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This article mainly introduces "what are the methods of Python collections code optimization". In the daily operation, I believe that many people have doubts about the methods of Python collections code optimization. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful to answer the doubts of "what are the methods of Python collections code optimization?" Next, please follow the editor to study!

One of the biggest advantages of Python is that it has a variety of modules and packages to choose from. These modules and packages extend the functionality of Python to many popular areas, including machine learning, data science, Web development, and front-end. One of the best performers is Python's built-in collections module.

Generally speaking, the collections module in Python is a container for storing data collections such as lists, dictionaries, tuples, and sets. These containers are embedded in Python and can be used out of the box. The collections module provides additional high-performance data types that optimize the code and make some tasks more concise.

Counter

Official document: https://docs.python.org/2/library/collections.html#collections.Counter

Counter is a subclass of the dictionary object. The Counter () function in the collections module takes an iterator such as list or tuple and returns a Counter dictionary. The key of this dictionary is the only element in the iterator, and the value of each key is the count of iterator elements.

First, we need to import Counter from the collections package:

From collections import Counter

If we want to create a Counter object, we have to assign it to a variable, just like any other object class, and the only variable passed to the Counter object is the iterator.

Lst = [1,2,3,3,2,1,1,1,2,2,1,1] counter = Counter (lst)

If we print out this Counter using a simple print function (print (counter)), we will get some output that is slightly similar to dictionary:

Counter ({1: 7,2: 5,3: 3})

You can use these key values to access any Counter item. This is exactly the same way to get elements from standard Python dictionary.

Lst = [1,2,3,3,2,1,1,1,1,2,2,3,1,1] counter = Counter (lst) print (counter [1])

Most_common () function

By far, the most useful function in the Counter object is most_common (). When it is applied to a Counter object, it returns a list that contains the first N common elements and their counts, sorted in descending order of popularity.

Lst = [1,2,3,3,2,1,1,1,2,2,3,1,1] counter = Counter (lst) print (counter.most_common (2))

The above code prints out the list of the following tuples.

[(1,7), (2,5)]

The first element of each tuple is the only item in the list, and the second element is the count value. This can be a quick and easy way to get the top 3 common elements in list and their counts.

If you want to learn more about the features of Counter, you can check the official documentation.

Defaultdict

Official document: https://docs.python.org/2/library/collections.html#collections.defaultdict

Defaultdict works exactly like a normal python dictionary, except that when you try to access a key that doesn't exist, it doesn't report an error, but initializes the key with the default value. The default value is automatically set based on the data type entered as a parameter when the defaultdict object is created. The following code is an example.

Instead, it initializes the key with the default value. The default value is automatically set based on the data type entered as a parameter when the defaultdict object is created. The following code is an example.

From collections import defaultdict names_dict = defaultdict (int) names_dict ["Bob"] = 1 names_dict ["Katie"] = 2 sara_number = names_dict ["Sara"] print (names_dict)

In the above example, the default value passed to the defaultdict object is int. Then each key gets a value, that is, "Bob" and "Katie" each get a number. But on the last line, we tried to access an undefined key, "Sara".

In a normal dictionary, this operation reports an error. But when using defaultdict, a new key is automatically initialized for "Sara" with a value of 0 corresponding to our int data type. Therefore, the last line can print out the "Bob", "Katie" and "Sara" and the corresponding values.

Defaultdict (, {'Bob': 1,' Katie': 2, 'Sara': 0})

If we use list to initialize our defaultdict, that is, names_dict = defaultdict (list), then the value of "Sara" will be initialized to an empty list [], and the printed content will become:

Defaultdict (, {'Bob': 1,' Katie': 2, 'Sara': []})

If you want to learn more about the features of defaultdict, you can check the official documentation.

Deque

Official document: https://docs.python.org/2/library/collections.html#collections.deque

Queue is a basic data architecture in computer science, which follows the principle of first in, first out (First-In-First-Out,FIFO). Simply put, even the first object added to the queue must be the first to be deleted. We can only insert content in front of the queue, and we can only delete content from behind-- we can't operate on intermediate content.

This feature is optimized by deque in the collections library. A key feature of this approach is to keep the queue length constant, that is, if you set the maximum size of queue to 10, deque will add and remove elements according to FIFO principles to keep the maximum size of queue at 10. This is by far the best way to use queue in Python.

Let's look at another example. We first create a deque object and then initialize it with integers from 1 to 10.

From collections import deque my_queue = deque (maxlen=10) for i in range (10): my_queue.append (iTun1) print (my_queue)

In the above code, we first initialize the deque, specifying that its maximum length is 10. Then, we insert the value into the queue through for loop. Note that here we populate queue in the same way as the usual Python list. Finally, we print out the results.

Deque ([1,2,3,4,5,6,7,8,9,10], maxlen=10)

Because our queue is set to maxlen=10 and the loop value adds 10 elements, this queue contains all the numbers from 1 to 10. Now let's see what happens if we continue to add numbers to it.

For i in range (10,15): my_queue.append (iTun1) print (my_queue)

In the above code, we added five more elements to queue-- the numbers 11 to 15. But our queue can only have 10 elements, so it needs to delete some elements. Because queue must obey the FIFO principle, it deletes the first five elements inserted into the queue, in [1, 2, 3, 4, 5]. The printed results are as follows:

Deque ([6, 7, 8, 9, 10, 11, 12, 13, 14, 15], maxlen=10)

If you want to learn more about the features of deque, you can check the official documentation.

Namedtuple

Official document: https://docs.python.org/2/library/collections.html#collections.namedtuple

When you use python to create a regular tuple, the elements are generic and unnamed. This makes you have to remember the exact index of each tuple element. Namedtuple can solve this problem.

Namedtuple () can return a tuple, each location in the tuple has a fixed name, and the namedtuple object also has a common name. To use namedtuple, you need to first create a template for it. The following code creates a namedtuple template named "Person" with attributes of "name", "age", and "job".

From collections import namedtuple Person = namedtuple ('Person',' name age job') Once the template is created, you can use it to create namedtuple objects. Let's create 2 namedtuple's for 2 Persons and print out their representation. Person = namedtuple ('Person',' name age job') Mike = Person (name='Mike', age=30, job='Data Scientist') Kate = Person (name= "Kate", age=28, job='Project Manager') print (Mike) print (Kate)

The above code is easy to understand. We initialized a "Person" template for namedtuple and initialized all its properties. The final print result of the above code is:

Person (name='Mike', age=30, job='Data Scientist') Person (name='Kate', age=28, job='Project Manager')

Therefore, namedtuple makes the use of tuple easier, more readable, and more organized.

At this point, the study of "what are the ways of Python collections to optimize the code" is over. I hope to be able to solve everyone's doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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