Python module knowledge 4: serialization of Json/pickle and jsonpickle

Python module knowledge 4: serialization of Json/pickle and jsonpickle

Serialization and deserialization

Serialization: converts the basic data type of Python to a string.

Deserialization: converts a string to the basic data type of Python.

Two modules used for serialization in Python:

• Json is used for conversion between [String] and [python basic data type]. Because the string is common in various languages, json is more suitable for cross-language conversion; however, only dict, list, tuple, str, int, flost, True, and False are supported.

• Pickle is used for conversion between [python-specific types] and [python Basic Data Types]. It supports all types and is more suitable for all types of serialization, such as object-oriented

The Json module provides four functions: dumps, dump, loads, and load.

The pickle module provides four functions: dumps, dump, loads, and load.

About json:

• JSON: JavaScript Object Notation (JavaScript Object Notation ).

• JSON is the syntax for storing and exchanging text information. Similar to XML.

• JSON is smaller, faster, and easier to parse than XML

Case 1: Double quotation marks (such as li = ["a", "B"]) must be used when strings are involved in the dumps and loads json conversion process. instead of li = ['A', 'B']

Import json
Dic = {"k1": "v1", "k2": "v2 "}
Print ('dic ', dic, type (dic ))
Result = json. dumps (dic) # convert the basic python data type to a string
Print ('result', result, type (result ))
Dic2 = json. loads (result) # convert a string to a basic data type,
Print ('dic2', dic2, type (dic2 ))

Execution result:

Case 2: Reading and Writing data in json, dump, load

Import json
Li = ["a", "B"]
Json. dump (li, open ('db', 'w') # execution result. li content is written to the db file.
R = json. load (open ('db', 'R') # file read Operations
Print (r, type (r ))

Execution result:

['a', 'b'] <class 'list'>

Case 3: Obtain weather-related json data using the weather API

Import requests
Import json
R = requests. get ('HTTP: // wthrcdn.etouch.cn/weather_mini? City = Beijing ')
R. encoding = 'utf-8'
Dic = json. loads (r. text)
Print (dic)

Execution result:

Application of pickle:

Case 4: dumps and loads of pickle

Import pickle
Dic = {"k1": "v1", "k2": "v2 "}
Print ('dic ', dic, type (dic ))
Result = pickle. dumps (dic) # convert the basic python data type into bytes
Print ('result', result, type (result ))
Dic2 = pickle. loads (result) # convert string to basic data type
Print ('dic2', dic2, type (dic2 ))

Execution result:

Case 5-1: pickle writes and exports files. type must be added with B

Import pickle

Li = ["a", "B"]
Pickle. dump (li, open ('hh', 'wb') # Add B to the type
R = pickle. load (open ('hh', 'rb') # to read a file, add B
Print (r, type (r ))

Execution result:

Case 5-2: modify the content of the import File

Import pickle
Account_info = {'a1': ['hh', 222,20],
'A2 ': ['rr', 444,40]
}
Pickle. dump (account_info, open ('Acc. pkl', 'wb '))
T = pickle. load (open ('Acc. pkl', 'rb '))
Print ('first import', t, type (t ))

# Modify a value in the dictionary, store it again, and call
Account_info ['a2 '] [1] = 555
Account_info ['a2 '] [2] = 50
Pickle. dump (account_info, open ('Acc. pkl', 'wb '))
F = pickle. load (open ('Acc. pkl', 'rb '))
Print ("Modify value", f, type (f ))
# Add a new field
Account_info ['a3 '] = ['ppp', 666, 60]
Pickle. dump (account_info, open ('Acc. pkl', 'wb '))
Y = pickle. load (open ('Acc. pkl', 'rb '))
Print ("Add new value", y, type (y ))

Case 5-3: How to Use pickle of an object

Case 5-4: Use pickle on jupyter

New_data.to_pickle ('C: \ data_0921.pickle ')

New_data = pd. read_pickle ('C: \ data_0921.pickle ')

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