Why do I not recommend you to learn python?

foreword

The reason why I am engaged in Python is because I am very uncomfortable with the syntax of PHP, although PHP is known as the best programming language in the world. So, for this reason, I paid a heavy price and lost my job several times.

Some people around me always ask me some technical questions about Python from time to time, but most of them are relatively basic. If Python hadn't become popular in recent years, maybe I would really switch to delivering food.

For a language like Python, it can be learned as an interest or hobby, but if you want to find a good job, you should be cautious, which is why you are not recommended to engage in Python.

[----Help related technology learning, all the following learning materials are free at the end of the article! ----】

Python learning fever VS Python job market is huge

It is expected that Python will become popular, but it is not expected that it will be so fast. In 2014, I really couldn't stand the ending of the dollar sign and semicolon in PHP, so I started to get into Python. At that time, there were only a few books on the market for Python. As for Liao Xuefeng's Python introductory tutorial, I gave up after reading 2 articles, because the writing is too simple. Of course, he also wrote some advanced tutorials later, you can take a look if you are interested.

However, Python is still a niche language. How small is it? You can go to the pull hook or BOSS to directly hire and search. Basically, except for Beijing and Shanghai, there are only 10-20 companies coming and going in the first-tier cities, and 80% of these companies are entrepreneurs. company.

Once, I joined 2 Python companies in 1 year, and the first company closed down within 3 months after I entered it. Why? No one uses it for business.

At present, Python is more suitable for the business mainly as follows:

• data analysis
• information security
• System operation and maintenance
• application test

Needless to say, operation and maintenance, it is basically the era of automation now. In data analysis, reptiles are the most talked about. Actually, it doesn't have much to do with analytics. The common one is the export function of some reports. What is more advanced is big data.

For information security, it can be said that it is the transition from the Stone Age to the Bronze Age. Although there is a big killer like metasploit, many things have yet to be realized.

It is still used with caution for some of the following businesses:

• GUI development
• Embedded Development

GUI development first, not hot business now. Even if there is this business, it will only be a Windows GUI. They have MFC, and it has nothing to do with your Python. If you want to use PyQt5 to write an interface, you might as well use Electron, which is better at memory management.

As for embedded development, let's learn C language and assembly honestly. Don't expect Python to help you in it. Of course, any language is fine for doing application-level business.

For web development, there are even more options available. In the past, Java has been deeply involved in the market for many years, then there is nodejs, and there is Golang. However, there are really not many stalks that Python can get in this market, and a large part of it is brought by Django.

The popularity of Flask in recent years has made many people realize the simplicity of Python. However, the little-known framework of Pyramid is really easy to use (only my own point of view, you can dig a lot of interesting things).

Rational return

2018 is a crazy year, and the capital market is surging. Compared with 2019, it is much calmer. With the return of rationality, capital gradually returned to normal.

A buddy said that his company's front-end is the hottest now, and his Java will be relegated to the second-tier. Just any 3-year front-end will require a minimum salary of 16K at every turn. It is entirely possible to put it in 16-17 years, but putting it in 19 years can only be said to be wishful thinking.

Therefore, in 2019, we still need to start with our own learning and accumulate more skills. Of course, just learn things other than technology, and some ways to make money are always outside your knowledge.

You can take learning Python as an investment, and don't pay too much attention to whether there is a return.

wrong half life

The reason why it is not recommended to engage in Python is that you think that engaging in Python is an easy thing, but you choose a difficult road of no return.

I have to say that in the past few years of working in Python, I have spent every day from nine to six, and I still have to find something to do from time to time. Although the salary is not as high as the Java bosses, life is still enjoyable. Why? Python is efficient. How efficient is it, just install a package with pip directly.

Every time I see a Java boss using maven or grade to install dependencies, I meditate that pip is still easy to use, and at least there is a progress bar to know how long it will take to complete. As for npm, I won’t talk about it, and it will become a language dispute if it goes on.

If you think that working on Python is an easy thing, then the days of eating and dying will come to an end. Sometimes you pat your chest and tell the boss, I can finish this thing in half a day, but reality slaps you hard.

To give a simple example, for example, exporting a report to word is a very common business output. If you want to output the corresponding directory in word, it is really a terrible thing. Because the docx library commonly used in python does not support it at all, while Java's POI and PHP's phpword are supported, so you have to work overtime for your ignorance.

However, being idle will cause you to toss around, otherwise you will not be able to explain to your superiors. Here, we should also be glad that we always meet some good leaders who encourage you to learn more. So, it gradually became a soy sauce character, a firefighting version. You spend half an hour solving things that others can't handle.

What Win32 API programming, GUI programming, data reporting, analysis, patching and even the development of plug-ins have all gone through. As a result, 2-3 people did the work of a team of 5-6 people, and the money was still so much.

When you have been working in Python for a long time, you will find how little your knowledge is. As a result, you have to push your systematic study again, so that you spend long nights in the days with a huge amount of information every day.

To give the simplest example, in the analysis of stock technical aspects, what K-line analysis is a commonly used method. At this time, it is a very good idea for you to use Python to toss. As a result, you have to learn some financial knowledge to expand. Then things start to get uncontrollable and certain. Of course, the results will be interesting.

Once in a certain night, the return rate of the portfolio of several stocks analyzed by myself through Python reached 20%, and I almost lost sleep. I never thought that things are so simple and not easy to come by. As a person who pays back the money after the salary is paid, the rate of return of Yu'e Bao is only 3%. Even if I save 1W in 1 year, the interest will only be 300 after 1 year. However, it took me 1-2 months to achieve a 20% return. I admired myself for that kind of joy, but regretted that I invested too little at the beginning.

So, I gradually learned that Python is very useful in quantitative analysis, although no big companies use it for real-time transactions. However, I have to say that Python is a very good analysis language.

As an academic language, Python, which only exists in key universities, has its unique inherent advantages. Compared with other analysis tools, such as R and Matlab, Python is more user-friendly to use. Especially when developing plug-ins, of course, this involves part of the content of black production, so I won't expand the explanation. You will find that the effect of writing in Python is only slightly more complicated than that of Easy Language.

Leaving aside the field of information security, although data analysis is always a 7-figure income. However, it is not easy to become a data scientist. The reason why I can give you a salary at this price is because you can provide corresponding data viewpoints yourself. In other words, you can dig out business opportunities after big data. It must be a slow process.

Many people give up without persisting to that point. It is better to find some stable income channels earlier to avoid missing opportunities and wealth accumulation. If you can treat it rationally and clearly understand your current situation, you will know whether the position is suitable for you.

Finally, I would like to thank everyone who has read my article carefully. Reciprocity is always necessary. Although it is not a very valuable thing, you can take it away if you need it:

1. Introduction to Python

The following content is the basic knowledge necessary for all application directions of Python. If you want to do crawlers, data analysis or artificial intelligence, you must learn them first. Anything tall is built on primitive foundations. With a solid foundation, the road ahead will be more stable.All materials are free at the end of the article！！！

Include:

Computer Basics

python basics

Python introductory video 600 episodes:

Watching the zero-based learning video is the fastest and most effective way to learn. Following the teacher's ideas in the video, it is still very easy to get started from the basics to the in-depth.

2. Python crawler

As a popular direction, reptiles are a good choice whether it is a part-time job or as an auxiliary skill to improve work efficiency.

Relevant content can be collected through crawler technology, analyzed and deleted to get the information we really need.

This information collection, analysis and integration work can be applied in a wide range of fields. Whether it is life services, travel, financial investment, product market demand of various manufacturing industries, etc., crawler technology can be used to obtain more accurate and effective information. use.

Python crawler video material

3. Data Analysis

According to the report "Digital Transformation of China's Economy: Talents and Employment" released by the School of Economics and Management of Tsinghua University, the gap in data analysis talents is expected to reach 2.3 million in 2025.

With such a big talent gap, data analysis is like a vast blue ocean! A starting salary of 10K is really commonplace.

4. Database and ETL data warehouse

Enterprises need to regularly transfer cold data from the business database and store it in a warehouse dedicated to storing historical data. Each department can provide unified data services based on its own business characteristics. This warehouse is a data warehouse.

The traditional data warehouse integration processing architecture is ETL, using the capabilities of the ETL platform, E = extract data from the source database, L = clean the data (data that does not conform to the rules), transform (different dimension and different granularity of the table according to business needs) calculation of different business rules), T = load the processed tables to the data warehouse incrementally, in full, and at different times.

5. Machine Learning

Machine learning is to learn part of the computer data, and then predict and judge other data.

At its core, machine learning is "using algorithms to parse data, learn from it, and then make decisions or predictions about new data." That is to say, a computer uses the obtained data to obtain a certain model, and then uses this model to make predictions. This process is somewhat similar to the human learning process. For example, people can predict new problems after obtaining certain experience.

Machine Learning Materials:

6. Advanced Python

From basic grammatical content, to a lot of in-depth advanced knowledge points, to understand programming language design, after learning here, you basically understand all the knowledge points from python entry to advanced.

At this point, you can basically meet the employment requirements of the company. If you still don’t know where to find interview materials and resume templates, I have also compiled a copy for you. It can really be said to be a systematic learning route for nanny and .

But learning programming is not achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.

It can be achieved overnight, but requires long-term persistence and training. In organizing this learning route, I hope to make progress together with everyone, and I can review some technical points myself. Whether you are a novice in programming or an experienced programmer who needs to be advanced, I believe that everyone can gain something from it.

Data collection

This full version of the full set of Python learning materials has been uploaded to the official CSDN. If you need it, you can click the CSDN official certification WeChat card below to get it for free ↓↓↓ [Guaranteed 100% free]

Good article recommended

Understand the prospect of python: https://blog.csdn.net/SpringJavaMyBatis/article/details/127194835

Learn about python's part-time sideline: https://blog.csdn.net/SpringJavaMyBatis/article/details/127196603