有几种方法可以实现这一点，但我也会使用某种Map。
如果您读取一个MapCSV文件，其中包含需要路由的列和目标文件夹，就像您的示例中那样，我将使用dict进行键/值查找（dict非常快）。
逻辑是将需要路由的列转换为键，并使用目标文件夹作为值。
然后迭代实际的CSV，将相关列转换为键，并使用它查找目标文件夹。
我使用了您提供的示例作为下面的输入文件

import csv

def csv_dict_to_key(row: dict) -> str:
    # convert the row values into a key
    return f"{row['dwelling']}_{row['wall']}_{row['weather']}_{row['occ']}"

def create_mapping(filename: str) -> dict:
    # go over the mapping file and return a dict
    # which has the mapping
    result = {}
    with open(filename) as infile:
        reader = csv.DictReader(infile)
        for row in reader:
            key = csv_dict_to_key(row)
            if key in result:
                print(f"FOUND DUPLICATE {key}")
            result[key] = row["folder"]
    return result

mapping = create_mapping("mapping.csv")

with open("data.csv") as infile:
    reader = csv.DictReader(infile)
    for row in reader:
        key = csv_dict_to_key(row)
        if key in mapping:
            # do whatever you need here, I'm just using pring as example
            print(f"----\nROW:    {row}\nKEY:    {key}\nTARGET: {mapping[key]}")
        else:
            print(f"----\nKEY {key} not found in mapping")

输出

----
ROW:    {'dwelling': '5', 'wall': '2', 'weather': 'Ldn', 'occ': 'Pen', 'height': '154.7', 'temp': '23.4'}
KEY:    5_2_Ldn_Pen
TARGET: Semi_detached_solid
----
ROW:    {'dwelling': '5', 'wall': '4', 'weather': 'Ldn', 'occ': 'Pen', 'height': '172.4', 'temp': '28.7'}
KEY:    5_4_Ldn_Pen
TARGET: Semi_detached_cavity
----
ROW:    {'dwelling': '3', 'wall': '4', 'weather': 'Ldn', 'occ': 'Pen', 'height': '183.5', 'temp': '21', None: ['2']}
KEY:    3_4_Ldn_Pen
TARGET: Detached_cavity