Shulou(Shulou.com)06/01 Report--

This article will explain in detail the example analysis of serialization and deserialization in Python. The editor thinks it is very practical, so I share it with you for reference. I hope you can get something after reading this article.

First know serialization and deserialization what is serialization?

Generally speaking, serialization is to convert the information of objects or data structures through certain rules, which can achieve the effect of file storage or network transmission. From the study of the previous chapter, we know that if you want to store or transfer over the network, the final data type is a string, and these strings need to be strings with certain rules.

And the built-in module we're going to learn today-> json can be used for file storage or network transfer, which is also the role of serialization.

Deserialization means that the string generated by the serialization rule is then inverted to the original data type, which is deserialization.

Here we can introduce the conversion between string and byte to understand serialization and deserialization, string and byte type conversion between the commonly used encode () function, and decode () function, respectively represent encoding and decoding, so there must be decoding. When applied to serialization, since there is serialization, there must be a corresponding deserialization.

Serializable data type

Which data types can be serialized and which are not serializable?

Serializable: number, str, list, tuple, dict [dictionary is the most commonly used serialization data type]

Non-serializable: class, def (functions and instantiated objects), set cannot be serialized

Json in Python

Json module is a universal serialization module, through which the serialization and deserialization of generalization can be completed. Why it's universal is because almost all programming languages have json modules, and their rules for serialization and deserialization are the same.

So what we serialize in Python can be deserialized in any other programming language and use the raw data, which is the general meaning.

Dumps () and loads () functions

The most important functions in json-the dumps () and loads () functions

Method name parameter introduction example return value dumpsobj object serialization json.dumps ([1,2,3]) string loadsstr deserialization Json.loads ('[1,2,3]') raw data type serializable data type demonstration case

The demo is as follows:

Import jsonint_test = 666 # defines five data types of integer, string, list, tuple and dictionary, which are used for serialization testing str_test = 'test_string'list_test = [1,2,3] tuple_test = (4,5,6) dict_test = {' Name': 'Tony. Stark', 'Sex':' male'} int_test_json = json.dumps (int_test) # serialize the above five data types str_test_json = json.dumps (str_test) list_test_json = json.dumps (list_test) tuple_test_json = json.dumps (tuple_test) dict_test_json = json.dumps (dict_test)

Execute the above test script on the Treminal terminal, as shown below:

Here we focus on the serialization results of dictionary types.

In [7]: dict_test_jsonOut [7]:'{"Name": "\\ u6258\\ u5c3c.\\ u53f2\\ u5854\\ u514b", "Sex": "\\ u7537"}'

From the execution result, we can see the data type of the dictionary type, after serialization. When the dictionary becomes a string, the single quotation marks in the dictionary become double quotation marks, the Chinese also becomes bit type, and encode is carried out. (this is a standard for serialization)

Why do we say that dictionary types are very serialized? Actually, json is not only a standard, but also a file format. For example, the .py format file we write the script is the python file container; the .txt format file is the ordinary text file container; similarly, the .json format file is also the file container, and the style (format) stored in the json file is the serialization format of the dictionary type.

Next, let's try to deserialize the above five test number types and see what happens.

_ int_test_json = json.loads (int_test_json) _ str_test_json = json.loads (str_test_json) _ list_test_json = json.loads (list_test_json) _ tuple_test_json = json.loads (tuple_test_json) _ dict_test_json = json.loads (dict_test_json)

Execute the above test script on the Treminal terminal, as shown below:

Highlight: when a tuple type is serialized and then restored by deserialization, it becomes a list data type. This is because the tuple type is a unique data type in the python language. As a general format, json cannot recognize the tuple type. Therefore, when serializing a tuple type, the tuple type is first converted to a list, and then serialized; similarly, when deserialization is carried out, the serialized tuple type is converted to a list type. (conversion of types does not affect the use of data)

Serialization and deserialization of bool and None types

Examples are as follows:

Print (json.dumps (True)) # > > output result: trueprint (json.dumps (False)) # > > output result: falseprint (json.dumps (None)) # > output result: null

Judging from the above running results, after serialization, the bool type becomes lowercase true, false; and the None type becomes lowercase null.

The reason for this is that in most programming languages, bool types are lowercase true, false. Json, as a general serialization module, also follows this rule. (lowercase true and false are still string types. )

Next, we deserialize the bool and None types after serialization.

Print (json.loads (json.dumps (None)) # > > output result: Noneprint (json.loads (json.dumps (True) # > > output result: Trueprint (json.loads (json.dumps (False) # > output result: False

From the execution result, we can see that after deserialization, the bool and None types are restored to the python readable state.

Pickle in Python

Pickle module and json module can be serialized and deserialized, the difference is that pickle is Python built-in serialization module, and not as general as json, it can only be used for python itself, other languages may not be able to deal with, but the performance of pickle module is better than json. If it is used only for python itself, the pckle module is still a good choice.

Introduction of dumps () and loads () function method name parameters return example return value dumpsobj object serialization json.dumps ([1,2,3]) bit loadsstr deserialization Json.loads ('[1,2,3]) raw data type

Note: unlike json, the dumps () function of the pickle module returns the byte type, while the loads () function only supports deserialization of pickle sequences of type byte.

Serialization and deserialization exercises of pickle module

The use of the pickle module is exactly the same as that of the json module, so let's not demonstrate too much here, just for the dict type.

Json Module-Serialization small Battle

Demand:

Create an empty file for test.json.

Define a write function to write the contents of the dict data type to the test.json file

Define a read function that deserializes and reads the contents written to the test.json file

# coding:utf-8import jsondata = {'name':' Tony Stark', 'age': 52,' top': 185} def read (path): # define the read () function Read the test.json file (the object is deserialized) with open (path,'r') as f: data = f.read () return json.loads (data) def write (path, data): # define the write () function Write data to the test.json file with open (path,'w') as f: if isinstance (data, dict): # to determine whether data is a dictionary type. If not, take the initiative to throw an exception _ data = json.dumps (data) f.write (_ data) else: raise TypeError ('\ 'data\' is not a dictionary type of data') return Trueif _ _ name__ = ='_ _ main__': write ('test.json' Data) result = read ('test.json') print (result) result [' Sex'] = 'Man' # add {' Sex': 'Max'} key value pair write (' test.json', result) # write the added key value pair to the test.json file result_test_json = read ('test.json') print (result_test_json)

The implementation results are as follows:

This is the end of this article on "sample analysis of serialization and deserialization in Python". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.