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.

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