Dataframe筛选值解包报错：ValueError与UnboundLocalError求助

错误1：ValueError: too many values to unpack (expected 2) #

原因 #

find_district_info函数在无匹配数据时返回字符串"District not found"，但调用时用semt, avg_price = ...尝试将单个字符串拆分为两个变量，类型不匹配导致报错。

错误2：UnboundLocalError: cannot access local variable 'semt' #

原因 #

1. 筛选条件错误：函数最后一个筛选条件错误地用material used and luxuriness列匹配prestige_of_that_district_and_its_vicinity参数，导致筛选逻辑失效，可能出现district_info非空但无法正确提取semt的情况；
2. 变量定义未覆盖所有分支：若len(district_info)不为0，但后续提取semt的代码执行失败（比如列名错误），会导致semt未定义就执行return semt, avg_price。

修复方案 #

1. 统一函数返回值类型 #

避免混合返回字符串和元组，改为返回(None, None)表示无匹配，确保返回类型一致：

def find_district_info(district, BuildingAge,SquareMeter,Floor,Number_of_Floors,Elevator,number_of_bathrooms,Otopark,steeped_alley,material_used_and_luxuriness,prestige_of_that_district_and_its_vicinity):
    # 修复筛选条件：最后一行替换为prestige对应的正确列名（需与transactions_master_df实际列名一致）
    district_info = transactions_master_df[(transactions_master_df['District'] == district) & 
                                           (transactions_master_df['Building Age'] == BuildingAge)&
                                           (transactions_master_df['SquareMeter'] == SquareMeter)&
                                           (transactions_master_df['Floor'] == Floor)&
                                           (transactions_master_df['Number of Floors'] == Number_of_Floors)&
                                           (transactions_master_df['Elevator'] == Elevator)&
                                           (transactions_master_df['number of bathrooms'] == number_of_bathrooms)&
                                           (transactions_master_df['Otopark'] == Otopark)&
                                           (transactions_master_df['steeped alley'] == steeped_alley)&
                                           (transactions_master_df['material used and luxuriness'] == material_used_and_luxuriness)&
                                           # 替换为prestige字段的正确列名
                                           (transactions_master_df['prestige of that district and its vicinity'] == prestige_of_that_district_and_its_vicinity)
                                                                                                    ]
    
    if len(district_info) == 0:
        return None, None  # 统一返回元组类型
    
    semt = district_info.iloc[0]['Semt']
    avg_price = district_info['Price'].mean()
    
    return semt, avg_price

2. 调整调用逻辑 #

根据返回值的统一类型做判断，避免解包错误：

semt, avg_price = find_district_info(district_input, BuildingAge, SquareMeter, Floor, Number_of_Floors, Elevator, number_of_bathrooms, Otopark, steeped_alley, material_used_and_luxuriness, prestige_of_that_district_and_its_vicinity)
if semt is not None and avg_price is not None:
    print(f"For the district {district_input}:")
    print(f"Semt: {semt}")
    print(f"Average apartment price: {avg_price}")
else:
    print("No matching data found for the given parameters")

3. 额外优化建议 #

• 核对transactions_master_df的列名，确保与筛选条件中的列名完全一致（注意大小写、空格）；
• 避免用==强匹配所有字段，可考虑放宽筛选条件（比如面积范围、楼龄范围），减少无匹配的情况；
• 在函数开头初始化semt和avg_price为默认值，避免极端情况出现未定义错误：

def find_district_info(...):
    semt = None
    avg_price = None
    # 后续筛选逻辑...

内容的提问来源于stack exchange，提问作者Nima_Ebr