lol this is so elegant

Hello, i'am tring your solution in my machine, but i get error message from the compiler :

3 12 22 24 Traceback (most recent call last): File "meanMedianMode.py", line 5, in numbers = [list(map(int, input().split()))] File "", line 1 12 22 24 ^ SyntaxError: invalid syntax

I already trying to figure out what is wrong with this code but i'am still can not found the bugs. May you help me ?

Why use numpy and scipy when standard lib has statistics?

Yeah, but trying to do it without import is also cool for learning.

What is "size = int(input())" for? Apprently, the submission failed without this line of code, but I don't understand why. Please inform.

awesome very PYTHONIC code

lol

I'm sure this took little time to write, and so if your boss asked you to solve this problem he'd be very happy (bosses never want you to reinvent the wheel). My question is though, what have you learned about stats by doing this? I'd say no more than I learn about cooking when putting a ready-meal in the microwave. My pure C solution took me ages, but if I didn't already know mean/mode/median I sure as hell would now! Not meaning to be offensive by this, perhaps the real question should be is this challenge to learn about stats, or is it to learn about how to code a solution to a stats problem without needing to know anything about stats at all?

Could someone explain to me why this returns the smallest number for the mode when all the numbers are only repeated once?

Hi, I didn't understand how size variable effects output even though not seems to be used. I assume its use is hidden because when I changed the name it still runs but when I remove it, there is an error. Thanks for the code!

What did you learn? Name of methods which just corressponds to the name of statistic functions we wanted to perform?

That's awesome! I didn't even think of using numpy!

Did your code pass test case 3?

numpy is definitley the goto answer, but it just feels like cheating! Haha. But yeah, this is probably the most pythonic.

Can someone explain this code ?

Why there's no need to round the mean and median to 1 decimal place?

That won't work in Hackerrank.

Can anyone tell me why "size = int(input())" has to be there for the answer to be correct?

instead of int, i think you have to use float

Sorry, I'm kindof a beginner, what would be the complexity of this? And how would I even figure that out myself? Just looking up each different function?

Thanks!

import numpy as np from scipy import stats

size = int(input())

numbers = list(map(int, input().split())) print(np.mean(numbers)) print(np.median(numbers))

try: print(int(stats.mode(numbers))) except: print(min(numbers))

where you used size in your code

Can you please explain how does this take 10 numbers? I dont understand how this works here because when l try to run it on console it stops after taking 1 input.

script kiddie

HackerRank begineer here, didn't know you can import external libs before. What libs do I can/cannot import into the HackerRank python intrepreter? thanks for the advice!

sorry what is the 0 for in the last line

Whats the point of doing this way if you can use inbuilt functions!

can you tell me what * [0]* does in

print(int(stats.mode(numbers)[0]))

My Solution @ Java 8

import java.io.*;
import java.util.*;

public class Solution {

    private static Scanner scanner = new Scanner(System.in);
    
    public static void main(String[] args) {
        int n = scanner.nextInt();
        scanner.skip("(\r\n|[\n\r\u2028\u2029\u0085])?");

        int[] arr = new int[n];

        String[] arrItems = scanner.nextLine().split(" ");
        scanner.skip("(\r\n|[\n\r\u2028\u2029\u0085])?");

        for (int i = 0; i < n; i++) {
            int arrItem = Integer.parseInt(arrItems[i]);
            arr[i] = arrItem;
        }

        scanner.close();

        /*Mean*/
        double sum = 0;
        for (int number :
                arr) {
            sum += number;
        }
        System.out.println(sum / arr.length);
        /*Median*/
        Arrays.sort(arr);
        double median = 0;
        if (arr.length % 2 == 0) {
            median = ((double)arr[arr.length/2] + (double)arr[arr.length/2 - 1]) / 2;
        } else {
            median = arr[arr.length/2];
        }
        System.out.println(median);
        /*Mode*/
        int maxCount = 0;
        int maxValue = 0;
        Arrays.sort(arr);
        for (int number:
             arr) {
            int count = 0;
            for (int innerNumber:
                 arr) {
                if (number == innerNumber) {
                    ++count;
                }
            }
            if (count > maxCount) {
                maxCount = count;
                maxValue = number;
            }

        }
        System.out.println(maxValue);
    }
}

Why install new packages when you can go with standard Python libraries? Wouldn't this be a better, more portable Python 3? This involves no additional package installs like others are using in their discussion solutions with scipy, numpy, pandas, etc.

# With standard lib imports only
from statistics import mean, median

def basicstats(numbers):
    print(round(mean(numbers),1))
    print(median(numbers))
    print(max(sorted(numbers), key=numbers.count))

input() # Don't need array length, so replace var
numbers = list(map(float, input().split()))
basicstats(numbers)

EDIT: Removed import of collections, as mode can be done without.

Of course, if scipy is already included in an application, this isn't an issue.

Though, introducing additional non-standard packages adds complexity to the supply-chain (includes adding security complexity). For example, this year there was an Arbitrary Code Execution vulnerability associated with numpy that could be remedied only with an update to the package version.

Installing and depending on third-party packages in Python just because they assist in completing this particular task, when the standard libraries are capable with just as little code, is a problem. Imagine if you were needing to do this same task in an existing application, and a developer said they wanted to introduce scipy (which includes numpy as a dependency) packages in order to get the mode, median, and mean of certain data? Adding two new third-party packages? It's overkill and could be revealing a lack of experience/understanding of standard libraries.

Installing scipy and numpy adds ~45mb worth of files ( > 3k files) into the project to complete this task, which sounds like overkill.

Thank you bro !!

What is the use of this size object that you have created?

Hello

srious.......your code is very elegant Can you explain why did you use [0] in the mode function and how does that work ?

Thanks Somanshu

Thank you! Here's my copy, using your solution, but with some refs:

import numpy as np
from scipy import stats

n = int(input())
data = sorted([int(i) for i in input().split()])

data_mean = np.mean(data)

# numpy's median function returns the average of the two medians if our data 
# has an even number of elements, as required:
# https://numpy.org/devdocs/reference/generated/numpy.median.html?highlight=median#numpy.median
data_median = np.median(data)

# https://docs.python.org/3.4/library/statistics.html#statistics.mode
# "If there is more than one such value, only the smallest is returned"
data_mode = stats.mode(data)[0][0]

# Print number with one decimal places
print(data_mean)
print(data_median)
print(data_mode)

nice! But need to use float when reading and round the mean and median to 1 decimal - as required by the problem statement

import numpy as np from scipy import stats

n=int(input()) s1=input() s2=list(s1.split()) s3=list(map(int,s2))

print(np.mean(s3),np.median(s3),int(stats.mode(s3)[0]),sep='\n')

For those getting an error with this code:

import numpy as np from scipy import stats

size = int(input()) numbers = list(map(int, input().split())) print(np.mean(numbers)) print(np.median(numbers)) print(*stats.mode(numbers)[0]))

This unpacks the list element "mode" and prints as an integer value.

This is a tutorial to understand how these terms work, reinventing the wheel in this particular case is beneficial for building your foundation in statistics. So build the functions yourself;

Isnt' the idea of this excercise to make you excercise and implement stuff for yourself? And even it's not, isn't it better to do so to better understand the concepts?

The aim of the question is for you to understand the concept.

Why you have used [0] here.

please explain me these 2 lines

size = int(input()) numbers = list(map(int, input().split()))

Why stats.mode ??

/env/python

import numpy as np

arr = [input().split()] arr1 = np.array(arr).astype(np.float) index= [0] arr2 = np.delete(arr1,index) print (arr2.mean()) print (arr2.sum()/2) print (arr2.min())

hoo numpy vibes

That is pretty good. I knew there were some libraries, but thought it would throw an "module not found" error.

for code of c++, search my pure basic approach using recent filter

how is the size var being used here??

So Damn Awesome..A real help without any stupid complications..

Please kindly explain:

why using : size = int(input())
numbers = list(map(int, input().split()))

The input first line is N number of elements in the array, and second line is N space-separated integers that describe the array's elements. My understanding is that, if we use int(input()), size will be N+1 as the first line N is an integer in input.

\\

Please

why is "size = int(input())" exist can you explane ?

The list comp version of map/reduce is also elegant.

numbers = [int(num) for num in input().split()]

cries in c++

Python 3: Thanks to srious, wasn't able to figure out how to accept the input. Solution uses only numpy.

import numpy as np

N=int(input())
X=list(map(int, input().split()))
mean=np.mean(X)
X.sort()
if N%2!=0:
    median=X[N//2+1]
else:
    median=np.mean([X[N//2-1],X[(N//2)]])
v,c = np.unique(X,return_counts=True)
mode=v[c==c.max()].min()

print("{0:.1f}".format(mean))
print("{0:.1f}".format(median))
print("{}".format(mode))