How to Calculate MAPE in Python

There are different ways to calculate the Mean Absolute Percentage Error (MAPE) in Python, but here are two common methods:

Method 1: Using the numpy package

import numpy as np

def MAPE(actual, predicted):
    mask = actual != 0
    return (np.fabs(actual - predicted)/actual)[mask].mean() * 100

Method 2: Without using any package

def MAPE(actual, predicted):
    mask = actual != 0
    return (sum(np.abs((actual - predicted) / actual)[mask]) / len(actual)) * 100

In both methods, actual is a list or NumPy array of the actual (observed) values, and predicted is also a list or NumPy array of the predicted values. The MAPE is calculated by finding the average of the absolute differences between the actual and predicted values, divided by the actual values, and then multiplying by 100 to express it as a percentage.

You can use either of these functions to calculate the MAPE for your data by passing in the actual and predicted arrays as arguments, as shown below:

actual = [10, 20, 30, 40] 
predicted = [12, 22, 32, 42]

mape = MAPE(actual, predicted)

print(mape)

This will output the MAPE value for the given data.