使用分隔符分隔行和列并将字符串转换为列表并计算文本文件中最后一列的总和，而不使用 pandas

Question

2019-01-01,Cake Fudge,150,1,150
2019-01-01,Cake Fudge,150,3,450
2019-01-01,Death by Chocolate,180,1,180
2019-01-01,Vanilla Double Scoop,80,3,240
2019-01-01,Butterscotch Single Scoop,60,5,300
2019-01-01,Vanilla Single Scoop,50,5,250
2019-01-01,Cake Fudge,150,5,750
2019-01-01,Hot Chocolate Fudge,120,3,360
2019-01-01,Butterscotch Single Scoop,60,5,300
2019-01-01,Chocolate Europa Double Scoop,100,1,100
2019-01-01,Hot Chocolate Fudge,120,2,240
2019-01-01,Caramel Crunch Single Scoop,70,4,280
2019-01-01,Hot Chocolate Fudge,120,2,240
2019-01-01,Hot Chocolate Fudge,120,4,480
2019-01-01,Hot Chocolate Fudge,120,2,240
2019-01-01,Cafe Caramel,160,5,800
2019-01-01,Vanilla Double Scoop,80,2,510

This is the data, I want to find the sum of the last column from text file

Answer 1

Try this:

with open('test.txt') as fp:
    data = [[int(col.strip()) if col.strip().isnumeric() else col.strip() for col in line.split(',')] for line in fp if line.strip()]
res = sum([row[-1] for row in data])

Month wise calculation of avg, min, max and sum for last column.

from collections import defaultdict
from datetime import datetime

with open('test.txt') as fp:
    data = [[int(col.strip()) if col.strip().isnumeric() else col.strip() for col in line.split(',')] for line in fp if line.strip()]

d, res = defaultdict(list), {}
for row in data:
    month = datetime.strptime(row[0], '%Y-%m-%d').strftime('%B')
    d[month].append(row[1:])
for key in d:
    values = [x[-1] for x in d[key]]
    res[key] = {'min': min(values), 'max': max(values), 'sum': sum(values), 'avg': sum(values) / len(values)}
print(res)

#{'February': {'avg': 350.0, 'max': 750, 'min': 100, 'sum': 1750}, 'January': {'avg': 261.6666666666667, 'max': 450, 'min': 150, 'sum': 1570},'March': {'avg': 425.0, 'max': 800, 'min': 240, 'sum': 2550}}