一篇经典Python编程常用的30个操作以及代码演示

这些案例将涵盖数据处理、算法、文件操作、数据可视化、网络编程、机器学习等多个领域.

以下是具体的操作步骤和示例代码：

基础操作

1. 计算两个数的和

def add(a, b):  return a + b  
print(add(3, 5))  

2. 判断一个数是否为偶数

def is_even(n):  return n % 2 == 0  
print(is_even(4))  

3. 计算列表中所有数的平均值

def average(lst):  return sum(lst) / len(lst)  print(average([1, 2, 3, 4, 5]))  

4. 反转字符串

def reverse_string(s):  return s[::-1]  print(reverse_string("hello"))  

数据处理

5. 读取CSV文件

import pandas as pd  df = pd.read_csv('data.csv')  
print(df.head())  

6. 写入CSV文件

df.to_csv('output.csv', index=False)  

7. 处理缺失值

df.fillna(df.mean(), inplace=True)  

8. 数据分组和聚合

grouped = df.groupby('category').sum()  print(grouped)  

算法

9. 冒泡排序

def bubble_sort(arr):  n = len(arr)  for i in range(n):  for j in range(0, n-i-1):  if arr[j] > arr[j+1]:  arr[j], arr[j+1] = arr[j+1], arr[j]  return arr  print(bubble_sort([64, 34, 25, 12, 22, 11, 90]))  
10. 二分查找  
def binary_search(arr, x):  l, r = 0, len(arr) - 1  while l <= r:  mid = (l + r) // 2  if arr[mid] == x:  return mid  elif arr[mid] < x:  l = mid + 1  else:  r = mid - 1  return -1  print(binary_search([1, 2, 3, 4, 5, 6, 7], 4))  

11. 斐波那契数列

def fibonacci(n):  if n <= 0:  return []  elif n == 1:  return [0]  elif n == 2:  return [0, 1]  fibs = [0, 1]  for i in range(2, n):  fibs.append(fibs[-1] + fibs[-2])  return fibs  print(fibonacci(10))  

12. 求阶乘

def factorial(n):  if n == 0:  return 1  else:  return n * factorial(n-1)  print(factorial(5))  

文件操作

13. 读取文本文件

with open('file.txt', 'r') as file:  content = file.read()  print(content)  

14. 写入文本文件

with open('output.txt', 'w') as file:  file.write("Hello, world!")  

15. 文件复制

def copy_file(src, dest):  with open(src, 'r') as f_src:  content = f_src.read()  with open(dest, 'w') as f_dest:  f_dest.write(content)  copy_file('file.txt', 'copy.txt')  

数据可视化

16. 绘制折线图

import matplotlib.pyplot as plt  x = [1, 2, 3, 4, 5]  
y = [2, 3, 5, 7, 11]  
plt.plot(x, y)  
plt.xlabel('x')  
plt.ylabel('y')  
plt.title('折线图')  
plt.show()  

17. 绘制柱状图

plt.bar(x, y)  
plt.xlabel('x')  
plt.ylabel('y')  
plt.title('柱状图')  
plt.show()  

18. 绘制散点图

plt.scatter(x, y)  
plt.xlabel('x')  
plt.ylabel('y')  
plt.title('散点图')  
plt.show()  

19. 绘制饼图

labels = 'A', 'B', 'C', 'D'  
sizes = [15, 30, 45, 10]  
plt.pie(sizes, labels=labels, autopct='%1.1f%%')  
plt.title('饼图')  
plt.show()  

统计分析

20. 计算均值和标准差

import numpy as np  data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  
mean = np.mean(data)  
std_dev = np.std(data)  
print(f'均值: {mean}, 标准差: {std_dev}')  

21. 计算相关系数

x = [1, 2, 3, 4, 5]  
y = [2, 3, 5, 7, 11]  
correlation = np.corrcoef(x, y)[0, 1]  
print(f'相关系数: {correlation}')  

22. 线性回归

from sklearn.linear_model import LinearRegression  x = np.array(x).reshape(-1, 1)  
y = np.array(y)  
model = LinearRegression().fit(x, y)  
print(f'截距: {model.intercept_}, 斜率: {model.coef_[0]}')  

网络编程

23. 发送HTTP请求

import requests  response = requests.get('https://jsonplaceholder.typicode.com/posts/1')  
print(response.json())  

24. 创建一个简单的HTTP服务器

from http.server import SimpleHTTPRequestHandler, HTTPServer  Def run(server_class=HTTPServer, handler_class=SimpleHTTPRequestHandler):  server_address = ('', 8000)  httpd = server_class(server_address, handler_class)  print('Starting httpd...')  httpd.serve_forever()  run()  

机器学习

25. 训练和预测线性回归模型

from sklearn.model_selection import train_test_split  X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)  
model = LinearRegression().fit(X_train, y_train)  
predictions = model.predict(X_test)  
print(predictions)  

26. K近邻分类

from sklearn.neighbors import KNeighborsClassifier  knn = KNeighborsClassifier(n_neighbors=3)  
knn.fit(X_train, y_train)  
predictions = knn.predict(X_test)  
print(predictions)  

深度学习

27. 创建一个简单的神经网络

import tensorflow as tf  
from tensorflow.keras.models import Sequential  
from tensorflow.keras.layers import Dense  model = Sequential([  Dense(64, activation='relu', input_shape=(X_train.shape[1],)),  Dense(64, activation='relu'),  Dense(1)  
])  
model.compile(optimizer='adam', loss='mean_squared_error')  
model.fit(X_train, y_train, epochs=10, batch_size=32)  

28. 预测新数据

predictions = model.predict(X_test)  
print(predictions)  

自然语言处理

29. 词频统计

from collections import Counter  
import re  text = "Hello world! Hello everyone. This is a test."  
words = re.findall(r'\w+', text.lower())  
word_counts = Counter(words)  
print(word_counts)  

30. 词云生成

from wordcloud import WordCloud  
import matplotlib.pyplot as plt  wc = WordCloud(width=800, height=400).generate_from_frequencies(word_counts)  
plt.imshow(wc, interpolation='bilinear')  
plt.axis('off')  
plt.show()  

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面向对象编程

31. 创建类和对象

class Dog:  def __init__(self, name, age):  self.name = name  self.age = age  def bark(self):  print(f'{self.name} says woof!')  
my_dog = Dog('Buddy', 3)  
my_dog.bark()  

32. 继承和多态

class Animal:  def __init__(self, name):  self.name = name  def speak(self):  raise NotImplementedError("Subclass must implement abstract method")  
class Dog(Animal):  def speak(self):  return f'{self.name} says woof!'  class Cat(Animal):  def speak(self):  return f'{self.name} says