一、引言

二、Python 函数基础概念

2.1 定义函数

def 函数名(参数列表):
    """函数文档字符串，用于描述函数的功能、参数和返回值等信息"""
    函数体
    return 返回值

def add_numbers(a, b):
    """
    计算两个数的和并返回结果。
    :param a: 第一个数
    :param b: 第二个数
    :return: 两数之和
    """
    result = a + b
    return result

2.2 调用函数

sum_result = add_numbers(3, 5)
print(sum_result)  

def modify_list(lst):
    lst.append(4)
    return lst
my_list = [1, 2, 3]
new_list = modify_list(my_list)
print(new_list)  
print(my_list)  

2.3 函数参数

2.3.1 必选参数

def greet(name):
    print(f"Hello, {name}!")
greet("Alice") 

2.3.2 默认参数

def greet(name, age=18):
    print(f"Hello, {name}! You are {age} years old.")
greet("Bob") 
greet("Charlie", 25) 

def append_item(item, lst=[]):
    lst.append(item)
    return lst
print(append_item(1)) 
print(append_item(2)) 

def append_item(item, lst=None):
    if lst is None:
        lst = []
    lst.append(item)
    return lst
print(append_item(1)) 
print(append_item(2)) 

2.3.3 可变参数

def sum_numbers(*args):
    total = 0
    for num in args:
        total += num
    return total
result1 = sum_numbers(1, 2, 3)
result2 = sum_numbers(1, 2, 3, 4, 5)
print(result1) 
print(result2) 

2.3.4 关键字参数

def print_info(name, age, **kwargs):
    print(f"Name: {name}, Age: {age}")
    for key, value in kwargs.items():
        print(f"{key}: {value}")
print_info("Tom", 25, city="New York", job="Engineer")

2.3.5 参数组合

def complex_function(a, b=10, *args, **kwargs):
    print(f"a: {a}, b: {b}")
    print(f"args: {args}")
    print(f"kwargs: {kwargs}")
complex_function(1, 2, 3, 4, c=5, d=6)

2.4 返回值

def square(x):
    return x * x
result = square(5)
print(result)  

def divide_and_remainder(x, y):
    quotient = x // y
    remainder = x % y
    return quotient, remainder
q, r = divide_and_remainder(10, 3)
print(f"Quotient: {q}, Remainder: {r}")

def greet(name):
    print(f"Hello, {name}!")
result = greet("Alice")
print(result)  

三、Python 函数高级特性

3.1 变量作用域

def local_variable_demo():
    x = 10  
    print(x)  
local_variable_demo()  
# print(x)  # 这行代码会报错，因为x是局部变量，在函数外部不可见

y = 20  
def global_variable_demo():
    print(y)  
global_variable_demo()  
print(y)  

count = 0  
def increment():
    global count
    count = count + 1
    return count
print(increment())  
print(increment())  

3.2 匿名函数（lambda 函数）

lambda 参数列表: 表达式

add = lambda a, b: a + b
result = add(3, 5)
print(result)  

students = [
    {'name': 'Alice', 'age': 20},
    {'name': 'Bob', 'age': 18},
    {'name': 'Charlie', 'age': 22}
]
sorted_students = sorted(students, key=lambda student: student['age'])
print(sorted_students) 

3.3 内置函数

numbers = [1, 2, 3, 4, 5]
total = sum(numbers)
print(total)  

max_num = max([10, 5, 15, 8])
print(max_num)  
max_value = max(10, 20, 15)
print(max_value)  

nums = [5, 3, 8, 1, 2]
sorted_nums = sorted(nums)
print(sorted_nums)  

words = ['apple', 'banana', 'cherry', 'date']
sorted_words = sorted(words, key=lambda word: len(word))
print(sorted_words) 

3.4 装饰器

def log_decorator(func):
    def wrapper(*args, **kwargs):
        print(f"开始调用函数 {func.__name__}")
        result = func(*args, **kwargs)
        print(f"函数 {func.__name__} 调用结束")
        return result
    return wrapper
@log_decorator
def add(a, b):
    return a + b
sum_result = add(3, 5)
print(sum_result)

3.5 生成器

squares_generator = (i ** 2 for i in range(1, 11))
for square in squares_generator:
    print(square)  

def fibonacci_generator():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b
fib = fibonacci_generator()
for _ in range(10):
    print(next(fib))  

3.6 偏函数

num = int('1010', base=2)
print(num)  

from functools import partial
bin_to_int = partial(int, base=2)
num = bin_to_int('1010')
print(num)  

3.7 闭包

def outer_function(x):
    def inner_function(y):
        return x + y
    return inner_function
add_five = outer_function(5)
result = add_five(3)
print(result)  

def counter():
    count = 0
    def inner():
        nonlocal count
        count = count + 1
        return count
    return inner
my_counter = counter()
print(my_counter())  
print(my_counter())  
print(my_counter())  

3.8 递归

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

四、Python 函数在实际项目中的应用

4.1 数据处理与分析

import csv
def read_csv_data(file_path):
    data = []
    with open(file_path, 'r', encoding='utf-8') as csvfile:
        reader = csv.DictReader(csvfile)
        for row in reader:
            data.append(dict(row))
    return data
sales_data = read_csv_data('sales_data.csv')

def calculate_total_sales(data):
    total_sales = 0
    for order in data:
        total_sales += float(order['购买金额'])
    return total_sales
def calculate_average_purchase_per_customer(data):
    customer_purchases = {}
    for order in data:
        customer_id = order['客户ID']
        purchase_amount = float(order['购买金额'])
        if customer_id not in customer_purchases:
            customer_purchases[customer_id] = []
        customer_purchases[customer_id].append(purchase_amount)
    average_purchases = {}
    for customer_id, purchases in customer_purchases.items():
        average_purchases[customer_id] = sum(purchases) / len(purchases)
    return average_purchases
total_sales = calculate_total_sales(sales_data)
average_purchases = calculate_average_purchase_per_customer(sales_data)
print(f"总销售额: {total_sales}")
print(f"每个客户的平均购买金额: {average_purchases}")

4.2 机器学习与人工智能

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# 生成模拟数据
np.random.seed(0)
X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

def split_data(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    return X_train, X_test, y_train, y_test
def train_model(X_train, y_train):
    model = LinearRegression()
    model.fit(X_train, y_train)
    return model
def predict_and_evaluate(model, X_test, y_test):
    y_pred = model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    return y_pred, mse
X_train, X_test, y_train, y_test = split_data(X, y)
model = train_model(X_train, y_train)
y_pred, mse = predict_and_evaluate(model, X_test, y_test)
print(f"均方误差: {mse}")

4.3 Web 开发

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/login', methods=['POST'])
def login():
    data = request.get_json()
    username = data.get('username')
    password = data.get('password')
    # 这里可以添加实际的用户验证逻辑，例如查询数据库
    if username == 'admin' and password == '123456':
        return jsonify({'message': '登录成功'}), 200
    else:
        return jsonify({'message': '用户名或密码错误'}), 401