The **median absolute deviation** measures the spread of observations in a dataset.

It’s a particularly useful metric because it’s less affected by outliers than other measures of dispersion like standard deviation and variance.

The formula to calculate median absolute deviation, often abbreviated MAD, is as follows:

**MAD = median(|x _{i} – x_{m}|)**

where:

**x**The i_{i}:^{th}value in the dataset**x**The median value in the dataset_{m}:

The following examples shows how to calculate the median absolute deviation in Python by using the **mad** function from statsmodels.

**Example 1: Calculate MAD for an Array**

The following code shows how to calculate the median absolute deviation for a single NumPy array in Python:

import numpy as np from statsmodels import robust #define data data = np.array([1, 4, 4, 7, 12, 13, 16, 19, 22, 24]) #calculate MAD robust.mad(data) 11.1195

The median absolute deviation for the dataset turns out to be **11.1195**.

It’s important to note that the formula used to calculate MAD computes a robust estimate of the standard deviation assuming a normal distribution by scaling the result by a factor of roughly 0.67.

To avoid using this scaling factor, simply set c = 1 as follows:

#calculate MAD without scaling factor robust.mad(data, c=1) 7.5

**Example 2: Calculate MAD for a DataFrame**

The following code shows how to calculate MAD for a single column in a pandas DataFrame:

#make this example reproducible np.random.seed(1) #create pandas DataFrame data = pd.DataFrame(np.random.randint(0, 10, size=(5, 3)), columns=['A', 'B', 'C']) #view DataFrame data A B C 0 5 8 9 1 5 0 0 2 1 7 6 3 9 2 4 4 5 2 4 #calculate MAD for columnBdata[['B']].apply(robust.mad) B 2.965204 dtype: float64

The median absolute deviation for column *B* turns out to be **2.965204**.

We can use similar syntax to calculate MAD for multiple columns in the pandas DataFrame:

#calculate MAD for all columns data[['A', 'B', 'C']].apply(robust.mad) A 0.000000 B 2.965204 C 2.965204 dtype: float64

The median absolute deviation is **0 **for column A, **2.965204** for column B, and **2.965204 **for column C.

**Additional Resources**

How to Calculate MAPE in Python

How to Calculate SMAPE in Python

How to Calculate RMSE in Python