如何在python中以数字方式对csv文件中的数据进行排序

Question

I am writing a program that takes students scores from a csv file and needs to sort then highest to lowest score. the csv file looks like this:

josh 12
john 6
fred 8
harry 7

i have tried to put the items in a list like this:

 Mylist=[]
csvfile = open (classname,'r')
reader = csv.reader(csvfile)
for row in reader:
    Mylist.append(row)

then reverse the list to put the numerical value first:

Mynewlist = []
    for each in Mylist:
        value2 = ''.join(each[0])
        value1 = ''.join(each[1])
        mynewlist.append(value1,value2)

with no luck i get this error:

    Traceback (most recent call last):
  File "<pyshell#0>", line 1, in <module>
    main()
  File "\\SRV-FILE3\ca231$\task 3\3.py", line 143, in main
    value1 = ''.join(each[1])
IndexError: list index out of range

i use ''.join(each[1]) to convert to a string and then append them in the opposite order then i planned to use .sort() to sort them numerically but I cant get them to append to a list.

does anyone know how to sort the contents of a csv file by its numerical value?

Answer 1

I think you're overcomplicating things. Assuming you have the data as a list of lists:

data = [("josh", "12"), ("john", "6"), ("fred", "8"), ("harry", "7")]

This could come from CSV of course, it doesn't matter to the sorting. You can sort just by calling sorted() :

sorted(data, key = lambda x: int(x[1]))

The lambda is a function that picks the second element of each sub-list as the key, ie the score, and converts it to a number for sorting. This prints:

[('john', '6'), ('harry', '7'), ('fred', '8'), ('josh', '12')]

Answer 2

You could do something like this: ( Create a dictionary out of your values )

for row in reader:
    my_dict = {row[0]:row[1]}

Then you can do a representation of a sorted dictionary (dictionaries are inherently orderless so this will be a list):

import operator
sorted_dict = sorted(my_dict.items(), key=operator.itemgetter(1))

It's worth noting that there are better / simpler ways to do this ( Panda for instance ) but atleast you learn a different approach :)

Answer 3

If all your CSV contains is a name and a number and your names are unique, then

1. store CSV contents as {name:score} as a dict

2. Use the code below to sort based on values(scores in your case)

    import operator x = {"josh": 12, "john": 6, "fred": 8, "harry": 7,} sorted_x = sorted(x.iteritems(), key=operator.itemgetter(1)) 

Answer 4

from operator import itemgetter
result = []
with open("data", 'r') as f:
        r = csv.reader(f, delimiter=' ')
        # next(r, None)  # skip the headers
        for row in r:
            result.append(row[:-1])
# sort by numeric part, which is a key value for sorted.
# itemgetter(1) gets the number in each sublist
print(sorted(result,key=itemgetter(1)))

[['josh', '12'], ['john', '6'], ['harry', '7'], ['fred', '8']]

Answer 5

You can utilize pandas for this.

import pandas as pd

df = pd.read_csv('students.csv', header=None)
df.columns = ['Name', 'Score']
df.sort('Score', ascending=False, inplace=True)

At the end of this, you will have a data frame that looks like this:

    Name  Score
0   josh     12
2   fred      8
3  harry      7
1   john      6

---

The code is reading your CSV file, and explicitly stating there isn't a header. By default pandas assumes that the first row contains column headers. Since there isn't any headers, we then add those: Name and Score . Finally, we sort, inplace, based on the Score column. You could leave the original dataframe unchanged by removing the inplace= parameter and doing this:

sorted_df = df.sort('Score', ascending=False)

After this line, you'd have your original file in df and the sorted file in sorted_df

Answer 6

If your data in the csv file looks like this:

josh 12
john 6
fred 8
harry 7

Then you can create a dictionary and use key=d.__getitem__ :

import csv
with open('yourfile.csv', 'rb') as f:
    reader = csv.reader(f)
    d = {}
    for row in f:
        row = row.split(",")
        d[row[0]]=int(row[1]) 
k = sorted(d, key=d.__getitem__, reverse=True)
v = sorted(d.values(), reverse=True)
sorted_d = zip(k,v)
print (sorted_d)

Output:

[('josh', 12), ('fred', 8), ('harry', 7), ('john', 6)]