Normalizing a list of numbers in Python

Use :

norm = [float(i)/sum(raw) for i in raw]

to normalize against the sum to ensure that the sum is always 1.0 (or as close to as possible).

use

norm = [float(i)/max(raw) for i in raw]

to normalize against the maximum

if your list has negative numbers, this is how you would normalize it

a = range(-30,31,5)
norm = [(float(i)-min(a))/(max(a)-min(a)) for i in a]

try:

normed = [i/sum(raw) for i in raw]

normed
[0.25, 0.5, 0.25]

How long is the list you're going to normalize?

def psum(it):
    "This function makes explicit how many calls to sum() are done."
    print "Another call!"
    return sum(it)

raw = [0.07,0.14,0.07]
print "How many calls to sum()?"
print [ r/psum(raw) for r in raw]

print "\nAnd now?"
s = psum(raw)
print [ r/s for r in raw]

# if one doesn't want auxiliary variables, it can be done inside
# a list comprehension, but in my opinion it's quite Baroque    
print "\nAnd now?"
print [ r/s  for s in [psum(raw)] for r in raw]

Output

# How many calls to sum()?
# Another call!
# Another call!
# Another call!
# [0.25, 0.5, 0.25]
# 
# And now?
# Another call!
# [0.25, 0.5, 0.25]
# 
# And now?
# Another call!
# [0.25, 0.5, 0.25]

For ones who wanna use scikit-learn, you can use

from sklearn.preprocessing import normalize

x = [1,2,3,4]
normalize([x]) # array([[0.18257419, 0.36514837, 0.54772256, 0.73029674]])
normalize([x], norm="l1") # array([[0.1, 0.2, 0.3, 0.4]])
normalize([x], norm="max") # array([[0.25, 0.5 , 0.75, 1.]])