I have two lists as follows.
mylist1 = [["lemon", 0.1], ["egg", 0.1], ["muffin", 0.3], ["chocolate", 0.5]]
mylist2 = [["chocolate", 0.5], ["milk", 0.2], ["carrot", 0.8], ["egg", 0.8]]
I want to get the mean of the common elements in the two lists as follows.
myoutput = [["chocolate", 0.5], ["egg", 0.45]]
My current code is as follows
for item1 in mylist1:
for item2 in mylist2:
if item1[0] == item2[0]:
print(np.mean([item1[1], item2[1]]))
However, since there are two for loops (O(n^2) complexity) this is very inefficient for very long lists. I am wondering if there is more standard/efficient way of doing this in Python.
You can do it in O(n) (single pass over each list) by converting 1 to a dict, then per item in the 2nd list access that dict (in O(1)), like this:
mylist1 = [["lemon", 0.1], ["egg", 0.1], ["muffin", 0.3], ["chocolate", 0.5]]
mylist2 = [["chocolate", 0.5], ["milk", 0.2], ["carrot", 0.8], ["egg", 0.8]]
l1_as_dict = dict(mylist1)
myoutput = []
for item,price2 in mylist2:
if item in l1_as_dict:
price1 = l1_as_dict[item]
myoutput.append([item, (price1+price2)/2])
print(myoutput)
Output:
[['chocolate', 0.5], ['egg', 0.45]]
Collected from the Internet
Please contact [email protected] to delete if infringement.
Comments