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

**Method 1: Calculate Geometric Mean Using SciPy**

from scipy.stats import 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 SciPy library to calculate the geometric mean of an array of values:

from scipy.stats import 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 NumPy 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.

Thus, you can use the following bit of code to remove any zeros from an array before calculating the geometric mean:

#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]

**Additional Resources**

How to Calculate Mean Squared Error (MSE) in Python

How to Calculate Mean Absolute Error in Python