Introduction to Python's efficient programming skills

I've been programming with Python for years, even today I'm still amazed at how this language can make the code behave neatly and apply to dry programming principles. These years of experience have made me learn a lot of small skills and knowledge, mostly by reading very popular open source software, such as Django, Flask, requests.

Some of the techniques I've picked out below are often overlooked, but they can really help us in everyday programming.

1. Dictionary derivation (Dictionary comprehensions) and set derivation (set comprehensions)

Most Python programmers know and use list comprehensions. If you are not familiar with the list comprehensions concept-a list comprehension is a shorter, more concise way to create a list.

>>> some_list = [1, 2, 3, 4, 5]>>> another_list = [x + 1 for x in Some_list]>>> another_list[ 2, 3, 4, 5, 6]

Since Python 3.1 (or even Python 2.7), we can use the same syntax to create collections and dictionary tables:

>>> # Set Comprehensions>>> some_list = [1, 2, 3, 4, 5, 2, 5, 1, 4, 8]>>> even_set = {x for x  In Some_list if x% 2 = = 0}>>> even_setset ([8, 2, 4]) >>> # Dict Comprehensions>>> d = {x:x% 2 = = 0 for x in range (1, one)}>>> D{1:false, 2:true, 3:false, 4:true, 5:false, 6:true, 7:false, 8:true, 9:false, 10:true}

In the first example, we created a collection with non-repeating elements based on some_list, and the set contains only an even number. In the case of the dictionary table, we create a key that is an integer between 1 and 10, and value is a Boolean that indicates whether the key is an even number.

Another notable thing here is the collection of literal representations. We can simply create a collection in this way:

>>> My_set = {1, 2, 1, 2, 3, 4}>>> my_setset ([1, 2, 3, 4])

You do not need to use the built-in function set ().

2. Use the counter Count object when counting.

This may sound obvious, but it is often forgotten. For most programmers, counting one thing is a very common task, and in most cases it is not a very challenging thing-there are several ways to accomplish this task more simply.

The Collections class library in Python has a subclass of the built-in Dict class that is specifically designed to do this kind of thing:

>>> from collections import counter>>> c = Counter (' Hello World ') >>> ccounter ({' L ': 3, ' O ': 2, ': 1, ' E ': 1, ' d ': 1, ' H ': 1, ' R ': 1, ' W ': 1} ' >>> C.most_common (2) [(' L ', 3 ') [(' O ', 2 ')]

3. Beautiful print out JSON

JSON is a very good form of data serialization that is used by many of today's APIs and Web service. Using Python's built-in JSON processing makes the JSON string somewhat readable, but when it comes to large data, it behaves as a long, contiguous line, and the human eye is hard to see.

In order to make the JSON data more user-friendly, we can use the indent parameter to output beautiful JSON. This is especially useful when you are programming interactively or logging in the console:

>>> Import json>>> Print (json.dumps data)  # No indention{"status": "OK", "Count": 2, "results": [{ "Age": +, "name": "Oz", "Lactose_intolerant": true}, {"Age": "Name": "Joe", "lactose_intolerant": FALSE}]}>>&G T Print (Json.dumps (data, indent=2))  # with indention{  "status": "OK",  "Count": 2,  "results": [    {      "Age": +,      "name": "Oz",      "lactose_intolerant": True    },    {      "age": "      Name": "Joe",      "Lactose_intolerant": false    }  ]}

Also, using the built-in pprint module, you can make the printout of anything else more beautiful.

4. Create a one-time, fast, small Web service

Sometimes, we need to do some simple, very basic RPC interaction between two machines or services. We want to use a simple way of using the B program to invoke a method in a program--sometimes on another machine. Internal use only.

I do not encourage the methods described here to be used in non-internal, one-time programming. We can do this with a protocol called XML-RPC, which corresponds to this Python library.

Here is an example of using the Simplexmlrpcserver module to build a fast, small file read server:

From Simplexmlrpcserver import simplexmlrpcserverdef file_reader (file_name):    with open (file_name, ' R ') as F:        return F.read () server = Simplexmlrpcserver ((' localhost ', 8000)) Server.register_introspection_functions () Server.register_function (File_reader) server.serve_forever ()

Client:

Import Xmlrpclibproxy = Xmlrpclib. Serverproxy (' http://localhost:8000/') proxy.file_reader ('/tmp/secret.txt ')

We've got a remote file-reading tool with no external dependencies, just a few lines of code (of course, no security, so you can do it at home).

5. Python's Magical Open source community

A few of the things I've mentioned here are in the Python standard library, and if you have Python installed, you can already use it. For many other types of tasks, there are a number of community-maintained third-party libraries available for you to use.

The following checklist is an essential requirement for what I think is a good and robust open Source library:

A good open Source library must ...

• Contains a clear license statement that can be applied to your usage scenarios.

• 
  

• Development and maintenance work is very active (or, you can participate in the development to maintain it.) )

• 
  

• Simple to use PIP to install or deploy repeatedly.

• 
  

• There is a test suite with sufficient test coverage.

If you find a good library that meets your requirements, don't be embarrassed ———— most of the open source projects welcome the donation code and welcome help-even if you are not a Python master.

Original link: improving Your Python Productivity