Python是一种非常强大且易于学习的编程语言，适合初学者和专业开发者。以下是21个有趣的Python脚本示例，涵盖了从入门到精通的不同方面。 1. **Hello World**: ```python print("Hello, World!") ``` 2. **计算两个数的和**: ```python def add_numbers(a, b): return a + b result = add_numbers(3, 5) print(result) ``` 3. **判断一个数是奇数还是偶数**: ```python def check_odd_even(num): if num % 2 == 0: return "Even" else: return "Odd" print(check_odd_even(4)) ``` 4. **生成斐波那契数列**: ```python def fibonacci(n): a, b = 0, 1 for _ in range(n): yield a a, b = b, a + b for number in fibonacci(10): print(number) ``` 5. **列表推导式**: ```python squares = [x**2 for x in range(10)] print(squares) ``` 6. **读取文件内容**: ```python with open('example.txt', 'r') as file: content = file.read() print(content) ``` 7. **写入文件内容**: ```python with open('output.txt', 'w') as file: file.write("Hello, File!") ``` 8. **使用正则表达式提取邮箱地址**: ```python import re text = "Please contact us at support@example.com for further information." email = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', text) print(email) ``` 9. **绘制简单的条形图**: ```python import matplotlib.pyplot as plt data = [25, 20, 15, 10] plt.bar(range(len(data)), data) plt.show() ``` 10. **发送HTTP请求**: ```python import requests response = requests.get('https://api.github.com') print(response.status_code) ``` 11. **解析JSON数据**: ```python import json json_data = '{"name": "John", "age": 30}' data = json.loads(json_data) print(data['name']) ``` 12. **创建一个简单的Web服务器**: ```python from http.server import SimpleHTTPRequestHandler, HTTPServer handler = SimpleHTTPRequestHandler httpd = HTTPServer(('localhost', 8000), handler) httpd.serve_forever() ``` 13. **使用Pandas处理CSV文件**: ```python import pandas as pd df = pd.read_csv('data.csv') print(df.head()) ``` 14. **使用NumPy进行数组操作**: ```python import numpy as np array = np.array([1, 2, 3, 4]) print(array * 2) ``` 15. **使用Scikit-learn进行机器学习分类**: ```python from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score iris = datasets.load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3) clf = RandomForestClassifier()