基于规则的推荐：用Python实现带菜系限制的好友餐厅Top4推荐

现有一个包含3位好友、10家餐厅的DataFrame，数据包含好友姓名、餐厅名称、兴趣排名（InterestRank，1表示最感兴趣，10表示最不感兴趣），以及餐厅的菜系（Cuisine）、消费档次（Cost）、是否提供酒精饮品（Alcohol）等属性，原始数据如下：

FriendName,Restaurant,InterestRank,Cuisine,Cost,Alcohol
Amy,R2,1,French,$$,No
Ben,R2,3,French,$$,No
Cathy,R2,8,French,$$,No
Amy,R1,2,French,$$$,Yes
Ben,R1,9,French,$$$,Yes
Cathy,R1,5,French,$$$,Yes
Amy,R4,3,French,$$$,Yes
Ben,R4,5,French,$$$,Yes
Cathy,R4,10,French,$$$,Yes
Amy,R3,4,French,$$,Yes
Ben,R3,10,French,$$,Yes
Cathy,R3,6,French,$$,Yes
Amy,R10,5,Mexican,$$$,Yes
Ben,R10,6,Mexican,$$$,Yes
Cathy,R10,7,Mexican,$$$,Yes
Amy,R7,6,Japanese,$$,Yes
Ben,R7,1,Japanese,$$,Yes
Cathy,R7,9,Japanese,$$,Yes
Amy,R6,7,Japanese,$,No
Ben,R6,8,Japanese,$,No
Cathy,R6,3,Japanese,$,No
Amy,R8,8,Mexican,$$,No
Ben,R8,4,Mexican,$$,No
Cathy,R8,2,Mexican,$$,No
Amy,R5,9,Japanese,$$,No
Ben,R5,2,Japanese,$$,No
Cathy,R5,1,Japanese,$$,No
Amy,R9,10,Mexican,$$,No
Ben,R9,7,Mexican,$$,No
Cathy,R9,4,Mexican,$$,No

需要为每位好友推荐Top4餐厅，要求：

• 按InterestRank升序排序（数值越小越优先）
• 同一菜系的推荐数量不超过2家

预期输出的DataFrame如下：

利用Pandas的分组功能结合自定义筛选逻辑即可实现需求，核心思路是按好友分组后，对每个好友的餐厅列表先按兴趣排名排序，再逐个筛选并控制每个菜系的入选数量，直到选够4家为止。

import pandas as pd

# 构造原始DataFrame（也可通过pd.read_csv读取本地CSV文件）
data = [
    ["Amy", "R2", 1, "French", "$$", "No"],
    ["Ben", "R2", 3, "French", "$$", "No"],
    ["Cathy", "R2", 8, "French", "$$", "No"],
    ["Amy", "R1", 2, "French", "$$$", "Yes"],
    ["Ben", "R1", 9, "French", "$$$", "Yes"],
    ["Cathy", "R1", 5, "French", "$$$", "Yes"],
    ["Amy", "R4", 3, "French", "$$$", "Yes"],
    ["Ben", "R4", 5, "French", "$$$", "Yes"],
    ["Cathy", "R4", 10, "French", "$$$", "Yes"],
    ["Amy", "R3", 4, "French", "$$", "Yes"],
    ["Ben", "R3", 10, "French", "$$", "Yes"],
    ["Cathy", "R3", 6, "French", "$$", "Yes"],
    ["Amy", "R10", 5, "Mexican", "$$$", "Yes"],
    ["Ben", "R10", 6, "Mexican", "$$$", "Yes"],
    ["Cathy", "R10", 7, "Mexican", "$$$", "Yes"],
    ["Amy", "R7", 6, "Japanese", "$$", "Yes"],
    ["Ben", "R7", 1, "Japanese", "$$", "Yes"],
    ["Cathy", "R7", 9, "Japanese", "$$", "Yes"],
    ["Amy", "R6", 7, "Japanese", "$", "No"],
    ["Ben", "R6", 8, "Japanese", "$", "No"],
    ["Cathy", "R6", 3, "Japanese", "$", "No"],
    ["Amy", "R8", 8, "Mexican", "$$", "No"],
    ["Ben", "R8", 4, "Mexican", "$$", "No"],
    ["Cathy", "R8", 2, "Mexican", "$$", "No"],
    ["Amy", "R5", 9, "Japanese", "$$", "No"],
    ["Ben", "R5", 2, "Japanese", "$$", "No"],
    ["Cathy", "R5", 1, "Japanese", "$$", "No"],
    ["Amy", "R9", 10, "Mexican", "$$", "No"],
    ["Ben", "R9", 7, "Mexican", "$$", "No"],
    ["Cathy", "R9", 4, "Mexican", "$$", "No"],
]

df = pd.DataFrame(data, columns=["FriendName", "Restaurant", "InterestRank", "Cuisine", "Cost", "Alcohol"])

def get_top_recommendations(group):
    # 按兴趣排名升序排序，优先推荐兴趣度高的餐厅
    sorted_group = group.sort_values("InterestRank", ascending=True)
    recommendations = []
    cuisine_count = {}
    
    for _, row in sorted_group.iterrows():
        cuisine = row["Cuisine"]
        # 若当前菜系已选2家，跳过该餐厅
        if cuisine_count.get(cuisine, 0) >= 2:
            continue
        recommendations.append(row)
        cuisine_count[cuisine] = cuisine_count.get(cuisine, 0) + 1
        # 选够4家后停止筛选
        if len(recommendations) == 4:
            break
    
    # 转换为DataFrame并添加推荐排名
    rec_df = pd.DataFrame(recommendations)
    rec_df["RecommendationRank"] = range(1, len(rec_df)+1)
    return rec_df[["FriendName", "Restaurant", "RecommendationRank"]]

# 按好友分组处理，合并结果后重置索引
result = df.groupby("FriendName").apply(get_top_recommendations).reset_index(drop=True)

print(result)

代码说明 #

1. 数据构造/读取：可根据实际场景选择直接构造DataFrame或读取本地CSV文件。
2. 分组逻辑：通过groupby("FriendName")将数据按好友拆分，对每个分组应用自定义筛选函数。
3. 排序与筛选：每个好友的餐厅列表先按InterestRank升序排序，遍历过程中统计每个菜系的入选数量，确保单菜系不超过2家，直到选满4家。
4. 生成推荐排名：为选中的餐厅添加1到4的推荐排名，最后保留需求指定的列输出。

内容的提问来源于stack exchange，提问作者Utsav Maniar