In the currency circle, many veteran players will recommend that novices start with Bitcoin (BTC) or Ethereum (ETH). This is not a casual statement, but a consensus that has been verified by many years of markets.

The key to using Python to call WebAPI to obtain data is to master the basic processes and common tools. 1. Using requests to initiate HTTP requests is the most direct way. Use the get method to obtain the response and use json() to parse the data; 2. For APIs that need authentication, you can add tokens or keys through headers; 3. You need to check the response status code, it is recommended to use response.raise_for_status() to automatically handle exceptions; 4. Facing the paging interface, you can request different pages in turn and add delays to avoid frequency limitations; 5. When processing the returned JSON data, you need to extract information according to the structure, and complex data can be converted to Data

In Python, the following points should be noted when merging strings using the join() method: 1. Use the str.join() method, the previous string is used as a linker when calling, and the iterable object in the brackets contains the string to be connected; 2. Make sure that the elements in the list are all strings, and if they contain non-string types, they need to be converted first; 3. When processing nested lists, you must flatten the structure before connecting.

Magic methods (dunder methods) in Python are special methods used to customize object behavior. They start and end with a double underscore, such as __init__ or __str__, and are automatically triggered when a specific syntax or built-in function is used. 1.__init__ is used to initialize the object; 2.__str__ and __repr__ define the readable string representation and reconstructible expression of the object respectively; 3.__add__, __sub__, etc. define the addition and subtraction operation behaviors; 4. Control and comparison operations such as __eq__, __lt__, etc. Implementing these methods, such as adding __add__ to custom class Point to support operations, makes the class behave more naturally and in line with expectations. make

To master Python web crawlers, you need to grasp three core steps: 1. Use requests to initiate a request, obtain web page content through get method, pay attention to setting headers, handling exceptions, and complying with robots.txt; 2. Use BeautifulSoup or XPath to extract data. The former is suitable for simple parsing, while the latter is more flexible and suitable for complex structures; 3. Use Selenium to simulate browser operations for dynamic loading content. Although the speed is slow, it can cope with complex pages. You can also try to find a website API interface to improve efficiency.

There are three common methods for deduplication in Python. 1. Use set deduplication: It is suitable for situations where you don’t care about the order, and is implemented through list(set(my_list)). The advantage is that it is simple and fast, and the disadvantage is to disrupt the order; 2. Manually judge the deduplication: By traversing the original list and determining whether the elements already exist in the new list, the elements that appear for the first time are retained, which is suitable for scenarios where order needs to be maintained; 3. dict.fromkeys() deduplication: supported by Python 3.7, implemented through list(dict.fromkeys(my_list)), which maintains both the order and the writing method is concise. It is recommended to use modern Python. Notes include first converting the structure when dealing with non-hashable elements. It is recommended to use large data sets.

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To collect user behavior data, you need to record browsing, search, purchase and other information into the database through PHP, and clean and analyze it to explore interest preferences; 2. The selection of recommendation algorithms should be determined based on data characteristics: based on content, collaborative filtering, rules or mixed recommendations; 3. Collaborative filtering can be implemented in PHP to calculate user cosine similarity, select K nearest neighbors, weighted prediction scores and recommend high-scoring products; 4. Performance evaluation uses accuracy, recall, F1 value and CTR, conversion rate and verify the effect through A/B tests; 5. Cold start problems can be alleviated through product attributes, user registration information, popular recommendations and expert evaluations; 6. Performance optimization methods include cached recommendation results, asynchronous processing, distributed computing and SQL query optimization, thereby improving recommendation efficiency and user experience.