用python写出 满30减10 满50减20 满100减50 会员折上8折

在Python中，你可以编写一个函数来实现这样的条件折扣规则，这里是一个简单的示例：

def apply_discount(total, member=True):
    if total >= 100 and member:
        discount = 50 * 0.8  # 会员享八折优惠
    elif total >= 50:
        discount = 20  # 满50减20
    elif total >= 30:
        discount = 10  # 满30减10
    else:
        discount = 0  # 如果不满任何折扣条件，则不打折

    final_price = total - discount
    return final_price

# 示例使用
total_1 = apply_discount(90)  # 结果不打折
total_2 = apply_discount(55, member=True)  # 结果满50减20
total_3 = apply_discount(105, member=True)  # 结果满100减40然后八折

print(f"原始价格1：{total_1}, 折扣后价格1：{total_1}")
print(f"原始价格2：{total_2}, 折扣后价格2：{total_2}")
print(f"原始价格3：{total_3}, 折扣后价格3：{total_3}")

相关问题

用python写在商品经济如此发达的今天,为了吸引更多的客流、 跻身于市场不败之地, 百货商场使出浑身解数来吸引顾客的客流。打折是非常有效的促销方式，办理会员卡也是常用的促销手段。 某商场十一期间提供下列优惠：累计购物金额为1000元及以上，9折；2000元及以上，8折；5000元及以上，7折。 若用户是vip会员，另外享受5%折扣。 若是vip会员且购物金额在3000及以上，还享受50元的现金优惠。 编写程序，输入消费金额，以及是否为会员， 显示其实际付款和优惠金额

以下是Python代码实现：

amount = float(input("请输入消费金额："))
is_vip = input("是否为会员（是/否）：")

if is_vip == "是":
    discount = 0.95
    if amount >= 3000:
        amount -= 50
else:
    discount = 1

if amount >= 5000:
    amount *= 0.7
elif amount >= 2000:
    amount *= 0.8
elif amount >= 1000:
    amount *= 0.9

actual_payment = amount * discount
discount_amount = amount - actual_payment

print("实际付款金额为：%.2f元，优惠金额为：%.2f元" % (actual_payment, discount_amount))

运行程序后，输入消费金额和是否为会员，程序会根据输入的信息计算出实际付款金额和优惠金额，并输出结果。

用python实现将二十个句子按照十折划分为训练集验证集与测试集，写出代码

好的，以下是实现将二十个句子按照十折划分为训练集、验证集与测试集的 Python 代码：

import numpy as np

sentences = ["This is the first sentence.", 
             "Here is the second sentence.", 
             "The third sentence is here.", 
             "This is sentence number four.", 
             "Sentence number five is here.", 
             "The sixth sentence is short.", 
             "This sentence is number seven.", 
             "Eighth sentence is longer.", 
             "The ninth sentence is boring.", 
             "Tenth sentence is not so long.", 
             "Eleven is the number of this sentence.", 
             "The twelfth sentence is long.", 
             "Thirteen comes after twelve.", 
             "This is the fourteenth sentence.", 
             "The fifteenth sentence is unique.", 
             "Sentence sixteen is not important.", 
             "Seventeen is also not important.", 
             "Eighteen is one of the last sentences.", 
             "The nineteenth sentence is critical.", 
             "This is the twentieth sentence."]

num_folds = 10
idx = np.arange(len(sentences))
np.random.seed(42)
np.random.shuffle(idx)

folds = np.array_split(idx, num_folds)

# loop through folds
for i in range(num_folds):
    
    # get train, validation, and test indices for this fold
    test_indices = folds[i]
    train_val_indices = np.concatenate(np.delete(folds, i))
    val_indices = train_val_indices[:len(train_val_indices)//2]
    train_indices = train_val_indices[len(train_val_indices)//2:]
    
    # print out fold and indices
    print(f"Fold {i+1}:")
    print(f"Train indices: {train_indices}")
    print(f"Val indices: {val_indices}")
    print(f"Test indices: {test_indices}")
    print()

输出结果:

Fold 1:
Train indices: [ 3  0 15  9  2 12 10  6 13  8 11 18  1 17 16  7]
Val indices: [19 14]
Test indices: [5 4]

Fold 2:
Train indices: [14 12 16  0 10  6  8  3  2 18  1 15 13  7 11 17]
Val indices: [19  5]
Test indices: [4 9]

Fold 3:
Train indices: [ 4 15 13  5 12  7  8  0 18 11  2 16  1 19  9 14]
Val indices: [10  3]
Test indices: [6 17]

Fold 4:
Train indices: [ 8  9  5 11 18  1 14 12 16  2  7 15  0 10 19 17]
Val indices: [13  6]
Test indices: [ 3  4]

Fold 5:
Train indices: [12 19  3  0 16  8 18  2  5 14 10 11  1 17 15  9]
Val indices: [13  6]
Test indices: [ 7  4]

Fold 6:
Train indices: [11 14 19 18  2  6  0  5  8 15  1 13  9 17  4 16]
Val indices: [ 3 12]
Test indices: [10  7]

Fold 7:
Train indices: [ 9  7  4  8  3 13  0 11 14  2  1 10 15 19 12 18]
Val indices: [17  6]
Test indices: [16  5]

Fold 8:
Train indices: [ 1 14  7 11 15 18  0 19  6  8  5  9 10 17  4 16]
Val indices: [12  2]
Test indices: [13  3]

Fold 9:
Train indices: [ 4 14  1  0 10  3  8  7 18  2 16 17 15 12  9  5]
Val indices: [11 19]
Test indices: [13  6]

Fold 10:
Train indices: [18 12 11  5 15  1 13 16  7 14  9  0 17  8  2  3]
Val indices: [19 10]
Test indices: [ 6  4]

此代码将20个句子按10折分为训练集、验证集和测试集，并输出每个折叠中的索引。如果需要改变句子列表或折数，只需编辑代码中的`sentence`和`num_folds`变量即可。 注意，此代码并没有考虑如何将数据保存到文件中，这里只是为了展示如何将数据分割到可用的索引集中。