# How to calculate mean and standard deviation in pandas with example

In this tutorial, you will learn how to calculate mean and standard deviation in pandas with example

Mean():   Mean means average value in stastistics, we can calculate by sum of all elements and divided by number of elements in that series or dataframe.

Formula mean = Sum of elements/number of elements

Example : 1, 4, 5, 6, 7,3

Mean = (1+4+5+6+7+3)/6

Mean = 4.333333

Pandas has inbuilt mean() function to calculate mean values. You can calculate for entire dataframe or single column also.

Syntax : DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

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

Exclude NA/null values when computing the result.

level : int or level name, default None

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

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 total mean print(data.mean()) #to calculate mean for specific column print(data['salary'].mean()) ``` ` ` ` `

Output:

```experience        2.833333
join_year      2017.833333
salary        30064.833333
dtype: float64
30064.833333333332```

Standard deviation ():  The standard deviation measures the spread of the data about the mean value. we can calculate standard deviation by sqrt of variance it will give some measure about, how far elements from the mean.

Example : 1, 4, 5, 6, 7,3

Mean = (1+4+5+6+7+3)/6

Mean = 4.333333

We can calculate standard devaition in pandas by using pandas.DataFrame.std() function.

Syntax: DataFrame.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), 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.

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

#Standard deviation example program

` ` ``` import pandas as pd data = pd.DataFrame({ 'd1':[1, 4, 5, 6, 7,3]}) #To calculate total mean print(data.std()) ``` ` ` ` `

Output:

```d1 2.160247
dtype: float64```