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# Pandas DataFrame | mad method

Pandas
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DataFrame
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Basic and Descriptive Statistics
schedule Jul 1, 2022
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Pandas DataFrame.mad(~) method computes the mean absolute deviation (MAD) for each row or column of the DataFrame.

Note that MAD is calculated like follows:

$$\mathrm{MAD}=\frac{1}{N}\sum_{i=0}^{N-1} |x_i-\bar{x}|$$

Where,

• $N$ is the number of data points in the row/column

• $x_i$ is the $i$-th value in the row/column

• $\bar{x}$ is the mean of the row/column

# Parameters

1. axislink | int or string | optional

Whether to compute the MAD row-wise or column-wise:

Axis

Description

MAD is computed for each column.

"index" or 0

MAD is computed for each row.

"columns" or 1

By default, axis=0.

2. skipnalink | boolean | optional

Whether or not to skip NaN. Skipped NaN would not count towards the total size ($N$), which is the divisor when computing MAD. By default, skipna=True.

3. level | string or int | optional

The name or the integer index of the level to consider. This is relevant only if your DataFrame is Multi-index.

# Return Value

If the level parameter is specified, then a DataFrame will be returned. Otherwise, a Series will be returned.

# Examples

Consider the following DataFrame:

 df = pd.DataFrame({"A":[2,4,6],"B":[2,5,8]})df A B0 2 21 4 52 6 8 

To compute MAD for each column:

 df.mad() A 1.333333B 2.000000dtype: float64 

To compute MAD for each row:

 df.mad(axis=1) 0 0.01 0.52 1.0dtype: float64 

## Specifying skipna

Consider the following DataFrame:

 df = pd.DataFrame({"A":[3,pd.np.nan,5]})df A0 3.01 NaN2 5.0 

By default, skipna=True, which means that all missing values are ignored:

 df.mad() # skipna=True A 1.0dtype: float64 

To consider missing values:

 df.mad(skipna=False) A NaNdtype: float64 

With skipna=False, if a row/column contains one or more missing values, then the MAD for that row/column will be NaN.

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