使用python将csv文件转换为元组列表

Question

I am to take a csv with 4 columns: brand, price, weight, and type.

The types are orange, apple, pear, plum.

Parameters: I need to select the most possible weight, but by selecting 1 orange, 2 pears, 3 apples, and 1 plum by not exceeding as $20 budget. I cannot repeat brands of the same fruit (like selecting the same brand of apple 3 times, etc).

I can open and read the csv file through Python, but I'm not sure how to create a dictionary or list of tuples from the csv file?

For more clarity, here's an idea of the data.

Brand, Price, Weight, Type
brand1, 6.05, 3.2, orange
brand2, 8.05, 5.2, orange
brand3, 6.54, 4.2, orange
brand1, 6.05, 3.2, pear
brand2, 7.05, 3.6, pear
brand3, 7.45, 3.9, pear
brand1, 5.45, 2.7, apple
brand2, 6.05, 3.2, apple
brand3, 6.43, 3.5, apple
brand4, 7.05, 3.9, apple
brand1, 8.05, 4.2, plum
brand2, 3.05, 2.2, plum

Here's all I have right now:

import csv
test_file = 'testallpos.csv'
csv_file = csv.DictReader(open(test_file, 'rb'), ["brand"], ["price"], ["weight"], ["type"])

Answer 1

You can ponder this:

import csv

def fitem(item):
    item=item.strip()
    try:
        item=float(item)
    except ValueError:
        pass
    return item        

with open('/tmp/test.csv', 'r') as csvin:
    reader=csv.DictReader(csvin)
    data={k.strip():[fitem(v)] for k,v in reader.next().items()}
    for line in reader:
        for k,v in line.items():
            k=k.strip()
            data[k].append(fitem(v))

print data 

Prints:

{'Price': [6.05, 8.05, 6.54, 6.05, 7.05, 7.45, 5.45, 6.05, 6.43, 7.05, 8.05, 3.05],
 'Type': ['orange', 'orange', 'orange', 'pear', 'pear', 'pear', 'apple', 'apple', 'apple', 'apple', 'plum', 'plum'], 
 'Brand': ['brand1', 'brand2', 'brand3', 'brand1', 'brand2', 'brand3', 'brand1', 'brand2', 'brand3', 'brand4', 'brand1', 'brand2'], 
 'Weight': [3.2, 5.2, 4.2, 3.2, 3.6, 3.9, 2.7, 3.2, 3.5, 3.9, 4.2, 2.2]}

If you want the csv file literally as tuples by rows:

import csv
with open('/tmp/test.csv') as f:
    data=[tuple(line) for line in csv.reader(f)]

print data
# [('Brand', ' Price', ' Weight', ' Type'), ('brand1', ' 6.05', ' 3.2', ' orange'), ('brand2', ' 8.05', ' 5.2', ' orange'), ('brand3', ' 6.54', ' 4.2', ' orange'), ('brand1', ' 6.05', ' 3.2', ' pear'), ('brand2', ' 7.05', ' 3.6', ' pear'), ('brand3', ' 7.45', ' 3.9', ' pear'), ('brand1', ' 5.45', ' 2.7', ' apple'), ('brand2', ' 6.05', ' 3.2', ' apple'), ('brand3', ' 6.43', ' 3.5', ' apple'), ('brand4', ' 7.05', ' 3.9', ' apple'), ('brand1', ' 8.05', ' 4.2', ' plum'), ('brand2', ' 3.05', ' 2.2', ' plum')]

Answer 2

import csv
with open("some.csv") as f:
       r = csv.reader(f)
       print filter(None,r)

or with list comprehension

import csv
with open("some.csv") as f:
       r = csv.reader(f)
       print [row for row in r if row]

for comparison

In [3]: N = 100000

In [4]: the_list = [randint(0,3) for _ in range(N)]

In [5]: %timeit filter(None,the_list)
1000 loops, best of 3: 1.91 ms per loop

In [6]: %timeit [i for i in the_list if i]
100 loops, best of 3: 4.01 ms per loop

[edit] since your actual output does not have blanks you donot need the list comprehension or the filter you can just say list(r)

Final answer without blank lines

import csv
with open("some.csv") as f:
       print list(csv.reader(f))

if you want dicts you can do

import csv
with open("some.csv") as f:
       reader = list(csv.reader(f))
       print [dict(zip(reader[0],x)) for x in reader]
       #or
       print map(lambda x:dict(zip(reader[0],x)), reader)