The Python standard library has over 200 modules that programmers can import and use in their programs. While the average programmer will have some experience with many of these modules, it's likely that there are some useful ones that they're still unaware of.
I found that many of these modules contain functions that are very useful in various fields. Comparing data sets, collaborating with other functions, and audio processing can all be automated using just Python.
So, I have compiled a shortlist of Python modules that you may not know about and have given a proper explanation of these few modules so that you can understand and use them in the future.
All these modules have different functions and classes. I've included several lesser-known functions and classes, so even if you've heard of these modules, you may not know some of their aspects and uses.
difflib is a tool that focuses on comparing data sets (especially strings ) Python module. To get a concrete idea of a few things you can do with this module, let's examine some of its most common functions.
SequenceMatcher
SequenceMatcher is a method that compares two strings and returns them based on their similarity function of the data. By using ratio(), we will be able to quantifythis similarity in terms of a ratio/percentage .
Syntax:
SequenceMatcher(None, string1, string2)
The following simple example shows the function of this function:
from difflib import SequenceMatcher phrase1 = "Tandrew loves Trees." phrase2 = "Tandrew loves to mount Trees." similarity = SequenceMatcher(None, phrase1, phrase2) print(similarity.ratio()) # Output: 0.8163265306122449
get_close_matches
Next Is get_close_matches, this function returns the closest match to the string passed in as a parameter.
Grammar:
get_close_matches(word, possibilities, result_limit, min_similarity)
Let’s explain these parameters that may be confusing:
word is the target word that the function will look at. possibilities is an array containing the matches that the function will look for and find the closest match. result_limit is the limit on the number of results returned (optional). min_similarity is the minimum similarity that two words need to have in order to be considered a return value by the function (optional). The following is an example of its use:
from difflib import get_close_matches word = 'Tandrew' possibilities = ['Andrew', 'Teresa', 'Kairu', 'Janderson', 'Drew'] print(get_close_matches(word, possibilities)) # Output: ['Andrew']
除此之外还有几个是您可以查看的属于 Difflib 的其他一些方法和类：unified_diff、Differ 和 diff_bytes
sched 是一个有用的模块，它以跨平台工作的事件调度为中心，与 Windows 上的任务调度程序等工具形成鲜明对比。大多数情况下，使用此模块时，都会使用 schedular 类。
更常见的 time 模块通常与 sched 一起使用，因为它们都处理时间和调度的概念。
创建一个 schedular 实例：
schedular_name = sched.schedular(time.time, time.sleep)
可以从这个实例中调用各种方法。
run() 时，调度程序中的事件/条目会按照顺序被调用。在安排完事件后，此函数通常出现在程序的最后。enterabs() 是一个函数，它本质上将事件添加到调度程序的内部队列中。它按以下顺序接收几个参数：下面是一个示例，说明如何一起使用这两个函数：
import sched
import time
def event_notification(event_name):
print(event_name + " has started")
my_schedular = sched.scheduler(time.time, time.sleep)
closing_ceremony = my_schedular.enterabs(time.time(), 1, event_notification, ("The Closing Ceremony", ))
my_schedular.run()
# Output: The Closing Ceremony has started
还有几个扩展 sched 模块用途的函数：cancel()、enter() 和 empty()。
binaascii 是一个用于在二进制和 ASCII 之间转换的模块。
b2a_base64 是 binaascii 模块中的一种方法，它将 base64 数据转换为二进制数据。下面是这个方法的一个例子：
import base64
import binascii
msg = "Tandrew"
encoded = msg.encode('ascii')
base64_msg = base64.b64encode(encoded)
decode = binascii.a2b_base64(base64_msg)
print(decode)
# Output: b'Tandrew'
该段代码应该是不言自明的。简单地说，它涉及编码、转换为 base64，以及使用 b2a_base64 方法将其转换回二进制。
以下是属于 binaascii 模块的其他一些函数：a2b_qp()、b2a_qp() 和 a2b_uu()。
tty 是一个包含多个实用函数的模块，可用于处理 tty 设备。以下是它的两个函数：
setraw() 将其参数 (fd) 中文件描述符的模式更改为 raw。setcbreak() 将其参数 (fd) 中的文件描述符的模式更改为 cbreak。由于需要使用 termios 模块，该模块仅适用于 Unix，例如在上述两个函数中指定第二个参数（when=termios.TCSAFLUSH）。
weakref 是一个用于在 Python 中创建对对象的弱引用的模块。
弱引用是不保护给定对象不被垃圾回收机制收集的引用。
以下是与该模块相关的两个函数：
getweakrefcount() 接受一个对象作为参数，并返回引用该对象的弱引用的数量。getweakrefs() 接受一个对象并返回一个数组，其中包含引用该对象的所有弱引用。weakref 及其函数的使用示例：
import weakref
class Book:
def print_type(self):
print("Book")
lotr = Book
num = 1
rcount_lotr = str(weakref.getweakrefcount(lotr))
rcount_num = str(weakref.getweakrefcount(num))
rlist_lotr = str(weakref.getweakrefs(lotr))
rlist_num = str(weakref.getweakrefs(num))
print("number of weakrefs of 'lotr': " + rcount_lotr)
print("number of weakrefs of 'num': " + rcount_num)
print("Weakrefs of 'lotr': " + rlist_lotr)
print("Weakrefs of 'num': " + rlist_num)
# Output:
# number of weakrefs of 'lotr': 1
# number of weakrefs of 'num': 0
# Weakrefs of 'lotr': [<weakref at 0x10b978a90; to 'type' at #0x7fb7755069f0 (Book)>]
# Weakrefs of 'num': []
输出从输出的函数返回值我们可以看到它的作用。由于 num 没有弱引用，因此 getweakrefs() 返回的数组为空。
以下是与 weakref 模块相关的一些其他函数：ref()、proxy() 和 _remove_dead_weakref()。
Difflib is a module for comparing data sets, especially strings. For example, SequenceMatcher can compare two strings and return data based on their similarity. sched is a useful tool for use with the time module for use schedular Instance schedules events (in the form of a function). For example, enterabs() adds an event to the scheduler's internal queue, which will be called when run() Run when the function is executed. binaascii Converts between binary and ASCII to encode and decode data. b2a_base64 is a method in the binaascii module, which will Convert base64 data to binary data.
tty Modules need to be used together termios module, and handles tty devices. It only works on Unix.
weakref is used for weak references. Its functions can return weak references to an object, find the number of weak references to an object, etc. One of the most commonly used functions is getweakrefs(), which takes an object and returns an array of all weak references contained in the object.
Each of these functions has its own purpose, and each has varying degrees of usefulness. It's important to know as many Python functions and modules as possible in order to maintain a stable library of tools that you can use quickly when writing code.
No matter your level of programming expertise, you should always be learning. Investing a little more time can bring you more value and save you more time in the future.
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