仅使用标准库,按另一列中分组值的累积总和来对文本文件进行排序?

时间:2019-02-06 06:47:47

标签: python sorting sum cumulative-sum accumulate

我有一个带有这样的行的文件

id, car_type, cost
1, benz, 60000
2, benz, 55000
3, bmw, 30000
4, benz, 25000
5, bmw, 26000
6, ford, 5000

我想按每个car_type的总费用对该文件进行排序。例如,“ benz”的总费用为60000 + 55000 + 25000 = 14000

所以最终输出应该是

benz, 140000
bmw, 56000
ford, 5000

到目前为止,这就是我所拥有的:

file = "small_sample.txt"


f=open(file,"r")
lines=f.readlines()[1:]
car_and_cost ={}
for x in lines:
    cost = x.split(',')[4].rstrip('\n')
    car_and_cost.update({x.split(',')[3]:float(cost)})
f.close()
print(car_and_cost)

new_dic = {}
for key,lis in car_and_cost.items():
    new_dic[key] = sum(lis)
print(new_dic)

我几乎陷入困境。首先,我从中生成的字典的总和不正确,而且我根本不知道如何按值对字典进行排序

2 个答案:

答案 0 :(得分:3)

这是使用csvcollections模块的一种方法

例如:

import csv
from collections import defaultdict, OrderedDict

result = defaultdict(int)

with open(filename) as infile:
    reader = csv.DictReader(infile)
    for row in reader:                    #Iterate Each row
        result[row[" car_type"]] += int(row[" cost"])   #Add costs

print(OrderedDict(sorted(result.items(), key=lambda x: x[1], reverse=True)))

输出:

OrderedDict([(' benz', 140000), (' bmw', 56000), (' ford', 5000)])

答案 1 :(得分:1)

使用熊猫:

import pandas as pd
df = pd.read_csv(logFile)

result = df.groupby(' car_type').sum()
print(result)

输出:

           id    cost
 car_type            
 benz       7  140000
 bmw        8   56000
 ford       6    5000

编辑:

logFile = "tem.csv"
array = []
import csv

with open("tem.csv", "r+") as fin:
    for row in csv.reader(fin):
        array.append(row[1:])

dd = {k: 0 for k in dict(array).keys()}
for x in array: dd[x[0]] += int(x[1])
print(dd)

输出:

{' benz': 140000, ' bmw': 56000, ' ford': 5000}

或者如果您希望它们在列表中:

print([[k,v] for k,v in  dd.items()])

输出:

[[' benz', 140000], [' bmw', 56000], [' ford', 5000]]