Python 从单独的CSV文件中匹配特定值并连接行

我正在尝试创建一个python v3.x程序，它可以查看2个CSV文件。
最初，我需要将汽车CSV列表分为两个列表，昂贵的汽车和便宜的汽车，并为每个列表分配相关的行

然后我计划从指示器CSV中获取一个指示器，查看它是否是一个特定于昂贵汽车、廉价汽车或两者的指示器，然后将该指示器CSV行中的值与汽车CSV中的匹配行连接起来

CAR CSV-列出多辆车的详细信息

“车辆代码”、“价格”、“别名”、“组”
“Car1”、“100”、“蓝色汽车”、“廉价汽车”
“Car2”、“900”、“黄金车”、“昂贵车”
“Car3”、“150”、“红色汽车”、“廉价汽车”
“Car4”、“999”、“白金车”、“名贵车”
“Car5”、“122”、“棕色车”、“廉价车”
“Car6”、“500”、“粉红车”、“廉价车”、“昂贵车”

指示器CSV-列出车辆可能出现的故障/错误

“指标字段”、“指标值”、“描述”、“组”
“故障”、“生锈”、“车身生锈”、“便宜车；昂贵车”
“错误”、“车窗升降器停止”、“手动车窗升降器损坏”、“廉价车”
“故障”、“V12问题”、“V12发动机问题”、“昂贵的汽车”

串联示例

{“代码”：“Car1”，“错误”：“生锈”}
{“代码”：“Car2”，“故障”：“V12问题”}
{“代码”：“Car5”，“错误”：“车窗升降器停止”}
{“代码”：“Car1”，“错误”：“窗口卷绕器已停止”}

到目前为止，我所能做的就是从每个CSV中选择一条随机线，并将每个CSV中的特定值连接起来。问题是，现在所有的指示灯和车辆组都匹配起来了

from sys import argv
from time import sleep
import random
import csv
import heapq

script, indicator_file, car_file = argv
ind = ''
car = ''

def indicatorDefinition(i):
    with open(i) as file:
        reader = csv.DictReader(file)
        random_line, = heapq.nlargest(1, reader, key=lambda L: random.random())
        global ind
        ind = random_line['Indicator_Field']+'":"'+random_line['Indicator_Value']+'"'

def carDefinition(n):
    with open(n) as file:
        reader = csv.DictReader(file)
        random_line, = heapq.nlargest(1, reader, key=lambda L: random.random())
        global net
        net = '"code":"'+random_line['Car code']+'","'




def counter():
    count = 0
    while count < 6:
        carDefinition(car_file)
        indicatorDefinition(indicator_file)
        print("{"+car+ind+"}")
        sleep(random.randint(1,10))
        count += 1


counter()

从系统导入argv
从时间上导入睡眠
随机输入
导入csv
进口heapq
脚本，指示器文件，汽车文件=argv
ind=“”
汽车=“”
def指示灯定义（i）：
打开（i）作为文件：
reader=csv.DictReader（文件）
random_line，=heapq.nlargest（1，读卡器，key=lambda L:random.random（））
全球工业
ind=随机线['指标\字段']+'：“'+随机线['指标\值']+'”
def卡定义（n）：
打开（n）作为文件：
reader=csv.DictReader（文件）
random_line，=heapq.nlargest（1，读卡器，key=lambda L:random.random（））
全球网络
net=““代码”：“+”随机_行['Car code']+'”，“'
def计数器（）：
计数=0
当计数小于6时：
卡片定义（car\U文件）
指示符定义（指示符文件）
打印（“{”+car+ind+“}”）
睡眠（random.randint（1,10））
计数+=1
计数器（）

可能是这样的吧？我假设Groups字段应该被拆分，而不是按原样使用。根据评论更新以阅读所有组

import csv
import io
import random
import collections
import itertools

# Data included in source for clarity

car_csv = """
"Car code","Price","Alias","Groups"
"Car1","100","Blue Car","Cheap Cars"
"Car2","900","Gold Car","Expensive Cars"
"Car3","150","Red Car","Cheap Cars"
"Car4","999","Platinum Car","Expensive Cars"
"Car5","122","Brown Car","Cheap Cars"
"Car6","500","Pink Car","Cheap Cars","Expensive Cars"
"TestCarWithThreeGroups","500","Pink Car","Cheap Cars","Expensive Cars","Broken Cars"
""".lstrip()

indicator_csv = """
"Indicator_Field","Indicator_Value","Desc","Groups"
"Fault","Rusting","Bodywork is rusting","Cheap Cars; Expensive Cars"
"Error","Window Winder Stopped","Manual window winder broken","Cheap Cars"
"Fault","V12 Issues","V12 Engine Problems","Expensive Cars"
""".lstrip()

# Read data (replace io.StringIO(...) with a file object to read from a file)
indicator_data = list(csv.DictReader(io.StringIO(indicator_csv)))

# Note `restkey` to capture all of the additional columns into `OtherGroups`.
car_data = list(csv.DictReader(io.StringIO(car_csv), restkey='OtherGroups'))

# Pre-massage the car data so `Groups` is always a set of groups, and `OtherGroups` is no longer there:
for car in car_data:
    car['Groups'] = {car['Groups']} | set(car.pop('OtherGroups', ()))


# Create a mapping of groups <-> indicators
indicators_by_group = collections.defaultdict(list)
for indicator in indicator_data:
    for group in indicator['Groups'].split('; '):
        indicators_by_group[group].append(indicator)


def generate_car():
    car = random.choice(car_data)
    # Concatenate all indicators based on the groups of the car
    available_indicators = list(itertools.chain(
        *(indicators_by_group.get(group, []) for group in car['Groups'])
    ))
    # Choose a random indicator -- this will crash if there are no available indicators
    # for the car.
    indicator = random.choice(available_indicators)
    # Generate output datum
    return {
        'code': car['Car code'],
        indicator['Indicator_Field']: indicator['Indicator_Value'],
    }


for x in range(10):
    print(generate_car())

---

你愿意使用熊猫吗？@AntonvBR是的，如果这会有帮助的话那是完美的，但是在进一步调查Cars.csv之后，我给出的例子是不正确的：（似乎不是像在指示器中那样用a分隔组；Cars csv将其放入一个新值“group2”如果你在这里用实际的数据格式更新你的原始帖子，我可以对代码进行相关的更改。更改已经完成，Car6的汽车CSV注意到两个汽车组是如何分开的。谢谢你的帮助。顺便说一句。非常感谢你！！这工作非常好，我想我的主要问题是我对这方面太陌生了，我不知道所有不同的方式哟你还可以接近事物。

{'code': 'Car1', 'Error': 'Window Winder Stopped'}
{'code': 'Car6', 'Error': 'Window Winder Stopped'}
{'code': 'Car2', 'Fault': 'Rusting'}
{'code': 'Car6', 'Error': 'Window Winder Stopped'}
{'code': 'TestCarWithThreeGroups', 'Error': 'Window Winder Stopped'}
{'code': 'Car5', 'Fault': 'Rusting'}
{'code': 'Car2', 'Fault': 'Rusting'}
{'code': 'TestCarWithThreeGroups', 'Fault': 'V12 Issues'}
{'code': 'Car6', 'Fault': 'Rusting'}
{'code': 'TestCarWithThreeGroups', 'Error': 'Window Winder Stopped'}