If I want the maximum value in a list, I can just write `max(List)`, but what if I also need the index of the maximum value?

I can write something like this:

``````maximum=0
for i,value in enumerate(List):
if value>maximum:
maximum=value
index=i
``````

But it looks tedious to me.

And if I write:

``````List.index(max(List))
``````

Then it will iterate the list twice.

Is there a better way?

I think the accepted answer is great, but why don’t you do it explicitly? I feel more people would understand your code, and that is in agreement with PEP 8:

``````max_value = max(my_list)
max_index = my_list.index(max_value)
``````

This method is also about three times faster than the accepted answer:

``````import random
from datetime import datetime
import operator

def explicit(l):
max_val = max(l)
max_idx = l.index(max_val)
return max_idx, max_val

def implicit(l):
max_idx, max_val = max(enumerate(l), key=operator.itemgetter(1))
return max_idx, max_val

if __name__ == "__main__":
from timeit import Timer
t = Timer("explicit(l)", "from __main__ import explicit, implicit; "
"import random; import operator;"
"l = [random.random() for _ in xrange(100)]")
print "Explicit: %.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)

t = Timer("implicit(l)", "from __main__ import explicit, implicit; "
"import random; import operator;"
"l = [random.random() for _ in xrange(100)]")
print "Implicit: %.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
``````

Results as they run in my computer:

``````Explicit: 8.07 usec/pass
Implicit: 22.86 usec/pass
``````

Other set:

``````Explicit: 6.80 usec/pass
Implicit: 19.01 usec/pass
``````

There are many options, for example:

``````import operator
index, value = max(enumerate(my_list), key=operator.itemgetter(1))
``````

This answer is 33 times faster than @Escualo assuming that the list is very large, and assuming that it’s already an np.array(). I had to turn down the number of test runs because the test is looking at 10000000 elements not just 100.

``````import random
from datetime import datetime
import operator
import numpy as np

def explicit(l):
max_val = max(l)
max_idx = l.index(max_val)
return max_idx, max_val

def implicit(l):
max_idx, max_val = max(enumerate(l), key=operator.itemgetter(1))
return max_idx, max_val

def npmax(l):
max_idx = np.argmax(l)
max_val = l[max_idx]
return (max_idx, max_val)

if __name__ == "__main__":
from timeit import Timer

t = Timer("npmax(l)", "from __main__ import explicit, implicit, npmax; "
"import random; import operator; import numpy as np;"
"l = np.array([random.random() for _ in xrange(10000000)])")
print "Npmax: %.2f msec/pass" % (1000  * t.timeit(number=10)/10 )

t = Timer("explicit(l)", "from __main__ import explicit, implicit; "
"import random; import operator;"
"l = [random.random() for _ in xrange(10000000)]")
print "Explicit: %.2f msec/pass" % (1000  * t.timeit(number=10)/10 )

t = Timer("implicit(l)", "from __main__ import explicit, implicit; "
"import random; import operator;"
"l = [random.random() for _ in xrange(10000000)]")
print "Implicit: %.2f msec/pass" % (1000  * t.timeit(number=10)/10 )
``````

Results on my computer:

``````Npmax: 8.78 msec/pass
Explicit: 290.01 msec/pass
Implicit: 790.27 msec/pass
``````

With Python’s built-in library, it’s pretty easy:

``````a = [2, 9, -10, 5, 18, 9]
max(xrange(len(a)), key = lambda x: a[x])
``````

This tells `max` to find the largest number in the list `[0, 1, 2, ..., len(a)]`, using the custom function `lambda x: a[x]`, which says that `0` is actually `2`, `1` is actually `9`, etc.

I would suggest a very simple way:

``````import numpy as np
l = [10, 22, 8, 8, 11]
print(np.argmax(l))
print(np.argmin(l))
``````

Hope it helps.

``````max([(v,i) for i,v in enumerate(my_list)])
``````

``````max([(value,index) for index,value in enumerate(your_list)]) #if maximum value is present more than once in your list then this will return index of the last occurrence
``````

If maximum value in present more than once and you want to get all indices,

``````max_value = max(your_list)
maxIndexList = [index for index,value in enumerate(your_list) if value==max(your_list)]
``````

Maybe you need a sorted list anyway?

Try this:

``````your_list = [13, 352, 2553, 0.5, 89, 0.4]
sorted_list = sorted(your_list)
index_of_higher_value = your_list.index(sorted_list[-1])
``````

I made some big lists. One is a list and one is a numpy array.

``````import numpy as np
import random
arrayv=np.random.randint(0,10,(100000000,1))
listv=[]
for i in range(0,100000000):
listv.append(random.randint(0,9))
``````

Using jupyter notebook’s %%time function I can compare the speed of various things.

2 seconds:

``````%%time
listv.index(max(listv))
``````

54.6 seconds:

``````%%time
listv.index(max(arrayv))
``````

6.71 seconds:

``````%%time
np.argmax(listv)
``````

103 ms:

``````%%time
np.argmax(arrayv)
``````

numpy’s arrays are crazy fast.

List comprehension method:

Let’s say you have some list `List = [5,2,3,8]`

Then `[i for i in range(len(List)) if List[i] == max(List)]` would be a pythonic list comprehension method to find the values “i” where `List[i] == max(List)`.

It is easily scalable for arrays that are lists of lists, simply by doing a for loop.

For instance, with an arbitrary list of lists “array” and initalizing “index” as an empty list.

``````array = [[5, 0, 1, 1],
[1, 0, 1, 5],
[0, 1, 6, 0],
[0, 4, 3, 0],
[5, 2, 0, 0],
[5, 0, 1, 1],
[0, 6, 0, 1],
[0, 1, 0, 6]]
index = []

for List in array:
index.append([i for i in range(len(List)) if List[i] == max(List)])
index
``````

Output: `[, , , , , , , ] `

Here is a complete solution to your question using Python’s built-in functions:

``````# Create the List
numbers = input("Enter the elements of the list. Separate each value with a comma. Do not put a comma at the end.\n").split(",")

# Convert the elements in the list (treated as strings) to integers
numberL = [int(element) for element in numbers]

# Loop through the list with a for-loop

for elements in numberL:
maxEle = max(numberL)
indexMax = numberL.index(maxEle)

print(maxEle)
print(indexMax)
``````