π **Question**: How to find the index of the median element in a Python list? Something like `argmedian()`

?

In case you need a quick refresher, I have written this tutorial on finding the median itself (but not yet its index):

π **Recommended**: 6 Ways to Get the Median of a Python List

## Solution

A multi-liner solution to get the index of the median index is to first sort the list, take the mid-element from the sorted list, and search the index of the median using the `list.index()`

method. This finds the index of the first occurrence of the median.

Here’s a code snippet that does that:

def median_index(lst): sorted_lst = sorted(lst) mid = len(lst) // 2 median = sorted_lst[mid] return lst.index(median) print(median_index([10, 1, 5])) # 2 print(median_index([10, 1, 5, 99, 100, 100])) # 3 print(median_index([10, 1, 5, 99, 100, 100, 0])) # 0

This is a Python function that takes a list of numbers as input and returns the index of the median element of the list. Here’s how the code works:

`def median_index(lst)`

: This line defines a function called`median_index`

that takes a list`lst`

as input.`sorted_lst = sorted(lst)`

: This line creates a new list called`sorted_lst`

that contains the same elements as the input list`lst`

, but sorted in ascending order using the built-in`sorted()`

function.`mid = len(lst) // 2`

: This line calculates the index of the median element in the original unsorted list`lst`

by dividing the length of the list by 2 using the floor division operator`//`

.`median = sorted_lst[mid]`

: This line assigns the value of the median element in the sorted list`sorted_lst`

to a new variable called`median`

. If the length of the list is odd, this will be the element at the index`mid`

. If the length of the list is even,`mid`

will be the index of the element to the left of the middle two elements, and`median`

will be the leftmost of those two elements.`return lst.index(median)`

: This line returns the index of the median element in the original unsorted list`lst`

by using the built-in`index()`

function to find the index of the first occurrence of the`median`

value in`lst`

.- The
`print()`

statements at the end of the code call the`median_index()`

function with different input lists and print the output to the console. For example,`print(median_index([10, 1, 5]))`

will print`2`

because the median element of the input list`[10, 1, 5]`

is`5`

, and its index in the list is`2`

.

## Python One-Liner to Find the Index of Median

I love Python one-liners. β₯οΈ

So, here’s the same code condensed in a single line of Python code that finds the index of the median element in the `lst`

using `lst.index(sorted(lst)[len(lst)//2])`

def median_index(lst): return lst.index(sorted(lst)[len(lst)//2])

## Python One-Liners Book: Master the Single Line First!

**Python programmers will improve their computer science skills with these useful one-liners.**

*Python One-Liners* will teach you how to read and write “one-liners”: ** concise statements of useful functionality packed into a single line of code. **You’ll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert.

The book’s five chapters cover (1) tips and tricks, (2) regular expressions, (3) machine learning, (4) core data science topics, and (5) useful algorithms.

Detailed explanations of one-liners introduce ** key computer science concepts **and

**. You’ll learn about advanced Python features such as**

*boost your coding and analytical skills**,*

**list comprehension****,**

*slicing***,**

*lambda functions***,**

*regular expressions***and**

*map***functions, and**

*reduce***.**

*slice assignments*You’ll also learn how to:

- Leverage data structures to
**solve real-world problems**, like using Boolean indexing to find cities with above-average pollution - Use
**NumPy basics**such as*array*,*shape*,*axis*,*type*,*broadcasting*,*advanced indexing*,*slicing*,*sorting*,*searching*,*aggregating*, and*statistics* - Calculate basic
**statistics**of multidimensional data arrays and the K-Means algorithms for unsupervised learning - Create more
**advanced regular expressions**using*grouping*and*named groups*,*negative lookaheads*,*escaped characters*,*whitespaces, character sets*(and*negative characters sets*), and*greedy/nongreedy operators* - Understand a wide range of
**computer science topics**, including*anagrams*,*palindromes*,*supersets*,*permutations*,*factorials*,*prime numbers*,*Fibonacci*numbers,*obfuscation*,*searching*, and*algorithmic sorting*

By the end of the book, you’ll know how to ** write Python at its most refined**, and create concise, beautiful pieces of “Python art” in merely a single line.