1. Python, reason 1 that you have to learn:
Python is a widely used high-level programming language. Its advantages include:
1. Easy to learn: Python’s syntax is concise and clear, and the learning curve is low, making it suitable for beginners.
2. Open source and free: Python is open source, free to use, and has rich libraries and resources to meet various development needs.
3. Cross-platform: Python supports multiple operating systems, such as Windows, macOS, Linux, etc., and has strong portability.
4. Interpretability: Python is an interpreted language that does not require compilation and can be run directly, which is convenient and fast.
5. Object-oriented: Python supports object-oriented programming, which can better organize and manage code.
6. Rich libraries: Python has a wealth of third-party libraries, such as NumPy, Pandas, Matplotlib, etc., which can easily perform data processing, drawing and other operations.
7. Multi-threading: Python supports multi-thread programming, which can improve the running efficiency of the program.
8. Wide application: Python can be used in various fields, such as web development, data analysis, artificial intelligence, game development, etc.
2.Python, two reasons why you have to learn:
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Enhanced core functionality: Python libraries add endless possibilities to our code like magic. These libraries provide a variety of functions. For example, NumPy provides high-performance numerical calculations, Pandas simplifies data processing, Matplotlib beautifies charts, and Seaborn is full of statistical charm. These libraries are like a palette of colors that bring life and inspiration to our code.
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Extended functions: Python libraries are like adding wings to our projects, allowing us to fly higher. Through libraries, we can easily build and train machine learning models. For example, Scikit-learn provides implementations of various machine learning algorithms, and TensorFlow and PyTorch provide powerful tools for deep learning. These libraries are like our assistants, helping us achieve complex tasks and making our code smarter.
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Simplify development: The Python library is like a very experienced and wise mentor, guiding us on the development road. Flask and Django are two commonly used web frameworks that provide simple and powerful tools to help us quickly build web applications. SQLAlchemy simplifies interacting with databases, and Requests makes communicating with Web APIs easy. These libraries are like simplified versions of coding secrets, making our code more concise and efficient.
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Community support: The Python library has built a vast community network for us, which is full of enthusiasm and creativity. There are many developers who contribute to various libraries, and they provide rich documentation, tutorials, and sample code, making it easier to master and apply these libraries. This community is like a treasure trove of knowledge, answering our questions and providing inspiration so that we can grow quickly.
Overall, Python libraries are like a magic wand. They provide a rich variety of functions and tools that allow us to develop with amazing efficiency and creativity. They extend the capabilities of the Python language and enable us to handle a variety of complex tasks, whether it is data analysis, machine learning, web development or image processing. These libraries are like our right-hand assistants, making our code shine and making the development path smoother!
3.Python’s top 30 plug-in libraries:
Python's third-party libraries are like bright gems, shining with charming light and adding a lot of color to the Python language.
I'm very sorry, let me introduce to you in more vivid language the purpose, usage and cases of each plug-in library:
1. NumPy: This is a powerful numerical calculation and multi-dimensional array manipulation library. It provides high-performance mathematical functions and array operations and plays an important role in the field of scientific computing. For example, in data analysis, image processing, and machine learning, NumPy's array objects can easily store and manipulate large amounts of data. For example, we can use NumPy for matrix operations, array indexing, and slicing operations.
2. pandas: This is a cute data processing and analysis assistant. The pandas library provides fast, flexible and efficient data structures and data manipulation tools, making data cleaning, aggregation, grouping and statistics very simple. With pandas, we can easily read and write data in different formats such as CSV, Excel, and database. For example, we can use pandas to filter, sort and calculate statistical indicators on large data sets.
3. Matplotlib: This is a talented mathematical painter who is good at creating various static, dynamic and interactive visualization charts. Matpoltlib is a full-featured plotting library that can create various chart types such as line charts, scatter plots, and bar charts. We can use Matplotlib to display the distribution, trends and relationships between different variables of data. For example, we can use Matplotlib to draw stock price charts or bubble charts of temperature changes.
4. Requests: This library is an excellent Internet app for sending HTTP requests and interacting with Web APIs. When we surf the Internet, requests play a role in helping us easily obtain web content and send various types of requests. We can use the requests library to communicate with the server and get the response content. For example, we can use it to crawl web data, download files, or interact with web services.
5. BeautifulSoup: This is a smart parser that is good at extracting useful information from messy HTML and XML documents. BeautifulSoup provides a simple and flexible API that allows us to easily traverse, search, and modify the tag tree of a web page. For example, we can use the BeautifulSoup library to crawl the titles, text, and links of news websites, or extract key information from XML files.
6. scikit-learn (Scike-Lein): This is a machine learning master that can help us implement various machine learning algorithms and data processing tools. Using scikit-learn, we can perform common tasks such as data preprocessing, feature selection, model training and evaluation. For example, we can use scikit-learn to build a classifier to predict whether an email is spam, or use a regression model to predict house prices.
7. TensorFlow: This is a powerful deep learning framework that can be used to create and train various neural network models. TensorFlow provides flexible APIs and computational graph concepts, making it easy to build complex neural networks. We can use TensorFlow to solve problems such as image classification, natural language processing, and speech recognition. For example, we can use TensorFlow to build a convolutional neural network to identify objects in images.
8. PyTorch: This is another well-respected deep learning framework that provides an easy-to-use interface and the concept of dynamic computational graphs. PyTorch makes building and training neural networks more flexible and intuitive. We can use PyTorch to solve tasks such as image generation, reinforcement learning, and natural language processing. For example, we can use PyTorch to build a recurrent neural network to generate headlines for news articles.
9. Django: This is an excellent web development master who provides us with an advanced Python framework for building powerful web applications. Django has rich built-in functions and easy-to-use APIs, making developing web applications more efficient and convenient. We can use Django to build various types of websites, such as blogs, e-commerce platforms, and social networks. For example, we can use Django to create an online shopping website, including user authentication, product display and shopping cart functions.
10. Flask: This is a small and flexible web framework, suitable for building small, streamlined web applications. Flask provides simple yet powerful tools that support functions such as URL routing, template rendering, and messaging. Although smaller in scale, Flask can still build fully functional web applications. For example, we can use Flask to build a simple blog system, including article publishing, commenting and user registration functions.
Of course, here is an introduction to the next five plug-in libraries:
11. OpenCV : This is a powerful computer vision library suitable for processing image and video data. OpenCV provides a wealth of image processing and computer vision algorithms, such as image filtering, feature detection and target tracking. We can use OpenCV to process images, such as edge detection, face recognition, image segmentation and other tasks. For example, we can use OpenCV to implement a real-time face recognition system.
12. NLTK: This is a natural language processing expert that provides us with a rich and powerful toolkit. NLTK can help us process and analyze text data, such as word segmentation, part-of-speech tagging and sentiment analysis. We can use NLTK to build text classifiers, generate natural language descriptions, or perform information extraction. For example, we can use NLTK to build a spam filter.
13. SQLAlchemy (Sem Alkanmi): This is a flexible and powerful Python database toolkit. SQLAlchemy provides object-relational mapping (ORM) functionality, making interacting with the database easier to manage and maintain. We can use SQLAlchemy to connect to different types of databases and perform data query, insert, and update operations. For example, we can use SQLAlchemy to build a department management system, including the storage and query of employee information.
14. Seaborn: This is a data visualization artist who focuses on creating statistical graphics and information visualization. Seaborn is based on the Matplotlib library and provides a more advanced interface and more beautiful graphic styles. With Seaborn, we can easily create various statistical charts, such as box plots, heat maps, and violin plots. For example, we can use Seaborn to visualize sales across different channels in sales data.
15. Pygame: This is an interesting game developer that provides us with a Python library for creating games and multimedia applications. Pygame is based on the SDL library and provides simple yet powerful functions such as graphics rendering, audio playback and user input processing. We can use Pygame to build various types of games, from simple platform jumping games to complex role-playing games. For example, we can use Pygame to create a shooting game similar to the classic game "Space Invaders".
16. Matplotlib (Matt Pulley): This is a powerful data visualization library for creating various types of charts and plots. Matplotlib provides a wide range of drawing options, including line charts, scatter charts, histograms, etc., and supports customization and beautification of charts. We can use Matplotlib to display information such as trends, relationships, or distributions of data. For example, we can use Matplotlib to create a fitting curve for a linear regression model.
17. NumPy (NumPy): This is a powerful numerical calculation library for processing multi-dimensional arrays and performing various numerical calculation tasks. NumPy provides efficient array operations and mathematical functions, making processing large-scale data more convenient and efficient. We can use NumPy to perform operations such as array creation, indexing, operations, and statistical analysis. For example, we can use NumPy to calculate the mean and standard deviation of an array or perform matrix operations.
18. Pandas: This is a feature-rich data analysis library that provides efficient data structures and data processing tools. Pandas mainly uses two data structures: DataFrame and Series, which can be used for tasks such as data cleaning, conversion, aggregation and analysis. We can use Pandas to load data from various data sources and perform data filtering, sorting and statistics operations. For example, we can use Pandas to perform summary statistics and visual analysis of sales data.
19. Keras: This is an easy-to-use and highly flexible neural network library for building and training deep learning models. Keras provides a concise API and rich neural network components, making it faster and more convenient to create various types of neural networks. We can use Keras to build convolutional neural networks, recurrent neural networks, or custom deep learning models. For example, we can use Keras to build an image classifier to identify pictures of cats and dogs.
20. SciPy: This is an open source scientific computing library that provides a variety of algorithms and tools for solving problems in science and engineering. SciPy contains many submodules that can be used for tasks such as numerical optimization, signal processing, image processing, statistical analysis, and differential equation solving. We can use SciPy to solve various scientific computing problems, such as peak signal detection, image filtering and function fitting. For example, we can use SciPy to perform Fourier transform on a set of signals to analyze their spectral characteristics.
For example, drawing: I want to use python to draw, which plug-in should I use?
In Python, there are many libraries and plug-ins that can be used to draw graphics. Some of the most popular include:
1. Matplotlib: Matplotlib is a widely used Python plotting library that provides several different plotting interfaces that allow you to easily create line plots, scatter plots, bar plots, contour plots, 3D graphics, etc.
2. Seaborn: Seaborn is a Python data visualization library based on Matplotlib. It provides more advanced drawing functions, including classification plots, heat maps, violin plots, faceted plots, time series plots, etc.
3. Plotly: Plotly is a Python data visualization library that provides interactive drawing functions, allowing users to create various types of charts, such as scatter plots, bar charts, line charts, contour charts, etc., and Connect with Plotly's online chart editor.
4. Bokeh: Bokeh is an interactive Python drawing library that provides functions for creating various types of charts, including scatter plots, bar charts, line charts, heat maps, etc., and supports responsive and real-time data streaming. app.
Write a python code to draw a teddy
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
# 创建画布和坐标轴
fig, ax = plt.subplots()
# 绘制身体
body = Circle((0, 0), 1, fc='brown')
ax.add_patch(body)
# 绘制头部
head = Circle((0, 0), 0.6, fc='brown')
ax.add_patch(head)
# 绘制眼睛
eye1 = Circle((-0.2, 0.3), 0.1, fc='white')
ax.add_patch(eye1)
eye2 = Circle((0.2, 0.3), 0.1, fc='white')
ax.add_patch(eye2)
# 绘制鼻子
nose = Circle((0, 0), 0.05, fc='black')
ax.add_patch(nose)
# 绘制嘴巴
mouth = Circle((0, -0.2), 0.2, fc='black')
ax.add_patch(mouth)
# 调整坐标轴
ax.set_xlim(-1.5, 1.5)
ax.set_ylim(-1.5, 1.5)
ax.set_aspect('equal')
# 隐藏坐标轴
ax.axis('off')
# 显示绘制结果
plt.show()
Write a web framework
Create a staticsubfolder called , add cssand imagestwo subfolders inside it. Save style.cssthe file to cssthe folder, save the pictures you need to display to imagesthe folder.
Please make sure you have installed the Flask library and python app.pyrun the application using the command. After that, enter it in your browser http://127.0.0.1:5000/to view your website.
........It's all very simple