1. json and pickle

json is used for conversion between strings and Python data types
pickle is used for conversion between python-specific types and python-specific data types
json and pickle provide four methods: dumps,dump,loads,load

##json
dumps()   ##Convert to a string
loads()   ##Converting json-coded strings to python data structures
dump()    ##Converting strings and storing them in files
load()    ##Read data from data files and convert json-encoded strings into python data structures

>>> print(json.dumps(['aa', 'bb', 'cc']))
["aa", "bb", "cc"]
>>> print(json.loads('["aa", "bb", "cc"]'))
['aa', 'bb', 'cc']


##pickle
dumps()   ##Converting data in a special form to a string that only python knows
loads()   ##Converting pickle data to python data structure
dump()    ##Convert the data into a string that only python knows, and write it to a file
load()    ##Read data from a data file and convert it to python's data structure

>>> print(pickle.dumps(['aa', 'bb', 'cc']))
b'\x80\x03]q\x00(X\x02\x00\x00\x00aaq\x01X\x02\x00\x00\x00bbq\x02X\x02\x00\x00\x00ccq\x03e.'
>>> print(pickle.loads(b'\x80\x03]q\x00(X\x02\x00\x00\x00aaq\x01X\x02\x00\x00\x00bbq\x02X\x02\x00\x00\x00ccq\x03e.'))
['aa', 'bb', 'cc']

2,random

random.random()           ##Random decimal between 0 and 1
random.uniform(1,3)       ##Random decimal numbers between 1 and 3
random.randint(1,5)       ##Random integers between [1,5]
random.randrange(1,10,2)  ##[1,10] Random odd numbers with intervals of 2
random.choice([1,'23',[4,5]])    ##Random selection of a return
random.sample([1,'23',[4,5]],2)  ##Random selection of multiple returns, the number of returns is the second parameter of the function
random.shuffle([1,3,5,7,9])  ##Disturbing the order of lists


##Return validation code
import random

def v_code():

    code = ''
    for i in range(5):

        num=random.randint(0,9)
        alf=chr(random.randint(65,90))
        add=random.choice([num,alf])
        code="".join([code,str(add)])

    return code

print(v_code())

3,hashlib

hashlib provides common summary algorithms, such as MD5, SHA1, and so on.

##md5 computing
md5 = hashlib.md5("key".encode("utf8"))   ##You can add a secret key, which can be a user id, so that even if the password is the same, the encrypted md5 will be different.
md5.update(b'love fly')
print(md5.hexdigest())

##SHA1
sha1 = hashlib.sha1("key".encode("utf8"))
sha1.update(b'love fly')
print(sha1.hexdigest())

##Calculate MD5
>>> import hashlib
>>> md5 = hashlib.md5()
>>> md5.update(b'love fly')
>>> print(md5.hexdigest())
61976ec704dbce25ccb37ecacef1e4d6

##If the amount of data is large, update() can be called several times in blocks, and the final result is the same.
>>> import hashlib
>>> md5 = hashlib.md5()
>>> md5.update(b'love')
>>> md5.update(b' fly')
>>> print(md5.hexdigest())
61976ec704dbce25ccb37ecacef1e4d6

4,collections

ollections is a Python built-in collection module, which provides many useful collection classes and is a complement to python's default data structure.

## namedtuple is a function that creates a custom tuple object, specifies the number of tuple elements, and can refer to an element of a tuple with attributes rather than indexes.
>>> from collections import namedtuple
>>> colltuple = namedtuple('colltuple', ['x','y'])
>>> col = colltuple('yong','fly')
>>> col.x
'yong'
>>> col.y
'fly'
Based on the built-in data types (dict, list, set, tuple), the collections module also provides several additional data types: Counter, deque, defaultdict, namedtuple and Ordered Dict.
1.namedtuple: Generate tuple that can access element content by name
 2.deque: Double-ended queue that can quickly append and push objects from the other side
 3.Counter: Counter, mainly used for counting
 4.OrderedDict: Ordered Dictionary
 5.defaultdict: A dictionary with default values


# # deque: list inserts and deletes elements very slowly, and when there is a large amount of data, insertion and deletion efficiency is very low. Deque is a bidirectional list for efficient insertion and deletion operations, suitable for queues and stacks.
>>> from collections import deque
>>> q = deque(['a','b','c'])
> Q. append ('d') # # defaults to add the last item in the list
 > Q. appendleft ('e') # added to the first item in the list
>>> q
deque(['e', 'a', 'b', 'c', 'd'])
> Q. pop ()# # Default Delete the Last Element of the List
'd'
> Q. popleft ()# # Delete the first element of the list
'e'
>>> q
deque(['a', 'b', 'c'])

When using a dictionary ## defaultdict, if the key referenced does not exist, a KeyError will be thrown. If you want the key to not exist, you can return a default value, you can use defaultdict. 
    X=defaultdict(lambda: 'N/A')
Application of OrderedDict Ordered Dictionary. OrderedDict is ordered in the order of insertion, not KEY.
A simple counter # Counter, for example, counts the number of characters that appear.