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This article introduces the relevant knowledge of "what are the common data structures of Python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Learning purpose of data structures commonly used in Python

This project, try to use the most concise text, with the help of typical cases to inventory the data structures commonly used in Python.

If you are still in the entry stage of Python, you usually only need to master data structures such as list, tuple, set, and dict to use them flexibly.

However, with the deepening of learning, usually encounter the actual scene becomes complex, it is necessary to understand the Python built-in more powerful data structures deque, heapq, Counter, OrderedDict, defaultDict, ChainMap, master them, often you can write less code and more efficient implementation of the function.

Learning goal

The first stage of learning data structures: master their basic usage and use them to solve some basic problems

The second stage of learning: know which scenario to choose which data structure is the most appropriate, to solve the problem

The third stage of learning: understand the source code behind the built-in data structure, connect with the knowledge of "algorithm and data structure", and get through the governor's second pulse.

Based on the three defined phases, the following 10 most commonly used data structures are summarized:

1. List

Basic usage needless to say, there is a separate topic in front detailing the usage list of list.

Using scene list is used in scenarios that need to be queried or modified, and is not good at scenarios that require frequent insertion and deletion of elements.

The implementation principle list corresponds to the linear table of the data structure. The list length does not need to be specified in the initial state. Dynamic expansion is started when the inserted element exceeds the initial length, especially at the beginning of the list when deleted, and the time complexity is O (n).

2. Tuple

A tuple is a special list that does not allow adding or deleting elements, that is, once it is created, it will never be added, deleted, or modified.

Basic usage tuples are widely used in packaging and unpacking, for example, when a function has multiple return values, it is packaged into a tuple, and when assigned to a variable to the left of the equal sign, it is unpacked.

In [22]: tweak 1 Out 2 In [23]: type (t) Out [23]: tuple

Actually create a tuple instance

Use the scene if you are sure that your object will not be modified later, you can boldly use tuples. Why? This is particularly important because tuple instances are more memory-efficient than list.

In [24]: from sys import getsizeof In [25]: getsizeof (list ()) Out [25]: 72 # an list instance occupies 72 bytes In [26]: getsizeof (tuple ()) Out [26]: 56 # one tuple instance occupies 56 bytes

So by creating 100 instances, tuple can save 1K multi-bytes.

3. Set

Basic usage set is a data structure that cannot contain repeating elements, and this feature is naturally used in list deduplication.

In [27]: a = [3 In [28]: set (a) Out [28]: {2,3,5}

In addition, it is known that the set structure can be used for the intersection, union, difference and other operations of two set instances.

In [29]: a = {2jue 3je 5} In [30]: B = {3jue 4 6 In 2} In [31]: a.intersection (b) # find the intersection Out [31]: {2,3}

Use this structure if you just want to cache some element values and require that the element values cannot be repeated. And set allows the addition and deletion of elements, and the efficiency is very high.

The implementation principle set internally hashes the value as an index, and then obtains the data according to the index, so the effect of deleting, adding and querying elements is very high.

4. Dict

Basic usage dict is one of the most frequently used data structures in Python. Dictionaries are created through dict functions, {} writing, dictionary generation, etc., adding, deleting and querying elements are very efficient.

D = {'axiaqiang for for areco b in zip ([' aplomb']) (1) In [39]: d Out [39]: {'averse: 1,' baked: 2}

The use of scene dictionary is especially suitable for scenarios with many queries, and the time complexity is O (1). For example, when solving the sum of two numbers in leetcode's first question, the O (1) query time complexity of dict will be used.

At the same time, information such as attribute values in the Python class is also cached in the typical data structure of the word _ _ dict__.

However, it is worth noting that the number of bytes consumed by dict is 3 or 4 times that of list and tuple, so scenarios with demanding memory requirements should be carefully considered.

In [40]: getsizeof (dict ()) Out [40]: 248

The implementation principle dictionary is a hash table with key-value pairs saved.

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