# How to calculate standard deviation in pandas on dataframes and series with example in python

In this tutorial, You will learn how to write a program to calculate standard deviation in pandas.

Pandas has a inbuilt function std() , we can use that. You can calculate for standard deviation for entire data and single column also.

Standard Deviation on Dataframes:

Syntax: DataFrame.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Parameters:
axis : {index (0), columns (1)}
skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

ddof : int, default 1

Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of elements.

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

` ` ``` import pandas as pd data = pd.DataFrame({ 'name':['ravi','david','raju','david','kumar','teju'], 'experience':[1,2,3,4,5,2], 'salary':[15000,20000,30000,45389,50000,20000], 'join_year' :[2017,2017,2018,2018,2019,2018] }) #To calculate standard deviation print(data.std()) #to calculate standard deviation for specific column print(data['salary'].std()) ``` ` ` ` `

Output:

Standard Deviation on Series:

Syntax: pandas.Series.std
Series.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)[source]¶
Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

Parameters:
axis : {index (0)}
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar

ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N represents the number of elements.

numeric_only : boolean, default None
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns:
std : scalar or Series (if level specified)

` ` ``` import pandas as pd d= pd.Series([1,2,3,6]) #To calculate standard deviation print(d.std()) ``` ` ` ` `