Named after Italian statistician Corrado Gini, the **Gini coefficient** is a way to measure the income distribution of a population.

The value for the Gini coefficient ranges from 0 to 1 where higher values represent greater income inequality and where:

**0**represents perfect income equality (everyone has the same income)**1**represents perfect income inequality (one individual has all the income)

You can find a list of Gini coefficients by country here.

The following example shows how to calculate a Gini coefficient in Python.

**Example: Calculate Gini Coefficient in Python**

To calculate a Gini coefficient in Python, we’ll need to first define a simple function to calculate a Gini coefficient for a NumPy array of values:

**import numpy as np
#define function to calculate Gini coefficient
def gini(x):
total = 0
for i, xi in enumerate(x[:-1], 1):
total += np.sum(np.abs(xi - x[i:]))
return total / (len(x)**2 * np.mean(x))**

Next, we’ll use this function to calculate a Gini coefficient for an array of income values.

For example, suppose we have the following list of annual incomes for 10 individuals:

Income: $50k, $50k, $70k, $70k, $70k, $90k, $150k, $150k, $150k, $150k

The following code shows how to use the **gini()** function we just created to calculate the Gini coefficient for this population:

#define NumPy array of income values incomes = np.array([50, 50, 70, 70, 70, 90, 150, 150, 150, 150]) #calculate Gini coefficient for array of incomes gini(incomes) 0.226

The Gini coefficient turns out to be **0.226**.

**Note**: In a real-world scenario there would be hundreds of thousands of different incomes for individuals in a certain country, but in this example we used 10 individuals as a simple illustration.

**Additional Resources**

The following tutorials explain how to calculate a Gini coefficient using different statistical software:

How to Calculate Gini Coefficient in R

How to Calculate Gini Coefficient in Excel