How to learn Python crawlers?
Learning crawlers requires a certain foundation of Python, and it is easier to learn Python crawlers with a programming foundation. But you have to watch and practice more, and have your own logical ideas. It is valuable to use Python to achieve your own learning goals. If it is an introductory study and understanding, it is not difficult to start learning, but it is difficult to learn in depth, especially for large projects.
Most crawlers follow the process of "sending a request—obtaining a page—parsing the page—extracting and storing content", simulating the process of using a browser to obtain web page information. After sending a request to the server, you will get the returned page. After parsing the page, you can extract the part of the information we want and store it in the specified document or database.
[----Help Python learning, all the following learning materials are free at the end of the article! ----】
Three stages of getting started with Python crawlers:
1. Zero foundation stage
Learn reptiles from scratch, get started with the system, and get started with reptiles from 0. In addition to the necessary theoretical knowledge, the practical application of reptiles is more important. The ability to capture data from mainstream websites is the learning goal at this stage.
Learning points:
- Basic knowledge of computer network/front-end/regular//xpath/CSS selectors required by crawlers;
- Realize data capture of static web pages and dynamic web pages, two major types of web pages;
- Detailed explanation of difficulties such as simulated login, response to anti-climbing, and identification of verification codes;
- Explanation of common application scenarios such as multi-threading and multi-process;
2. Mainstream framework
The mainstream framework Scrapy realizes massive data crawling, and improves the ability from native crawlers to frameworks. After learning, you can thoroughly play with the Scrapy framework and develop your own distributed crawler system, which is fully competent for the work of intermediate Python engineers. Gain the ability to efficiently capture massive amounts of data.
Learning points:
- Scrapy framework knowledge explanation spider/FormRequest/CrawlSpider, etc.;
- Explanation from stand-alone crawler to distributed crawler system;
- Scrapy breaks through the limitations of anti-crawlers and the principle of Scrapy;
- More advanced features of Scrapy include sscrapy signal, custom middleware;
- Existing mass data combined with Elasticsearch to create a search engine;
3. Reptiles
In-depth App data capture, improved crawler capabilities, coping with App data capture and data visualization display, capabilities are no longer limited to web crawlers. From then on, broaden your crawler business and enhance your core competitiveness. Master App data capture to realize data visualization.
Learning points:
- Learn the application of mainstream packet capture tools Fiddler/Mitmproxy;
- App data capture in actual combat, combining learning and practice with in-depth mastery of App crawler skills;
- Create a multi-task grabbing system based on Docker to improve work efficiency;
- Master the basics of the Pyecharts library, draw basic graphics, maps, etc. to achieve data visualization;
Crawler Python is used in many fields, such as crawling data, conducting market research and business analysis; as raw data for machine learning and data mining; crawling high-quality resources: pictures, texts, and videos.
It is very easy to master the correct method and be able to crawl the data of mainstream websites in a short period of time. It is recommended to set a specific goal from the beginning when getting started with reptile Python. Learning will be more efficient when driven by the goal.
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
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