There are many artifice in fluent python, and the whole book emphasizes how to maximize the use of the Python standard library. This article introduces many of the infrequently used data types, operations, libraries, etc. of Python, and it should be helpful to get started with Python and to improve your understanding of Python. Now read through a few sympathetic actions:
Divided by the types of elements that can be stored: container sequence and flat sequence
Can be modified by sequence: variable sequence and immutable sequence
Many variants with similar dictionary types are available in the Collections module in the standard library.
Orderdict: This type saves the order when the key is added, so the key iteration order is always the same
Chainmap: The type can hold several different mapping pairs, and when the key is searched, the objects are looked up and down as a whole until the key is found
Counter: This mapping type will give the key an integer technology, each time a key is added to the counter, so this type can be used to count the hash object, or as a multi-set to use.
UserDict: This class actually writes the standard dict with Python again. Typically used instead of dict when programmers want to create their own dict by inheriting Dict. The main reason is that the direct inheritance of native dict is a bug.
Defaultdict: A choice to handle keys that cannot be found
When a key is not in the map, we also want to get a default value. This is defaultdict, which is the subclass of Dict, and implements the missing method.
The implementation of the set and the resulting result1. The key must be hashable: A hashable object must meet the following requirements. (1) The hash() function is supported, and the hash value obtained by the __hash__() method is constant. (2) Support for equality by the __eq__() method. (3) If a == b is true, then hash(a) == hash(b) is also true. All user-defined objects are hashable by default because their hash values are obtained by id() , and And they are all unequal. 2. The dictionary is very expensive in memory (using memory for efficiency). There are two reasons why a tuple can replace a dictionary to save space: (1) Avoiding the space spent on hash tables, (2) There is no need to save the name of the field in the record in each element. 3. The key query is very fast 4. The order of the keys depends on the order of addition 5. Adding a new key to the dictionary may change the order of the existing keys.
Collections.namedtuple can be used to build a tuple with a field name and a class with a name1. The combined elements must be hashable 2. Collection and consumption of memory 3. It can be very efficient to determine whether an element exists in a collection 4. The order of the elements depends on the order in which they are added to the collection 5. Adding elements to the collection may change the order of the elements already in the collection.
Creating a named tuple requires two parameters, one is the class name, and the other is the name of each field of the class. Latter
Can be either an iterative object consisting of several strings, or a string of field names separated by spaces.
When the list is not preferred>>> from collections import namedtuple >>> City = namedtuple('City', 'name country population coordinates') >>> tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667)) >>> tokyo City(name='Tokyo', country='JP', population=36.933, coordinates=(35.689722, 139.691667)) >>> tokyo.population 36.933 >>> tokyo.coordinates (35.689722, 139.691667) >>> tokyo[1] 'JP' >>> City = namedtuple('City_Name', 'name country population coordinates') >>> tokyo = City('Tokyo', 'JP', 36.933, (35.689722, 139.691667)) >>> tokyo City_Name(name='Tokyo', country='JP', population=36.933, coordinates=(35.689722, 139.691667))
When you format the output, the difference between%r and%s is like the difference between the repr () function processing object and the STR () function.
This article highlights some of the different uses of the two:
>> s = 'world'
>> print('hello %s'%s)
Hello world
>> print('hello %r'%s)
Hello 'world'
>> str(s)
'world'
>> repr(s)
"'world'"
2. datetime object in the datetime library
>> from datetime import datetime
>> timeinfo = datetime.today()
>> timeinfo
Datetime.datetime(2016, 6, 7, 21, 17, 34, 925488)
>> type(timeinfo)
Datetime.datetime
>> repr(timeinfo)
'datetime.datetime(2016, 6, 7, 21, 17, 34, 925488)'
>> str(timeinfo)
'2016-06-07 21:17:34.925488'
disassembly function Python opcode
The Python dis module supports disassembly of Python code to generate bytecode instructions.
In[1]: def test(): ... x = 1 ... if x < 3: ... return "yes" ... else: ... return "no" In[2]: dis.dis(test) 2 0 LOAD_CONST 1 (1) 3 STORE_FAST 0 (x) 3 6 LOAD_FAST 0 (x) 9 LOAD_CONST 2 (3) 12 COMPARE_OP 0 (<) 15 POP_JUMP_IF_FALSE 22 4 18 LOAD_CONST 3 ('yes') 21 RETURN_VALUE 6 >> 22 LOAD_CONST 4 ('no') 25 RETURN_VALUE 26 LOAD_CONST 0 (None) 29 RETURN_VALUE >>> def add(a, b = 0): ... return a + b ... >>> >>> dis.dis(add) 2 0 LOAD_FAST 0 (a) 2 LOAD_FAST 1 (b) 4 BINARY_ADD 6 RETURN_VALUE >>>
Class Memoryview (obj) is a python built-in class, if you want to use Memoryview to reference an object, then this object must support buffer protocol, Python3 Zhongyuan (built-in) The bytes and bytearray,memoryview that support the buffer protocol can read and manipulate the same piece of memory in different ways, and the original memory bytes are not moved at random. Similar to the strong turn in C, the advantage is that there is no memory copy.
For example, use Memoryview to modify the data for a short integer signed integer array.
From array import array
From random import random
Numbers = array('h', [-2, -1, 0, 1, 2]) #signed short
Memv = memoryview(numbers) #5 short integer signed arrays to create a memoryview
Print (len(memv)) #print length
Print (memv.tolist()) #convert to list form
Memv_oct = memv.cast('B') #Memory Sharing Convert to unsigned character type
Print (memv_oct.tolist())
Memv_oct[5] = 4 # Assign the position 5 byte to 4
Print (numbers) #because we changed the high byte of the 2-byte integer to 4, the value of this signed integer becomes 1024
The output is as follows:
5 #array length
[-2, -1, 0, 1, 2] #list form display
[254, 255, 255, 255, 0, 0, 1, 0, 2, 0]# double the length to convert to unsigned character type
Array('h', [-2, -1, 1024, 1, 2]) #The original array was modified
ByteArray is a variable (mutable) byte sequence, relative to Str in Python2, but Str is immutable (immutable).
In Python3, because STR is UNICODE-encoded by default, only ByteArray can be accessed by byte.
Comparison of the following two behaviors:
The simple point is that the slicing operation of STR and ByteArray produces new slices of STR and bytearry and copies the data, not after using Memoryview.
Examples in the Python2
A = ' aaaaaa '
b = A[:2] # will produce a new string
Using MemoryviewA = ByteArray (' aaaaaa ')
b = A[:2] # will produce a new ByteArray
B[:2] = ' BB ' # changes to B do not affect a
A
ByteArray (b ' aaaaaa ')
B
ByteArray (b ' BB ')
A = ' aaaaaa '
Ma = Memoryview (a)
Ma.readonly # read-only Memoryview
True
MB = Ma[:2] # does not produce a new string
A = ByteArray (' aaaaaa ')
Ma = Memoryview (a)
Ma.readonly # writable Memoryview
False
MB = Ma[:2] # will not produce a new ByteArray
Mb[:2] = ' BB ' # change to MB is a change to Ma
Mb.tobytes ()
' BB '
Ma.tobytes ()
' Bbaaaa '
There are a variety of callable types in Python, so the callable () function is judged:
Random.shuffle scrambled Sequence>>> abs, str, 13 (<built-in function abs>, <class 'str'>, 13) >>> [callable(obj) for obj in (abs, str, 13)] [True, True, False]
Vim commonly used fast>>> import random >>> a=range(10) >>> random.shuffle(a) >>> a [1, 0, 8, 5, 6, 7, 9, 3, 2, 4] >>> random.shuffle(a) >>> a [7, 5, 6, 2, 1, 8, 9, 0, 3, 4]
Special methods unrelated to operatorsMath Function Description abs() Returns absolute value of a number divmod() Returns quotient and remainder of integer division max() Returns the largest of the given arguments or items in an iterable min() Returns the smallest of the given arguments or items in an iterable pow() Raises a number to a power round() Rounds a floating-point value sum() Sums the items of an iterable Type Conversion Function Description ascii() Returns a string containing a printable representation of an object bin() Converts an integer to a binary string bool() Converts an argument to a Boolean value chr() Returns string representation of character given by integer argument complex() Returns a complex number constructed from arguments float() Returns a floating-point object constructed from a number or string hex() Converts an integer to a hexadecimal string int() Returns an integer object constructed from a number or string oct() Converts an integer to an octal string ord() Returns integer representation of a character repr() Returns a string containing a printable representation of an object str() Returns a string version of an object type() Returns the type of an object or creates a new type object Iterables and Iterators Function Description all() Returns True if all elements of an iterable are true any() Returns True if any elements of an iterable are true enumerate() Returns a list of tuples containing indices and values from an iterable filter() Filters elements from an iterable iter() Returns an iterator object len() Returns the length of an object map() Applies a function to every item of an iterable next() Retrieves the next item from an iterator range() Generates a range of integer values reversed() Returns a reverse iterator slice() Returns a slice object sorted() Returns a sorted list from an iterable zip() Creates an iterator that aggregates elements from iterables Composite Data Type Function Description bytearray() Creates and returns an object of the bytearray class bytes() Creates and returns a bytes object (similar to bytearray, but immutable) dict() Creates a dict object frozenset() Creates a frozenset object list() Constructs a list object object() Returns a new featureless object set() Creates a set object tuple() Creates a tuple object Classes, Attributes, and Inheritance Function Description classmethod() Returns a class method for a function delattr() Deletes an attribute from an object getattr() Returns the value of a named attribute of an object hasattr() Returns True if an object has a given attribute isinstance() Determines whether an object is an instance of a given class issubclass() Determines whether a class is a subclass of a given class property() Returns a property value of a class setattr() Sets the value of a named attribute of an object super() Returns a proxy object that delegates method calls to a parent or sibling class Input/Output Function Description format() Converts a value to a formatted representation input() Reads input from the console open() Opens a file and returns a file object print() Prints to a text stream or the console Variables, References, and Scope Function Description dir() Returns a list of names in current local scope or a list of object attributes globals() Returns a dictionary representing the current global symbol table id() Returns the identity of an object locals() Updates and returns a dictionary representing current local symbol table vars() Returns __dict__ attribute for a module, class, or object Miscellaneous Function Description callable() Returns True if object appears callable compile() Compiles source into a code or AST object eval() Evaluates a Python expression exec() Implements dynamic execution of Python code hash() Returns the hash value of an object help() Invokes the built-in help system memoryview() Returns a memory view object staticmethod() Returns a static method for a function __import__() Invoked by the import statement
Bisect module manages ordered sequencesCategory method name String/byte sequence representation __repr__, __str__, __format__, __bytes__ Numeric conversion __abs__, __bool__, __complex__, __int__, __float__, __hash__, __index__ Collection simulation __len__, __getitem__, __setitem__, __delitem__, __contains__ Iterative enumeration __iter__, __reversed__, __next__ Callable simulation __call__ Context management __enter__, __exit__ Instance creation and destruction __new__, __init__, __del__ Attribute Management __getattr__, __getattribute__, __setattr__, __delattr__, __dir__ Attribute descriptor __get__, __set__, __delete__ Class-related services __prepare__, __instancecheck__, __subclasscheck__
When list is not the optimal choice, Dict is the core type of Python, but it is the result of space-time, compared to the memory, tuple is a good alternative to the DICT structure, set to do the inclusion and the weight is appropriate.Bisect.bisect_left(a,x, lo=0, hi=len(a)) : Find the index in which x is inserted in the ordered list a. Lo and hi are used to specify the range of the list. The default is to use the entire list. If x already exists, insert it on the left side. The return value is index. Bisect.bisect_right(a,x, lo=0, hi=len(a)) Bisect.bisect(a, x,lo=0, hi=len(a)) : These two functions are similar to bisect_left, but if x already exists, it is inserted on the right side. Bisect.insort_left(a,x, lo=0, hi=len(a)) : Insert x in the ordered list a. Same effect as a.insert(bisect.bisect_left(a,x, lo, hi), x). Bisect.insort_right(a,x, lo=0, hi=len(a)) Bisect.insort(a, x,lo=0, hi=len(a)) : Similar to insort_left, but if x already exists, insert it to the right. The functions provided by the Bisect module can be divided into two categories: bisect* is only used to find index, no actual insertion is made; insort* is used for actual insertion.
Python_ built-in four types of queuesFrom array import array From random import random Floats = array('d', (random() for i in range(10**7))) Fp = open('floats.bin', 'wb') Floats.tofile(fp) Fp.close() Floats2 = array('d') Fp = open('floats.bin', 'rb') Floats2.fromfile(fp, 10**7) Fp.close() Floats2 == floats # Python2.7 - Python3.7 has this problem `` >>> a=(1,2,[1,2,3,4,5]) >>> a[2]+=[1,1,1] Traceback (most recent call last): File "<pyshell#1>", line 1, in <module> a[2]+=[1,1,1] TypeError: 'tuple' object does not support item assignment >>> a (1, 2, [1, 2, 3, 4, 5, 1, 1, 1]) >>> a[2].extend([2,2,2]) >>> a (1, 2, [1, 2, 3, 4, 5, 1, 1, 1, 2, 2, 2]) >>> ```
From queue import Queue #LILO queue
q = Queue() #Create a queue object
Q.put(0) #Insert elements at the end of the queue
Q.put(1)
Q.put(2)
Print('LILOQueue', q.queue) #View all the elements in the queue
Print(q.get()) #return and delete the queue header element
Print(q.queue)
From queue import LifoQueue #LIFO queue
lifoQueue = LifoQueue()
lifoQueue.put(1)
lifoQueue.put(2)
lifoQueue.put(3)
Print('LIFOQueue', lifoQueue.queue)
lifoQueue.get() #return and delete the tail element of the queue
lifoQueue.get()
Print(lifoQueue.queue)
From queue import PriorityQueue # priority queue
priorityQueue = PriorityQueue() #Create priority queue object
priorityQueue.put(3) #insert element
priorityQueue.put(78) #insert element
priorityQueue.put(100) #insert element
Print(priorityQueue.queue) #View all the elements in the priority queue
priorityQueue.put(1) #insert element
priorityQueue.put(2) #insert element
Print('Priority Queue:', priorityQueue.queue) #View all elements in the priority queue
priorityQueue.get() #return and delete the lowest priority element
Print('remaining elements after deletion', priorityQueue.queue)
priorityQueue.get() #return and delete the lowest priority element
Print('remaining elements after deletion', priorityQueue.queue) #remaining elements after deletion
priorityQueue.get() #return and delete the lowest priority element
Print('remaining elements after deletion', priorityQueue.queue) #remaining elements after deletion
priorityQueue.get() #return and delete the lowest priority element
Print('remaining elements after deletion', priorityQueue.queue) #remaining elements after deletion
priorityQueue.get() #return and delete the lowest priority element
Print('All deleted: ', priorityQueue.queue) #View all elements in the priority queue
From collections import deque
dequeQueue = deque(['Eric','John','Smith'])
Print(dequeQueue)
dequeQueue.append('Tom') #Insert new element on the right side
dequeQueue.appendleft('Terry') #Insert new elements on the left side
Print(dequeQueue)
dequeQueue.rotate(2) loops right 2 times
Print('Queue after shifting right 2 times', dequeQueue)
dequeQueue.popleft() #return and delete the leftmost element of the queue
Print('Delete the leftmost element: ', dequeQueue)
dequeQueue.pop() #return and delete the rightmost element of the queue
Print('Delete the rightmost element: ', dequeQueue)
The above queues are available in multithreading and are thread safe, but cannot be used for communication in multiple processes. In a multi-process, you need to use this:
From multiprocessing import Process, Queue
Myqueue = Queue(100)
## Reference
Https://blog.csdn.net/sinat_38682860/article/details/80392493
Https://www.cnblogs.com/cmnz/p/6936181.html
Key words
Builtins Modulefrom keyword import kwlistprint(kwlist)
import builtinsdir(builtins)
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