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Python supports most object oriented programming (OOP) techniques. It allows polymorphism, not only within a class hierarchy but also by duck typing. Any object can be used for any type, and it will work so long as it has the proper methods and attributes. And everything in Python is an object, including classes, functions, numbers and modules.
In Lua, "table" is a fundamental type that can be used either as an array (numerical index, fast) or as an associative array. The keys and values can be of any type, except nil. The following focuses on non-numerical indexes. A table literal is written as { value, key = value, [index] = value, ["non id string"] = value }. For example:
The most frequently used general-purpose implementation of an associative array is with a hash table: an array combined with a hash function that separates each key into a separate "bucket" of the array. The basic idea behind a hash table is that accessing an element of an array via its index is a simple, constant-time operation.
Python dictionaries (a form of associative array) can also be directly iterated over, when the dictionary keys are returned; or the items() method of a dictionary can be iterated over where it yields corresponding key,value pairs as a tuple:
The C++ Standard Library's associative containers (std::unordered_map and std::map) use operator[] to get the value associated to a key. If there is nothing associated to this key, it will construct it and value initialize [4] [unreliable source] [failed verification] the value.
In computing, a hash table is a data structure that implements an associative array, also called a dictionary or simply map; an associative array is an abstract data type that maps keys to values. [2] A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value ...
The array, set and dictionary binary types are made up of pointers - the objref and keyref entries - that index into an object table in the file. This means that binary plists can capture the fact that - for example - a separate array and dictionary serialized into a file both have the same data element stored in them.
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.