Five Python libraries that make everyday coding easier
Today we will study some very useful third-party modules that can make our daily coding easier and more convenient.
sh
https://github.com/amoffat/sh
If you have ever used the subprocess library in Python, then we are very familiar with it. It's possible to be disappointed with it, it's not the most intuitive library, can be a bit complex, and has difficulty handling the output of low-level system calls.
But the sh library ends the pain of inefficient use of subprocesses. Using the sh library, we can make almost any system call we want, as if it were an ordinary function, which makes executing these calls cumbersome. Be more simple and intuitive. We can use them just like normal Python functions.
Here is an example of using ls to get the contents of a directory from sh:
All we have to do is import the system call like a normal function, preferably The part is that all functions are dynamically generated, so we should have access to most binaries that already exist in the underlying system.
Next time when we need to make some system calls, we can try using sh.
rstr
https://github.com/leapfrogonline/rstr
If we need some easily accessible random data the number of times is uncountable Whether it's building tests for your code, filling buffers, or fuzzing your code with random data to see where it breaks, the rstr library has us covered.
This handy little library allows us to generate different types of random string data. It can generate alphanumeric data, special symbols, and even use regular expressions to build complex data patterns.
For example, suppose we want a string of numbers of a certain length. We can do this using the following rstr snippet:
#This will print out a randomly selected 10-digit number, convenient!
IPython
https://ipython.org/
IPython is not an ordinary module, it is an incredible interactive shell Modules that enhance our Python REPL. IPython brings a great set of features to the interactive Python shell, such as autocompletion, colored output, and run details.
Introducing classes and inspecting functions becomes much easier with IPython. The command history is easier to navigate, and we also get powerful tab completion and autocomplete functionality similar to Zsh.
Humanize
https://github.com/jmoiron/humanize
This library is really exciting, it will take date, time and Numbers, etc., and "humanize" them into human-readable phrases like "three o'clock" or "ten billion", and even better it does this automatically!
For example, let's say we're dealing with a bunch of very large numbers (think millions and billions) and want to display them in a more user-friendly way. This library can do it without even trying:
Ouptu:
400.0 million
The human module is also great for calculating our stock market returns (or Loss...) and presented in an easy-to-read format.
Emoji
https://github.com/carpedm20/emoji/
Finally we introduce an interesting library that combines emoticons Symbols are added to the text. No one wants to copy and paste emojis directly into their code, or fumble through confusing character codes.
Here's an example of how to add a winking emoji in code:
This will print out the actual emoji, now when the user gets an error in the console , you can wink at them.
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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