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# How to find the standard deviation of specific columns in a dataframe in Pandas Python?

Standard deviation tells about how the values in the dataset are spread. They also tells how far the values in the dataset are from the arithmetic mean of the columns in the dataset.

Sometimes, it may be required to get the standard deviation of a specific column that is numeric in nature. This is where the std() function can be used. The column whose mean needs to be computed can be indexed to the dataframe, and the mean function can be called on this using the dot operator.

The index of the column can also be passed to find the standard deviation.

Let us see a demonstration of the same −

## Example

import pandas as pd my_data = {'Name':pd.Series(['Tom','Jane','Vin','Eve','Will']),'Age':pd.Series([45, 67, 89, 12, 23]),'value':pd.Series([8.79,23.24,31.98,78.56,90.20])} print("The dataframe is :") my_df = pd.DataFrame(my_data) print(my_df) print("The standard deviation of column 'Age' is :") print(my_df['Age'].std()) print("The standard deviation of column 'value' is :") print(my_df['value'].std())

## Output

The dataframe is : Name Age value 0 Tom 45 8.79 1 Jane 67 23.24 2 Vin 89 31.98 3 Eve 12 78.56 4 Will 23 90.20 The standard deviation of column 'Age' is : 31.499206339207976 The standard deviation of column 'value' is : 35.747101700697364

## Explanation

The required libraries are imported, and given alias names for ease of use.

Dictionary of series consisting of key and value is created, wherein a value is actually a series data structure.

This dictionary is later passed as a parameter to the ‘Dataframe’ function present in the ‘pandas’ library

The dataframe is printed on the console.

We are looking at computing the standard deviation of a specific column that contain numeric values in them.

The ‘std’ function is called on the dataframe by specifying the name of the column, using the dot operator.

The standard deviation of numeric column is printed on the console.

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