Python作为一种高效、简洁且功能强大的编程语言,被广泛应用于各个领域。为了帮助大家更高效地编写代码,本文整理了一些常用的Python代码实例,涵盖数据处理、操作系统、网络编程等多个方面。这些代码片段不仅实用,而且易于理解和应用。
一、数据处理
列表去重:
my_list = [1, 2, 2, 3, 4, 4, 5] unique_list = list(set(my_list)) print(unique_list)# 输出: [1, 2, 3, 4, 5]
列表元素求和:
my_list = [1, 2, 3, 4, 5] total = sum(my_list) print(total)# 输出: 15
字典按值排序:
my_dict = {'a': 2, 'b': 1, 'c': 3}
sorted_dict = dict(sorted(my_dict.items(), key=lambda item: item[1]))
print(sorted_dict)
# 输出: {'b': 1, 'a': 2, 'c': 3}列表推导式:
squares = [x**2 for x in range(10)]print(squares) # 输出: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
二、操作系统
获取当前工作目录:
import oscurrent_directory = os.getcwd() print(current_directory)
列出目录中的所有文件和文件夹:
import ositems = os.listdir('.')
print(items)检查文件是否存在:
import osfile_exists = os.path.isfile('path/to/file.txt')
print(file_exists)创建新目录:
import osos.makedirs('new_directory', exist_ok=True)
print("Directory created successfully")三、网络编程
HTTP GET 请求:
import requests
response = requests.get('
print(response.status_code)print(response.json())抓取网页内容:
import requests from bs4 import BeautifulSoup url = 'https://www.example.com'response = requests.get(url) soup = BeautifulSoup(response.content, 'html.parser') print(soup.prettify())
发送电子邮件:
import smtplibfrom email.mime.text
import MIMETextfrom email.mime.multipart
import MIMEMultipart
sender_email = "your_email@gmail.com"
receiver_email = "receiver_email@gmail.com"
password = "your_password"
subject = "Test Email"
body = "This is a test email."
# 创建MIMEMultipart对象msg = MIMEMultipart()
msg['From'] = sender_email
msg['To'] = receiver_email
msg['Subject'] = subject# 添加邮件正文msg.attach(MIMEText(body, 'plain'))
# 登录邮件服务器并发送邮件
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(sender_email, password)
text = msg.as_string()
server.sendmail(sender_email, receiver_email, text)
server.quit()print("Email sent successfully")四、文件处理
读取文件内容:
with open('file.txt', 'r') as file:
content = file.read()
print(content)写入文件:
with open('file.txt', 'w') as file:
file.write('Hello, World!')逐行读取文件:
with open('file.txt', 'r') as file:
for line in file:
print(line.strip())五、数据分析与可视化
读取CSV文件:
import pandas as pd
df = pd.read_csv('file.csv')
print(df.head())数据绘图:
import matplotlib.pyplot as pltdata = [1, 2, 3, 4, 5]
plt.plot(data)
plt.title('Simple Plot')
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')
plt.show()散点图:
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.scatter(x, y)
plt.title('Scatter Plot'
)plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')plt.show()六、机器学习
简单线性回归:
from sklearn.linear_model import LinearRegressionimport numpy as np # 创建数据集 X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) y = np.array([1, 3, 2, 4, 5]) # 创建线性回归模型 model = LinearRegression()model.fit(X, y) # 预测predictions = model.predict(X)print(predictions)
加载和划分数据集:
from sklearn.datasets
import load_irisfrom sklearn.model_selection
import train_test_split
# 加载数据集data = load_iris()
X = data.data
y = data.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print("Training Set:", X_train.shape, y_train.shape)
print("Test Set:", X_test.shape, y_test.shape)结语
以上是一些常用的Python代码片段,涵盖了数据处理、文件操作、网络编程、数据分析和机器学习等多个方面。这些代码可以帮助你在实际工作中提高效率和生产力。希望本文能成为你编程过程中的一个有用参考。如果你有任何问题或想了解更多内容,欢迎在评论区留言讨论。一起在Python编程的道路上不断进步!
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