pandas mad – Calculate Mean Absolute Deviation in Python

To find the mean absolute deviation of a series or a column in a DataFrame in pandas, the easiest way is to use the pandas mad() function.

df["Column1"].mad()

When doing data analysis, the ability to compute different summary statistics, such as the mean or standard deviation of a variable, is very useful to help us understand the data. One such summary statistic which can be useful is the mean absolute deviation of a variable.

The mean absolute deviation of a variable is computed as the mean of absolute deviation of data points from their mean.

Finding the mean absolute deviation of columns or a Series using pandas is easy. We can use the pandas mad() function to find the mean absolute deviation of a column of numbers.

Let’s say we have the following DataFrame.

df = pd.DataFrame({'Name': ['Jim', 'Sally', 'Bob', 'Sue', 'Jill', 'Larry'],
                   'Weight': [160.20, 160.20, 209.45, 150.35, 187.52, 187.52],
                   'Height': [50.10, 68.94, 71.42, 48.56, 59.37, 63.42] })

print(df)
# Output: 
    Name  Weight  Height
0    Jim  160.20   50.10
1  Sally  160.20   68.94
2    Bob  209.45   71.42
3    Sue  150.35   48.56
4   Jill  187.52   59.37
5  Larry  187.52   63.42

To get the mean absolute deviation of all columns in our DataFrame, we can use the pandas mad() function on the DataFrame in the following Python code:

print(df.mad())

# Output:
Weight    18.956667
Height     7.625000
dtype: float64

If we only want to get the mean absolute deviation of the column “Height”, we can do so easily like in the following Python code:

print(df["Height"].mad())

# Output:
7.625

Hopefully this article has been helpful for you to understand how to find the mean absolute deviation of a variable within a column or Series using the pandas mad() function in Python.