Algorithms

In this chapter, we will cover the following recipes:

• Searching, sorting, filtering—high-performance searching in sorted containers
• Getting the n^th element of any iterable—grabbing the n^th element of any iterable, generators too
• Grouping similar items—splitting an iterable into groups of similar items
• Zipping—merging together data from multiple iterables into a single iterable
• Flattening a list of lists—converting a list of lists into a flat list
• Producing permutations and—computing all possible permutations of a set of elements
• Accumulating and reducing—applying binary functions to iterables
• Memoizing—speeding up computation by caching functions
• Operators to functions—how to keep references to callables for a Python operator
• Partials—reducing the number of arguments of a function by preapplying...