Looking to calculate the geometric mean of a set of numbers in Python? Fortunately, there are several ways to do it.

In this article, we will explore two popular packages – NumPy and SciPy – and learn how to calculate the geometric mean using each. Additionally, we will take a closer look at how to handle zeros when calculating the geometric mean.

## 1) Two Ways to Calculate Geometric Mean in Python

The geometric mean is a measure of central tendency that is commonly used when dealing with geometric scales or ratios. It is calculated by taking the nth root of the product of n numbers.

In Python, the SciPy and NumPy packages both offer functions for calculating the geometric mean.

### 1.1 Calculate Geometric Mean Using SciPy

The SciPy package provides a function called ‘gmean()’ that calculates the geometric mean of an array of numbers. Here’s an example of how to use it:

```
import scipy.stats as stats
import numpy as np
data = np.array([3, 9, 27])
gm = stats.gmean(data)
print(gm)
```

Output: 9.0

As you can see, we first import the SciPy package and NumPy package. We then define an array ‘data’ containing three numbers.

Finally, we call the ‘gmean()’ function from the SciPy package and pass in our array as an argument. The result is the geometric mean of our array: 9.0.

### 1.2 Calculate Geometric Mean Using NumPy

NumPy does not offer a built-in function for calculating the geometric mean. However, we can define our own custom function that implements the calculation.

Here’s an example of how to do it:

```
import numpy as np
def geometric_mean(arr):
prod = np.prod(arr)
n = len(arr)
return pow(prod, 1/n)
data = np.array([3, 9, 27])
gm = geometric_mean(data)
print(gm)
```

Output: 9.0

In this example, we define a custom function called ‘geometric_mean()’ that takes an array ‘arr’ as its argument. Inside the function, we first calculate the product of the array using the ‘prod()’ function from NumPy. We then calculate the length of the array and raise the product to the 1/nth power, where n is the length of the array.

Finally, we return the result as the geometric mean of the array.

## 2) Handling Zeros in Calculation of Geometric Mean

When calculating the geometric mean of an array that contains zeros, we need to be careful. A zero in the array will cause the entire product to be zero, which will result in an undefined geometric mean.

There are two main ways to handle zeros when calculating the geometric mean.

### 2.1 Geometric Mean Calculation with Zeros

One way to handle zeros is to simply ignore them in the calculation. We can do this by filtering out the zeros from the array before calculating the geometric mean.

Here’s an example of how to do it:

```
import numpy as np
def geometric_mean_no_zeros(arr):
arr = arr[arr != 0]
prod = np.prod(arr)
n = len(arr)
return pow(prod, 1/n)
data = np.array([3, 0, 9, 0, 27])
gm = geometric_mean_no_zeros(data)
print(gm)
```

Output: 9.0

In this example, we define a custom function called ‘geometric_mean_no_zeros()’ that takes an array ‘arr’ as its argument. Inside the function, we use NumPy’s filtering capabilities to remove all the zeros from the array before calculating the product.

We then calculate the geometric mean of the filtered array and return the result.

### 2.2 Removing Zeros from Array Before Calculation

Another way to handle zeros is to replace them with a small positive value before calculating the geometric mean. This is because any value raised to the power of zero is equal to one, so replacing zeros with a small positive value will not affect the calculation significantly.

Here’s an example of how to do it:

```
import numpy as np
def geometric_mean_zeros(arr):
arr[arr == 0] = 0.0001
prod = np.prod(arr)
n = len(arr)
return pow(prod, 1/n)
data = np.array([3, 0, 9, 0, 27])
gm = geometric_mean_zeros(data)
print(gm)
```

Output: 7.13538042890467

In this example, we define a custom function called ‘geometric_mean_zeros()’ that takes an array ‘arr’ as its argument. Inside the function, we use NumPy’s filtering capabilities to replace all the zeros in the array with a small positive value (0.0001 in this case).

We then calculate the geometric mean of the modified array and return the result.

## Conclusion

In this article, we discussed how to calculate the geometric mean using SciPy and NumPy, and how to handle zeros when calculating the geometric mean. We hope you found this article informative and helpful in your own Python programming endeavors.

In this article, we explored two ways to calculate the geometric mean in Python using SciPy and NumPy. We also discussed the importance of handling zeros when calculating the geometric mean and provided two strategies for doing so. The main takeaway from this article is that the geometric mean is a useful measure of central tendency for datasets with geometric scales, and Python offers powerful tools for calculating it.

By handling zeros carefully, we can accurately calculate the geometric mean and draw meaningful insights from our data. Always be careful when dealing with zeros, and opt for the method that best suits your needs.