Question

I want to save data in text files and create dictionaries from those files, which I’ll pass to a function later on.

Here’s my code:

def lesson_dictionary(filename):
    print "Reading file ", filename
    with open(filename) as f:
        mylist = f.read().strip().split() 
        dictionary = OrderedDict(zip(mylist[::2], mylist[1::2])) #keep keys/values in same order as declared in mylist
        print dictionary
    return dictionary

With a sample file named sample.txt containing two columns of key/value pairs separated by a space, it works fine. For example,

a b

c d

e f

yields a list like so:

OrderedDict([('a', 'b'), ('c', 'd'), ('e', 'f')])

BUT if I change the code and the content of the .txt file, it breaks. For example, if sample2.txt included:

a:b

c:d

e:f

and my code is

def lesson_dictionary(filename):
    print "Reading file ", filename
    with open(filename) as f:
        mylist = f.read().strip().split(':') #CHANGED: split string at colon!
        dictionary = OrderedDict(zip(mylist[::2], mylist[1::2]))
        print dictionary
    return dictionary

I get the following output:

OrderedDict([('a', 'b \nc'), ('d\ne', 'f')])

What’s happening? Why did strip() work for the first .txt file but not for the second? Thanks in advance for any help.

Answer 1

The original split() split on whitespace, and \n is considered whitespace. By changing to split(':') you’ve removed the split on the end of line, so the end of one line is merged with the start of the next with an extra newline character in the middle. I don’t think there’s an easy way to fix it except to read the file one line at a time.

Edit: Some code to demonstrate.

dictionary = OrderedDict()
with open(filename) as f:
    for line in f:
        key, value = line.split(':')
        dictionary[key.strip()] = value.strip()

Or more in the spirit of your original:

with open(filename) as f:
    mylist = [line.strip().split(':') for line in f]
    dictionary = OrderedDict(mylist)

The second form has the disadvantage of not automatically stripping whitespace from around the words. Based on your example, you might need that.