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In this shot, we will discuss how to use the statistics.geometric_mean()
function in Python.
The statistics
module in Python is an extremely useful module that provides functions to calculate mathematical statistics of numeric data.
The statistics
module supports both int
and float
type numeric data. Some of the functions in the statistics
module in Python include:
mean()
median()
mode()
geometric_mean()
harmonic_mean()
The geometric mean is referred to as the $n^{th}$ root of the product of $n$ numbers. Say we have a set of numbers, $x_1, x_2, x_3$, … $x_n$. The geometric mean is defined as:
$\prod_{i=1}^n$$x_{i}^{n}$ = $\sqrt[n]{x_1x_2x_3}$
For example, let the list of numbers be 54, 24, 36. Their geometric mean is $\sqrt[3]{54.24.36}$ = 36.
Let’s take a look at the code snippet below.
import statisticsdata = [54, 24, 36]result = statistics.geometric_mean(data)print(result)
In line 1, we import the statistics
module to call geometric_mean()
.
In line 2, we provide a list of data whose geometric mean needs to be found.
In line 3, we compute the geometric mean of the provided numbers.
In line 4, we print the output, which is stored in result
.
Note:
Module_name.function_name
is the syntax for calling any function inside a module.- The output is not exactly 36 and has some floating points. This is because of the computational style of a machine. We can overcome this by using the
round()
function.
The statistics.geometric_mean()
function comes in handy when we want to compute the central tendency of datasets.
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Grokking Modern System Design Interview for Engineers & Managers
Ace your System Design Interview and take your career to the next level. Learn to handle the design of applications like Netflix, Quora, Facebook, Uber, and many more in a 45-min interview. Learn the RESHADED framework for architecting web-scale applications by determining requirements, constraints, and assumptions before diving into a step-by-step design process.