


[Python] Web Crawler (11): Show your sword! The crawler framework Scrapy makes its debut!
The previous ten chapters of crawler notes have successively recorded some simple Python crawler knowledge,
can be used to solve simple Tieba downloads, and the calculation of grade points is naturally a no-brainer.
But if you want to download a large amount of content in batches, such as all the questions and answers on Zhihu, it seems a bit difficult.
Ever since, the crawler framework Scrapy has appeared!
Scrapy = Scrach+Python. The word Scrach means scraping.
For the time being, we can call it: small scratch.
Xiaozhuazhao’s official website address: Click me, click me.
#Then let’s briefly demonstrate the installation process of Scrapy.
For specific procedures, please refer to: Official website tutorial
Friendly reminder: Be sure to download according to the Python version, otherwise you will be reminded that Python cannot be found during installation. It is recommended that you install 32-bit because some 64-bit versions of essential software are difficult to find.
1. Install Python (32-bit recommended)
It is recommended to install Python2.7.x, 3.x does not seem to be supported yet.
After installation, remember to configure the environment and add the python directory and the Scripts directory under the python directory to the Path of the system environment variable.
Enter python in cmd. If version information appears, the configuration is complete.
2. Install lxml
lxml is a library written in Python that can process XML quickly and flexibly. Click here to select the corresponding Python version to install.
3. Install setuptools
is used to install the egg file. Click here to download the corresponding version of setuptools for python2.7.
4. Install zope.interface
You can use the setuptools downloaded in the third step to install the egg file. There is also an exe version now. Click here to download.
5. Install Twisted
Twisted is an event-driven network engine framework implemented in Python. Click here to download.
6. Install pyOpenSSL
pyOpenSSL is Python’s OpenSSL interface, click here to download.
7. Install win32py
Provides win32api, click here to download
8 .Install Scrapy
Finally it’s time to get excited! After installing so many widgets, it’s finally the protagonist’s turn.
Directly enter easy_install scrapy in cmd and press Enter.
9. Check the installation
Open a cmd window and execute the scrapy command anywhere. If you get the following page, it means the environment configuration is successful.
The above is [Python] Web Crawler (11): Show your sword! The crawler framework Scrapy makes its debut! For more related content, please pay attention to the PHP Chinese website (www.php.cn)!

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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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