Finding median of list in Python

Python 3.4 has statistics.median:

Return the median (middle value) of numeric data.

When the number of data points is odd, return the middle data point. When the number of data points is even, the median is interpolated by taking the average of the two middle values:

>>> median([1, 3, 5])
3
>>> median([1, 3, 5, 7])
4.0

Usage:

import statistics

items = [6, 1, 8, 2, 3]

statistics.median(items)
#>>> 3

It's pretty careful with types, too:

statistics.median(map(float, items))
#>>> 3.0

from decimal import Decimal
statistics.median(map(Decimal, items))
#>>> Decimal('3')

(Works with python-2.x):

def median(lst):
    n = len(lst)
    s = sorted(lst)
    return (s[n//2-1]/2.0+s[n//2]/2.0, s[n//2])[n % 2] if n else None

>>> median([-5, -5, -3, -4, 0, -1])
-3.5

numpy.median():

>>> from numpy import median
>>> median([1, -4, -1, -1, 1, -3])
-1.0

For python-3.x, use statistics.median:

>>> from statistics import median
>>> median([5, 2, 3, 8, 9, -2])
4.0

The sorted() function is very helpful for this. Use the sorted function to order the list, then simply return the middle value (or average the two middle values if the list contains an even amount of elements).

def median(lst):
    sortedLst = sorted(lst)
    lstLen = len(lst)
    index = (lstLen - 1) // 2
   
    if (lstLen % 2):
        return sortedLst[index]
    else:
        return (sortedLst[index] + sortedLst[index + 1])/2.0

Of course in Python3 you can use built in functions, but if you are using Python2 or just would like to create your own you can do something like this. The trick here is to use ~ operator that flip positive number to negative. For instance ~2 -> -3 and using negative in for list in Python will count items from the end. So if you have mid == 2 then it will take third element from beginning and third item from the end.

def median(data):
    data.sort()
    mid = len(data) // 2
    return (data[mid] + data[~mid]) / 2.0

Here's a cleaner solution:

def median(lst):
    quotient, remainder = divmod(len(lst), 2)
    if remainder:
        return sorted(lst)[quotient]
    return sum(sorted(lst)[quotient - 1:quotient + 1]) / 2.

Note: Answer changed to incorporate suggestion in comments.

You can use the list.sort to avoid creating new lists with sorted and sort the lists in place.

Also you should not use list as a variable name as it shadows python's own list.

def median(l):
    half = len(l) // 2
    l.sort()
    if not len(l) % 2:
        return (l[half - 1] + l[half]) / 2.0
    return l[half]

You can try the quickselect algorithm if faster average-case running times are needed. Quickselect has average (and best) case performance O(n), although it can end up O(n²) on a bad day.

Here's an implementation with a randomly chosen pivot:

import random

def select_nth(n, items):
    pivot = random.choice(items)

    lesser = [item for item in items if item < pivot]
    if len(lesser) > n:
        return select_nth(n, lesser)
    n -= len(lesser)

    numequal = items.count(pivot)
    if numequal > n:
        return pivot
    n -= numequal

    greater = [item for item in items if item > pivot]
    return select_nth(n, greater)

You can trivially turn this into a method to find medians:

def median(items):
    if len(items) % 2:
        return select_nth(len(items)//2, items)

    else:
        left  = select_nth((len(items)-1) // 2, items)
        right = select_nth((len(items)+1) // 2, items)

        return (left + right) / 2

This is very unoptimised, but it's not likely that even an optimised version will outperform Tim Sort (CPython's built-in sort) because that's really fast. I've tried before and I lost.

def median(x):
    x = sorted(x)
    listlength = len(x) 
    num = listlength//2
    if listlength%2==0:
        middlenum = (x[num]+x[num-1])/2
    else:
        middlenum = x[num]
    return middlenum

def median(array):
    """Calculate median of the given list.
    """
    # TODO: use statistics.median in Python 3
    array = sorted(array)
    half, odd = divmod(len(array), 2)
    if odd:
        return array[half]
    return (array[half - 1] + array[half]) / 2.0

A simple function to return the median of the given list:

def median(lst):
    lst = sorted(lst)  # Sort the list first
    if len(lst) % 2 == 0:  # Checking if the length is even
        # Applying formula which is sum of middle two divided by 2
        return (lst[len(lst) // 2] + lst[(len(lst) - 1) // 2]) / 2
    else:
        # If length is odd then get middle value
        return lst[len(lst) // 2]

Some examples with the median function:

>>> median([9, 12, 20, 21, 34, 80])  # Even
20.5
>>> median([9, 12, 80, 21, 34])  # Odd
21

If you want to use library you can just simply do:

>>> import statistics
>>> statistics.median([9, 12, 20, 21, 34, 80])  # Even
20.5
>>> statistics.median([9, 12, 80, 21, 34])  # Odd
21

I posted my solution at Python implementation of "median of medians" algorithm , which is a little bit faster than using sort(). My solution uses 15 numbers per column, for a speed ~5N which is faster than the speed ~10N of using 5 numbers per column. The optimal speed is ~4N, but I could be wrong about it.

Per Tom's request in his comment, I added my code here, for reference. I believe the critical part for speed is using 15 numbers per column, instead of 5.

#!/bin/pypy
#
# TH @stackoverflow, 2016-01-20, linear time "median of medians" algorithm
#
import sys, random


items_per_column = 15


def find_i_th_smallest( A, i ):
    t = len(A)
    if(t <= items_per_column):
        # if A is a small list with less than items_per_column items, then:
        #
        # 1. do sort on A
        # 2. find i-th smallest item of A
        #
        return sorted(A)[i]
    else:
        # 1. partition A into columns of k items each. k is odd, say 5.
        # 2. find the median of every column
        # 3. put all medians in a new list, say, B
        #
        B = [ find_i_th_smallest(k, (len(k) - 1)/2) for k in [A[j:(j + items_per_column)] for j in range(0,len(A),items_per_column)]]

        # 4. find M, the median of B
        #
        M = find_i_th_smallest(B, (len(B) - 1)/2)


        # 5. split A into 3 parts by M, { < M }, { == M }, and { > M }
        # 6. find which above set has A's i-th smallest, recursively.
        #
        P1 = [ j for j in A if j < M ]
        if(i < len(P1)):
            return find_i_th_smallest( P1, i)
        P3 = [ j for j in A if j > M ]
        L3 = len(P3)
        if(i < (t - L3)):
            return M
        return find_i_th_smallest( P3, i - (t - L3))


# How many numbers should be randomly generated for testing?
#
number_of_numbers = int(sys.argv[1])


# create a list of random positive integers
#
L = [ random.randint(0, number_of_numbers) for i in range(0, number_of_numbers) ]


# Show the original list
#
# print L


# This is for validation
#
# print sorted(L)[int((len(L) - 1)/2)]


# This is the result of the "median of medians" function.
# Its result should be the same as the above.
#
print find_i_th_smallest( L, (len(L) - 1) / 2)

In case you need additional information on the distribution of your list, the percentile method will probably be useful. And a median value corresponds to the 50th percentile of a list:

import numpy as np
a = np.array([1,2,3,4,5,6,7,8,9])
median_value = np.percentile(a, 50) # return 50th percentile
print median_value 

Here what I came up with during this exercise in Codecademy:

def median(data):
    new_list = sorted(data)
    if len(new_list)%2 > 0:
        return new_list[len(new_list)/2]
    elif len(new_list)%2 == 0:
        return (new_list[(len(new_list)/2)] + new_list[(len(new_list)/2)-1]) /2.0

print median([1,2,3,4,5,9])

Just two lines are enough.

def get_median(arr):
    '''
    Calculate the median of a sequence.
    :param arr: list
    :return: int or float
    '''
    arr = sorted(arr)
    return arr[len(arr)//2] if len(arr) % 2 else (arr[len(arr)//2] + arr[len(arr)//2-1])/2

median Function

def median(midlist):
    midlist.sort()
    lens = len(midlist)
    if lens % 2 != 0: 
        midl = (lens / 2)
        res = midlist[midl]
    else:
        odd = (lens / 2) -1
        ev = (lens / 2) 
        res = float(midlist[odd] + midlist[ev]) / float(2)
    return res

I had some problems with lists of float values. I ended up using a code snippet from the python3 statistics.median and is working perfect with float values without imports. source

def calculateMedian(list):
    data = sorted(list)
    n = len(data)
    if n == 0:
        return None
    if n % 2 == 1:
        return data[n // 2]
    else:
        i = n // 2
        return (data[i - 1] + data[i]) / 2

def midme(list1):

    list1.sort()
    if len(list1)%2>0:
            x = list1[int((len(list1)/2))]
    else:
            x = ((list1[int((len(list1)/2))-1])+(list1[int(((len(list1)/2)))]))/2
    return x


midme([4,5,1,7,2])

def median(array):
    if len(array) < 1:
        return(None)
    if len(array) % 2 == 0:
        median = (array[len(array)//2-1: len(array)//2+1])
        return sum(median) / len(median)
    else:
        return(array[len(array)//2])

I defined a median function for a list of numbers as

def median(numbers):
    return (sorted(numbers)[int(round((len(numbers) - 1) / 2.0))] + sorted(numbers)[int(round((len(numbers) - 1) // 2.0))]) / 2.0

import numpy as np
def get_median(xs):
        mid = len(xs) // 2  # Take the mid of the list
        if len(xs) % 2 == 1: # check if the len of list is odd
            return sorted(xs)[mid] #if true then mid will be median after sorting
        else:
            #return 0.5 * sum(sorted(xs)[mid - 1:mid + 1])
            return 0.5 * np.sum(sorted(xs)[mid - 1:mid + 1]) #if false take the avg of mid
print(get_median([7, 7, 3, 1, 4, 5]))
print(get_median([1,2,3, 4,5]))

A more generalized approach for median (and percentiles) would be:

def get_percentile(data, percentile):
    # Get the number of observations
    cnt=len(data)
    # Sort the list
    data=sorted(data)
    # Determine the split point
    i=(cnt-1)*percentile
    # Find the `floor` of the split point
    diff=i-int(i)
    # Return the weighted average of the value above and below the split point
    return data[int(i)]*(1-diff)+data[int(i)+1]*(diff)

# Data
data=[1,2,3,4,5]
# For the median
print(get_percentile(data=data, percentile=.50))
# > 3
print(get_percentile(data=data, percentile=.75))
# > 4

# Note the weighted average difference when an int is not returned by the percentile
print(get_percentile(data=data, percentile=.51))
# > 3.04

Function median:

def median(d):
    d=np.sort(d)
    n2=int(len(d)/2)
    r=n2%2
    if (r==0):
        med=d[n2] 
    else:
        med=(d[n2] + d[n2+1]) / 2
    return med

Try This

import math
def find_median(arr):
    if len(arr)%2==1:
        med=math.ceil(len(arr)/2)-1
        return arr[med]
    else:
        return -1
print(find_median([1,2,3,4,5,6,7,8]))

Implement it:

def median(numbers):
    """
    Calculate median of a list numbers.
    :param numbers: the numbers to be calculated.
    :return: median value of numbers.

    >>> median([1, 3, 3, 6, 7, 8, 9])
    6
    >>> median([1, 2, 3, 4, 5, 6, 8, 9])
    4.5
    >>> import statistics
    >>> import random
    >>> numbers = random.sample(range(-50, 50), k=100)
    >>> statistics.median(numbers) == median(numbers)
    True
    """
    numbers = sorted(numbers)
    mid_index = len(numbers) // 2
    return (
        (numbers[mid_index] + numbers[mid_index - 1]) / 2 if mid_index % 2 == 0
        else numbers[mid_index]
    )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

source from

Using Numpy : Fastest way

import numpy as np
m = np.median([0, 2, 5, 6, 8, 9, 9])
print("ans:", m)
# ans: 6.0

Simply, Create a Median Function with an argument as a list of the number and call the function.

def median(l):
    l = sorted(l)
    lent = len(l)
    if (lent % 2) == 0:
        m = int(lent / 2)
        result = l[m]
    else:
        m = int(float(lent / 2) - 0.5)
        result = l[m]
    return result

What I did was this:

def median(a):
    a = sorted(a)
    if len(a) / 2 != int:
        return a[len(a) / 2]
    else:
        return (a[len(a) / 2] + a[(len(a) / 2) - 1]) / 2

Explanation: Basically if the number of items in the list is odd, return the middle number, otherwise, if you half an even list, python automatically rounds the higher number so we know the number before that will be one less (since we sorted it) and we can add the default higher number and the number lower than it and divide them by 2 to find the median.

Here's the tedious way to find median without using the median function:

def median(*arg):
    order(arg)
    numArg = len(arg)
    half = int(numArg/2)
    if numArg/2 ==half:
        print((arg[half-1]+arg[half])/2)
    else:
        print(int(arg[half]))

def order(tup):
    ordered = [tup[i] for i in range(len(tup))]
    test(ordered)
    while(test(ordered)):
        test(ordered)
    print(ordered)


def test(ordered):
    whileloop = 0 
    for i in range(len(ordered)-1):
        print(i)
        if (ordered[i]>ordered[i+1]):
            print(str(ordered[i]) + ' is greater than ' + str(ordered[i+1]))
            original = ordered[i+1]
            ordered[i+1]=ordered[i]
            ordered[i]=original
            whileloop = 1 #run the loop again if you had to switch values
    return whileloop