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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