How to calculate Skewness and Kurtosis for a data set in python?

Description

To calculate the skewness and kurtosis for a sample data set using python.

Skewness:

   It represents the shape of the
 distribution.

   Skewness can be quantified to define
 the extent to  which a distribution
 differs from a normal distribution.

   For calculating skewness by using 
df.skew()
 python  inbuilt function.

Kurtosis:

   Kurtosis is the measure of thickness
 or heaviness of the 
given distribution.

   Its actually represents the 
height of the distribution.

   The distribution with kurtosis equal to
3 is known as  mesokurtic. A random variable 
which follows normal  distribution has 
kurtosis 3.

   If the kurtosis is less than three,
 the distribution is called  as
 platykurtic. Here, the distribution has shorter
 and  thinner tails than normal distribution.

   If the kurtosis is greater than three, 
the distribution is  called as leptykurtic.
 Here, the distribution has longer  and
 fatter tails than normal distribution.

   For calculating kurtosis by using 
df.kurtosis() python 
inbuilt function.

#import pandas library
import pandas as pd
#Read the data set
data=pd.read_csv(‘/home/soft27/soft27/Sathish/
Pythonfiles/Employee.csv’)
#creating data frame
df=pd.DataFrame(data)
print(“The value of Skewness is:”)
#calculating the skewness
print(df.skew())
print(“The value of kurtosis is:”)
#calculating the kurtosis
print(df.kurtosis())

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