While working on a program that creates bars out of sets of numbers in a list, I found that adding up items in my list doesn't work. I thought the best way to do this is just to make a for loop.
Here's my list:
phonelist = [[12,14,16,17,18],[16,23,54,64,32]]
And then I try to add this up with a for loop
numphone = 0
for x in len(phonelist[0]):
numphone = numphone + x
Yet I get this error:
TypeError: 'int' object is not iterable
What should I do?
>>> phonelist = [[12,14,16,17,18],[16,23,54,64,32]]
>>> [sum(li) for li in phonelist]
[77, 189]
>>> sum([sum(li) for li in phonelist])
266
or:
>>> sum(sum(li) for li in phonelist) # generator expression...
266
If you are trying to create individual categories, you can use a dict:
data={'Bar {}'.format(i):sum(li) for i, li in enumerate(phonelist, 1)}
data['Total']=sum(data.values())
print data
{'Bar 2': 189, 'Bar 1': 77, 'Total': 266}
Then if you want to produce a simple bar graph:
for bar in sorted(data.keys()):
print '{}: {}'.format(bar, int(round(25.0*data[bar]/data['Total']))*'*')
Prints:
Bar 1: *******
Bar 2: ******************
Total: *************************
len(phonelist[0]) is an int, so you can't loop over it. You can change that to
for x in phonelist[0]:
This way x will take on each value of phonelist[0].
numphone = 0
for x in phonelist[0]:
numphone = numphone + x
This should work. You iterate overt the list, not over the length of it, since the length is an integer and iterating over an integer doesn't make sense
The best solution to this is to use the sum() built-in function. One method, as given in other answers to this question, is to sum each sumlist, then sum those subtotals.
However, a better solution is to flatten the sublists - this is best achieved with itertools.chain.from_iterable().
sum(itertools.chain.from_iterable(phonelist))
This is an optimal solution as it means that you do not have to perform a lot of separate sum operations, or create an intermediate list. It will also work lazily if your data is a lazy source, making your code more memory-efficient and spreading the processing load.
