> For the complete documentation index, see [llms.txt](https://stephanosterburg.gitbook.io/scrapbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stephanosterburg.gitbook.io/scrapbook/math/hackerrank/mean-medium-mode.md).

# Mean, Medium, Mode

### [Mean](http://mathworld.wolfram.com/ArithmeticMean.html) ($$\mu$$)

The average of all the integers in a set of values. Here is the basic formula for calculating the *mean* of a set of $$n$$ values: $$\mu = \frac{\sum\_{i=1}^n x\_i}{n}$$, where $$x$$is the $$i^{th}$$element of the set. 

### [Median](http://mathworld.wolfram.com/StatisticalMedian.html)

The midpoint value of a data set for which an equal number of samples are *less than* and *greater than* the value. For an odd sample size, this is the middle element of the sorted sample; for an even sample size, this is the *average* of the $$2$$ middle elements of the sorted sample. 

### [Mode](http://mathworld.wolfram.com/Mode.html)

The element(s) that occur most frequently in a data set. For the set $${1, 1, 1, 2, 2, 3, 4, 4}$$, the mode is $$1$$ because the number $$1$$ appears three times in the set and every other number in the set has a frequency $$< 3$$. In contrast, the set $${1, 2, 3, 4}$$ is [multimodal](http://mathworld.wolfram.com/Multimodal.html) because no number in the set appears more than $$1$$ time, so every number in the set is a valid *mode*.&#x20;

### [Precision and Scale](https://en.wikipedia.org/wiki/Significant_figures)

These are important terms to understand when formatting your output:

* *Precision* refers to the number of significant digits in a number. For example, the numbers $$123.45$$ and $$0.012345$$ both have a precision of $$5$$.
* *Scale* refers to the number of significant digits to the *right* of the decimal point. For example, the number $$123.45$$ has a scale of $$2$$ decimal places. This term is sometimes misrepresented as *precision* in documentation.&#x20;

```
num = int(input())
values = list(map(int, input().split()))

mean = sum(values)/num
print(mean)

values.sort()
if num % 2 == 0:
    v1 = values[int(num/2)-1]
    v2 = values[int(num/2)]
    median = (v1+v2)/2
else:
    c = floor(num % 2) + 1
    median = values[c]
print(median)

mode = max(values, key=lambda n: values.count(n))
print(mode)

```