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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
Last updated
local_offer PythonPandas
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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 B
0 2 2
1 4 5
2 6 8

Computing MAD column-wise

To compute MAD for each column:

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

Computing MAD row-wise

To compute MAD for each row:

df.mad(axis=1)
0 0.0
1 0.5
2 1.0
dtype: float64

Specifying skipna

Consider the following DataFrame:

df = pd.DataFrame({"A":[3,pd.np.nan,5]})
df
A
0 3.0
1 NaN
2 5.0

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

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

To consider missing values:

df.mad(skipna=False)
A NaN
dtype: 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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Published by Isshin Inada
Edited by 0 others
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