python monitor memory usage

write an iterative class of read files to easily implement file reads:Listing 9. Another example of yielddef read_file (Fpath): block_size = 1024x768 with open (Fpath, ' RB ') as F: While True: BLOCK = F.read (block_ SIZE) if block: yield block else: returnThe above simply introduces the basic concepts and usage of yield, and yield is more powerful in

This article mainly introduces the differences and usage of the built-in data types list, tuple, dict, and set in Python, which are all very basic knowledge and meticulous and comprehensive, if you have any need, refer. The Python language is concise and clear, and you can use less code to implement the same function. The four built-in data types of

binarytype corresponds to the use of the ByteArray in Python. output Result: 4. Composite data types (arraytype, Maptype, Structtype) Composite data types TotalThere are three kinds: Array (arraytype), dictionary (maptype), struct (structtype), where the array (arraytype) requires the same array element type, and the dictionary (Maptype) requires all "key" types to be consistent, all "value" , but the type of "key", "value" can be inconsistent, and

unpredictable memory consumption. A good approach is to use fixed-length buffers to continuously read the contents of the file. With yield, we no longer need to write an iterative class of read files to easily implement file reads:Listing 9. Another example of yield 123456789 def read_file(fpath): BLOCK_SIZE = 1024 with open(fpath, ‘rb‘) as f: while True: block = f.read(BLOCK_SIZE) if block: yield block else: return The above

Describes the usage of the yield generator in Python.Yield is the meaning of generation, but in python it is understood as a generator. The use of the generator can mainly be iterated, which simplifies many calculation models (not very familiar with how to simplify ).Yield is an expression with a returned value. When a function contains yield, it is no longer a common function, but a generator. When the fun

the file. With yield, we no longer need to write an iterative class of read files to easily implement file reads:Listing 9. Another example of yield Defread_file (Fpath): =1024 'rb') as F: whiletrue: =f.read (block_size) ifblock: yieldblock Else: returnThe above simply introduces the basic concepts and usage of yield, and yield is more powerful in

Python built-in function memoryview (obj) usage, pythonmemoryview The memoryview () function returns the memory view object (Momory view) of the given parameter ). Syntax Memoryview Syntax: memoryview (obj) Parameter description: obj -- Object Returned value: returns the list of tuples. English document: Class memoryview (obj) Memoryview objects allow

This article mainly introduces the _ slots _ usage in python. The example analyzes the functions of _ slots _ and related usage skills, for more information about how to use _ slots _ in python, see the example in this article. Share it with you for your reference. The specific analysis is as follows: After _ slots

"Turn from: Http://www.ibm.com/developerworks/cn/linux/sdk/python/python-5/index.html#N1004E"When we talk about "text processing," we usually refer to what we are dealing with. Python reads the contents of a text file into a string variable that can be manipulated very easily. The file object provides three "read" methods:. Read (),. ReadLine (), and. ReadLines (

This article mainly introduces the usage of is and id in Python, which is the basic knowledge in Python learning. The author provides an example to briefly explain how to use it. For more information, see (Ob1 is ob2) is equivalent to (id (ob1) = id (ob2 )) First, the id function can obtain the object's memory address

The examples in this article describe the IS and ID usage in Python. Share to everyone for your reference. The specific analysis is as follows: (Ob1 is OB2) equivalent to (ID (ob1) = = ID (ob2)) First the ID function can get the memory address of the object, and if the memory address of the two objects is the same, t

The Decorator is one of the advanced Python skills. the Decorator can bring many features required in functional programming, here we will follow the article to gain a deep understanding of the usage of the Python modifier. For more information, see that functions or classes are objects and they can be passed everywhere. They are mutable objects and can be change

At present, the development of naming conventions are basically followed by the hump nomenclature, such as: UserName. I'm not going to have the specs, and we all know it. Then get to the point, how to get a good name in Python, so you can better see the name of the idea?1. Tuple (tuple) type variable nameIn Python, a tuple type variable, such as: Schoolroles = ("Student", "Class

Python-linecache module Read File usage detailedThe Linecache module allows any row to be fetched from any file, and is optimized with caching, as is often the case with multiple lines being read from a single file.1. Linecache.getlines (filename)Get all the content from the file named filename, output it as a list format, one element in each behavior list for the file, and store it as an element in the lis

This article describes how to calculate the cpu usage of python, and how to use the win32pdh module in Python. For more information, see the following example. Share it with you for your reference. The specific implementation method is as follows: #-*-Coding = utf-8-*-import win32pdhimport time # Counter pathsPROCESSOR_PERCENT = r' \ Processor (_ Total) \ % Pro

Example of _ slots _ usage in python This example describes how to use _ slots _ in python. Share it with you for your reference. The specific analysis is as follows: After _ slots _ is defined, the attribute names that can be assigned on the instance will be limited to the specified names. Otherwise, AttributeError is triggered. This restriction can prevent ot

Python scientific computing package numpy usage example details, pythonnumpy This article describes how to use the Python scientific computation package numpy. We will share this with you for your reference. The details are as follows: 1. Data Structure Numpy uses a matrix data structure similar to Matlab called ndarray to manage data, which is more powerful than

;>> fromcollections importiterable>>> isinstance (Fab, iterable)False>>> isinstance (Fab (5), iterable)TrueEach call to the FAB function generates a new instance of generator, each of which does not affect each other:>>> F1 =fab (3)>>> F2 =fab (5)>>> print ' F1: ', F1.next ()F1:1>>> print ' F2: ', F2.next ()F2:1>>> print ' F1: ', F1.next ()F1:1>>> print ' F2: ', F2.next ()F2:1>>> print ' F1: ', F1.next ()F1:2>>> print ' F2: ', F2.next ()F2:2>>> print ' F2: ', F2.next ()F2:3>>> print ' F2: ', F2.

, self.a, self.b = 0, 0, 1 def __iter__ (s ELF): return self def next: if SELF.N The Fab class continuously returns the next number of columns through next (), and memory consumption is always constant:>>> for N in Fab (5): ... Print N ... 1 1 2) 3 5However, with this version of the class rewrite, the code is far less concise than the first version of the Fab function. Yield comes in handy if we want to maintain the s

This example describes the use of the list loop statement in Python. Share to everyone for your reference. The specific usage analysis is as follows: One of the powerful features of Python is its parsing of the list, which provides a compact way to map a list to another list by applying a function to each element in the list.Instance Copy Code code as foll