Measures of Variance With Python
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In statistics, variance is a measure of how far a value in a data set lies from the mean value. In other words, it indicates how dispersed the values are. It is measured by using standard deviation. The other method commonly used is skewness. Both of these are calculated by using functions available in pandas library.
Measuring Standard Deviation
Standard deviation is square root of variance. variance is the average of squared difference of values in a data set from the mean value. In python we calculate this value by using the function std() from pandas library.
# Measuring The Standard Deviation Exampleimport pandas as pd#Create a Dictionary of seriesa = {'Name': pd.Series(['Madhav','Ramesh','Divya','Ankita','Santosh','Ketan', 'Niloy','Preethi','Bhaskar','Deeksha']),'Age': pd.Series([25,26,25,23,30,25,23,34,40,30]),'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80[)# Create a Dataframedf = pd.DataFrame(a)# Calculate the Standard Deviationprint(repr(df.std()))
Its output is as follows −
Age 5.466057
Rating 0.720525
dtype: float64
Measuring Skewness
It used to determine whether the data is symmetric or skewed. If the index is between -1 and 1, then the distribution is symmetric. If the index is no more than -1 then it is skewed to the left and if it is at least 1, then it is skewed to the right
# Measuring The Skewness Exampleimport pandas as pd#Create a Dictionary of seriesa = {'Name': pd.Series(['Madhav','Ramesh','Divya','Ankita','Santosh','Ketan',
'Niloy','Preethi','Bhaskar','Deeksha']),'Age': pd.Series([25,26,25,23,30,25,23,34,40,30]),'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80])}# Create a Dataframedf = pd.DataFrame(a)# Calculate the Standard Deviationprint(repr(df.skew()))
Its output is as follows −
Age 1.309954
Rating -0.030865
dtype: float64
So the distribution of age rating is symmetric while the distribution of age is skewed to the right.
Also Attacking the ipynb file for reference
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