, lists are often over-allocated so that most
-appends may occur immediately. Occassionally, the list must be reallocated and
+appends may occur immediately. Occasionally, the list must be reallocated and
 possibly copied, which takes linear time. If we sampled performance by
 initializing lists of various sizes and appending an element, we may never
 observe a resize operation and so over-estimate performance.

docs/performance.rst

@@ -41,7 +41,7 @@ were found with various list, set, and dict implementations.
    Implemented in C. MIT License. Last updated April, 2017.  `bintrees on
    PyPI`_
 
-3. *sortedmap* -- Provides a fast, C++ implemenation for dict data types.  Uses
+3. *sortedmap* -- Provides a fast, C++ implementation for dict data types.  Uses
    the C++ standard library `std::map` data structure which is usually a
    red-black tree. Last updated February, 2016. `sortedmap on PyPI`_
 

docs/pycon-2016-talk.rst

@@ -105,7 +105,7 @@ sorted collections`_ library with `sorted list`_, `sorted dictionary`_, and
 C-extensions. It’s Python 2 and Python 3 compatible. It’s fully-featured. And
 it’s extensively tested with 100% coverage and hours of stress.
 
-`Performance`_ is a feature. That means graphs. Lot’s of them. There are 189
+`Performance`_ is a feature. That means graphs. Lots of them. There are 189
 performance graphs in total. Let’s look at a few of them together.
 
 Here’s the performance of adding a random value to a sorted list. I’m comparing
@@ -127,7 +127,7 @@ improves with scale. You can see it there in blue. It starts in the middle of
 the pack and has a lesser slope than competitors.
 
 In short, `Sorted Containers`_ is kind of like a `B-tree`_ implementation. That
-means you can configure the the fan-out of nodes in the tree. We call that the
+means you can configure the fan-out of nodes in the tree. We call that the
 load parameter and there are extensive performance graphs of three different
 `load parameters`_.
 
@@ -146,11 +146,11 @@ for most scenarios. It’s a very sane default.
 
 In addition to comparisons and load-factors, I also `benchmark
 runtimes`_. Here’s CPython 2.7, CPython 3.5 and `PyPy`_ version 5. You can see
-where the the just-in-time compiler, the jit-compiler, kicks in. That’ll make
+where the just-in-time compiler, the jit-compiler, kicks in. That’ll make
 `Sorted Containers`_ another ten times faster.
 
 Finally, I made a survey in 2015 on `Github`_ as to how people were using
-sorted collections. I noticed patterns like priority queues, mutli-sets,
+sorted collections. I noticed patterns like priority queues, multi-sets,
 nearest-neighbor algorithms, etc.
 
 This is the priority queue workload which spends 40% of its time adding

docs/sf-python-2015-lightning-talk.rst

@@ -38,7 +38,7 @@ Containers. The right answer is pure-Python. It's Python 2 and Python 3
 compatible. It's fast. It's fully-featured. And it's extensively tested with
 100% coverage and hours of stress.
 
-Performance is a feature. That means graphs. Lot's of them. Here's the
+Performance is a feature. That means graphs. Lots of them. Here's the
 performance of creating a sorted list from random numbers. I benchmark Sorted
 Containers against every worthy competitor I can find.