头条 Python 编程题：掌握核心技巧，解决实际问题156

欢迎来到头条 Python 编程题专题，我们将一起探索一系列精彩的 Python 编程题，涵盖各种主题，从基础数据结构到算法和面向对象编程，一步步提升你的 Python 编程技能。通过解决这些题目，你将学到核心概念，掌握 Python 的强大功能，并将其应用于解决实际问题。

1. 初级 Python 编程题

1.1 翻转字符串

编写一个 Python 函数，翻转给定的字符串。```python
def reverse_str(string):
return string[::-1]
```

1.2 查找列表中的最大值

编写一个 Python 函数，在给定的列表中找到最大的元素。```python
def find_max(list1):
return max(list1)
```

1.3 计算质数个数

编写一个 Python 函数，计算给定范围内（包括边界）的质数个数。```python
def count_primes(n):
count = 0
for i in range(2, n+1):
if all(i % j != 0 for j in range(2, int(i0.5) + 1)):
count += 1
return count
```

2. 中级 Python 编程题

2.1 判断回文数

编写一个 Python 函数，判断给定的数字是否是回文数。```python
def is_palindrome(n):
str_n = str(n)
return str_n == str_n[::-1]
```

2.2 查找最长公共子序列

编写一个 Python 函数，查找给定两个字符串的最长公共子序列。```python
def longest_common_subsequence(str1, str2):
dp = [[0] * (len(str2)+1) for _ in range(len(str1)+1)]
for i in range(1, len(str1)+1):
for j in range(1, len(str2)+1):
if str1[i-1] == str2[j-1]:
dp[i][j] = dp[i-1][j-1] + 1
else:
dp[i][j] = max(dp[i-1][j], dp[i][j-1])
return dp[-1][-1]
```

2.3 实现快速排序

编写一个 Python 函数，实现快速排序算法。```python
def quick_sort(arr):
if len(arr) pivot]
return quick_sort(left) + middle + quick_sort(right)
```

3. 高级 Python 编程题

3.1 设计面向对象的银行系统

设计一个 Python 类，表示银行系统，其中包含客户、账户和交易等基本功能。```python
class Bank:
def __init__(self):
= {}
= {}
def create_customer(self, name):
...
def create_account(self, customer, account_type):
...
def deposit(self, account, amount):
...
def withdraw(self, account, amount):
...
def transfer(self, from_account, to_account, amount):
...
```

3.2 编写多线程爬虫

使用 Python 编写一个多线程爬虫，从给定的 URL 列表中抓取网页内容。```python
import threading
import requests
def crawl(url):
try:
response = (url)
if response.status_code == 200:
print(f'Successfully crawled {url}')
except:
print(f'Failed to crawl {url}')
urls = ['url1', 'url2', 'url3', ...]
threads = []
for url in urls:
thread = (target=crawl, args=(url,))
(thread)
for thread in threads:
()
for thread in threads:
()
```

3.3 训练神经网络进行图像分类

使用 Python 编写代码，使用 TensorFlow 框架训练一个神经网络来进行图像分类。```python
import tensorflow as tf
# Load the dataset
(x_train, y_train), (x_test, y_test) = .load_data()
# Preprocess the data
x_train = ('float32') / 255.0
x_test = ('float32') / 255.0
# Create the neural network model
model = ([
(input_shape=(28, 28)),
(128, activation='relu'),
(0.2),
(10, activation='softmax')
])
# Compile the model
(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
(x_train, y_train, epochs=10)
# Evaluate the model
(x_test, y_test)
```