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The variation in any dataset can be determined by the mean absolute deviation(MAD) and it can be defined as the average distance between each data value and the mean. In this tutorial, we will learn the Python pandas `DataFrame.mad()` method. When the `DataFrame.mad()` method applied on the DataFrame, it returns the mean absolute deviation of the values over the requested `axis`.

The below shows the syntax of the `DataFrame.mad()` method.

### Syntax

``DataFrame.mad(axis=None, skipna=None, level=None)``

### Parameters

axis: '0' represents the index and '1' represents the columns. When the `axis=0`, method applied over the `index` axis and when the `axis=1` method applied over the `column` axis.

skipna: It represents the bool(True or False). The default value is None. If this parameter is `True`, it excludes all NA/null values when computing the result.

level: It represents the int or level name, the default value is None. It counts along with the particular level, if the DataFrame is Multiindex, collapsing into a Series.

## Example: The `DataFrame.mad()` Method

Let's create a DataFrame and get the mean absolute deviation of the values over the `index` axis by assigning parameter `axis=0 `in the `DataFrame.mad()` method. See the below example.

``````#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9],"D":[10,11,12]})
print("----------The DataFrame is------")
print(df)
print("---The mean absolute deviation of the DataFrame is---")

----------The DataFrame is------
A B C D
0 1 4 7 10
1 2 5 8 11
2 3 6 9 12
---The mean absolute deviation of the DataFrame is---
A 0.666667
B 0.666667
C 0.666667
D 0.666667
dtype: float64

## Example: The `DataFrame.mad()` method along the `column` axis

Let's create a DataFrame and get the mean absolute deviation of the values over the column axis by assigning parameter `axis=1 `in the `DataFrame.mad()` method. See the below example.

``````#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9],"D":[10,11,12]})
print("----------The DataFrame is------")
print(df)
print("---The mean absolute deviation of the DataFrame is---")

----------The DataFrame is------
A B C D
0 1 4 7 10
1 2 5 8 11
2 3 6 9 12
---The mean absolute deviation of the DataFrame is---
0 3.0
1 3.0
2 3.0
dtype: float64

## Example: The `DataFrame.mad()` Method excluding null values

Let's create a DataFrame with null values and get the mean absolute deviation of the values over the index axis excluding null values by passing parameter `skipna `in the `DataFrame.mad()` method. It excludes all NA/null values when computing the results. See the below example.

``````#importing pandas as pd
import pandas as pd
#creating the DataFrame
df = pd.DataFrame({"A":[1,None,3],"B":[None,5,6],"C":[7,8,9],"D":[10,11,None]})
print("----------The DataFrame is------")
print(df)
print("---The mean absolute deviation of the DataFrame is---")

----------The DataFrame is------
A B C D
0 1.0 NaN 7 10.0
1 NaN 5.0 8 11.0
2 3.0 6.0 9 NaN
---The mean absolute deviation of the DataFrame is---
A 1.000000
B 0.500000
C 0.666667
D 0.500000
dtype: float64

### Conclusion

In this tutorial, we learned the Python pandas `DataFrame.mad()` method. We learned the syntax, parameters and applying this method on the DataFrame to understand the` DataFrame.mad()` method.

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