Standard deviation Function in Python pandas (Dataframe, Row and column wise standard deviation)

Standard deviation Function in python pandas is used to calculate standard deviation of a given set of numbers, Standard deviation of a data frame, Standard deviation of column or column wise standard deviation in pandas and Standard deviation of rows, let’s see an example of each. We need to use the package name “statistics” in calculation of median. In this tutorial we will learn,

  • How to find the standard deviation of a given set of numbers
  • How to find standard deviation of a dataframe in pandas
  • How to find the standard deviation of a column in pandas dataframe
  • How to find row wise standard deviation of a pandas dataframe

 

Syntax of standard deviation Function in python

DataFrame.std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None)

Parameters :

axis : {rows (0), columns (1)}

skipna : Exclude NA/null values when computing the result

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

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

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

 

Standard deviation Function in Python pandas

Simple standard deviation function is shown below

# calculate standard deviation
import numpy as np

print(np.std([1,9,5,6,8,7]))
print(np.std([4,-11,-5,16,5,7,9]))

output:

2.82842712475
8.97881103594

 

Standard deviation of a dataframe in pandas python:

Create dataframe

import pandas as pd
import numpy as np

#Create a DataFrame
d = {
    'Name':['Alisa','Bobby','Cathrine','Madonna','Rocky','Sebastian','Jaqluine',
   'Rahul','David','Andrew','Ajay','Teresa'],
   'Score1':[62,47,55,74,31,77,85,63,42,32,71,57],
   'Score2':[89,87,67,55,47,72,76,79,44,92,99,69],
   'Score3':[56,86,77,45,73,62,74,89,71,67,97,68]}



df = pd.DataFrame(d)
df

So the resultant dataframe will be

Standard deviation Function in Python - pandas (Dataframe, Row and column wise standard deviation) 01

 

Standard deviation of the dataframe in pandas python:

# standard deviation of the dataframe
df.std()

will calculate the standard deviation of the dataframe across columns so the output will

Score1     17.446021
Score2     17.653225
Score3     14.355603
dtype: float64

 

 

Column wise Standard deviation of the dataframe in pandas python:

# column standard deviation  of the dataframe
df.std(axis=0)

axis=0 argument calculates the column wise standard deviation of the dataframe so the result will be

Score1     17.446021
Score2     17.653225
Score3     14.355603
dtype: float64

 

Row standard deviation of the dataframe in pandas python:

# Row standard deviation of the dataframe
df.std(axis=1)

axis=1 argument calculates the row wise standard deviation of the dataframe so the result will be

standard deviation Function in Python pandas (Dataframe, Row and column wise standard deviation) 1

 

Calculate the standard deviation of the specific Column in pandas python

# standard deviation of the specific column
df.loc[:,"Score1"].std()

The above code calculates the standard deviation of the “Score1” column so the result will be

17.446020645512156

 

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