There are two ways to calculate the geometric mean in Python:

Method 1: Calculate Geometric Mean Using SciPy

from scipy.statsimport gmean

#calculate geometric mean
gmean([value1, value2, value3, ...])

Method 2: Calculate Geometric Mean Using NumPy

import numpy as np

#define custom function
def g_mean(x):
    a = np.log(x)
    return np.exp(a.mean())

#calculate geometric mean 
g_mean([value1, value2, value3, ...])

Both methods will return the exact same results.

The following examples show how to use each of these methods in practice.

Example 1: Calculate Geometric Mean Using SciPy

The following code shows how to use the gmean() function from the library to calculate the geometric mean of an array of values:

from scipy.statsimport gmean

#calculate geometric mean
gmean([1, 4, 7, 6, 6, 4, 8, 9])

4.81788719702029

The geometric mean turns out to be 4.8179.

Example 2: Calculate Geometric Mean Using NumPy

The following code shows how to write a custom function to calculate a geometric mean using built-in functions from the library:

import numpy as np

#define custom function
def g_mean(x):
    a = np.log(x)
    return np.exp(a.mean())

#calculate geometric mean
g_mean([1, 4, 7, 6, 6, 4, 8, 9])

4.81788719702029

The geometric mean turns out to be 4.8179, which matches the result from the previous example.

How to Handle Zeros

Note that both methods will return a zero if there are any zeros in the array that you’re working with.

#create array with some zeros
x = [1, 0, 0, 6, 6, 0, 8, 9]

#remove zeros from array 
x_new = [i for i in x if i != 0]

#view updated array
print(x_new)

[1, 6, 6, 8, 9]