The perennially popular programming language Python may be a fan favorite, but can you make a living as a Python developer? What’s more, as one of the most popular languages in the world, the job market has a lot to do with it. How is the acceptance?
#It turns out that using Python is correct - and recently, it seems that is the case.
Delving deeper into the data in the Dice database, we filtered out the "Python Developer" job positions. We also “cleaned” the data to eliminate other work citing the language by name from this study.
It probably doesn’t surprise you that Python’s job market is very healthy. "Python Developer" is not only one of the most popular job titles in our database, but it is also historically one of the most stable. While many job titles and programming languages like Kotlin are subject to the whims of the industry and the decisions of big tech companies, Python weathers the storm with ease and is apparently unaffected by industry trends.
Over the past three quarters, Python job postings have seen significant growth. As you can see from the table below, the "Python Developer" job title has returned to very stable numbers from the beginning of 2016 (Q1 2016, to be specific). Job openings for Python developers nearly doubled from the third quarter of 2017 to the second quarter of 2018. (Keep in mind that these data are normative, so our findings here are primarily for Python developer roles. If we include all positions that use Python, the actual numbers are much higher.)
Let’s take a look at the salary and employment of domestic Python positions.
The back-end development position is a big one. It is nearly half of the current Python positions with the largest demand. The demand is 10 times that of crawler engineers
The second is Data analysis positions, second only to Python back-end development
Data mining and machine learning are very similar. They can be combined together, accounting for about 17%, but it is only half of the data analysis positions. It seems that AI The positions are far from saturated!
There are very few crawler jobs. The demand for crawler positions has gradually dropped, so those who want to switch to crawler jobs need to consider it
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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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