Learn more about how pip works: interpret the download and installation process of Python packages

This article has selected several top Python "finished" project websites and high-level "blockbuster" learning resource portals for you. Whether you are looking for development inspiration, observing and learning master-level source code, or systematically improving your practical capabilities, these platforms are not to be missed and can help you grow into a Python master quickly.

Use subprocess.run() to safely execute shell commands and capture output. It is recommended to pass parameters in lists to avoid injection risks; 2. When shell characteristics are required, you can set shell=True, but beware of command injection; 3. Use subprocess.Popen to realize real-time output processing; 4. Set check=True to throw exceptions when the command fails; 5. You can directly call chains to obtain output in a simple scenario; you should give priority to subprocess.run() in daily life to avoid using os.system() or deprecated modules. The above methods override the core usage of executing shell commands in Python.

To get started with quantum machine learning (QML), the preferred tool is Python, and libraries such as PennyLane, Qiskit, TensorFlowQuantum or PyTorchQuantum need to be installed; then familiarize yourself with the process by running examples, such as using PennyLane to build a quantum neural network; then implement the model according to the steps of data set preparation, data encoding, building parametric quantum circuits, classic optimizer training, etc.; in actual combat, you should avoid pursuing complex models from the beginning, paying attention to hardware limitations, adopting hybrid model structures, and continuously referring to the latest documents and official documents to follow up on development.

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

Use Seaborn's jointplot to quickly visualize the relationship and distribution between two variables; 2. The basic scatter plot is implemented by sns.jointplot(data=tips,x="total_bill",y="tip",kind="scatter"), the center is a scatter plot, and the histogram is displayed on the upper and lower and right sides; 3. Add regression lines and density information to a kind="reg", and combine marginal_kws to set the edge plot style; 4. When the data volume is large, it is recommended to use "hex"

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.

String lists can be merged with join() method, such as ''.join(words) to get "HelloworldfromPython"; 2. Number lists must be converted to strings with map(str, numbers) or [str(x)forxinnumbers] before joining; 3. Any type list can be directly converted to strings with brackets and quotes, suitable for debugging; 4. Custom formats can be implemented by generator expressions combined with join(), such as '|'.join(f"[{item}]"foriteminitems) output"[a]|[

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.