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Write a program in Python to compute grouped data covariance and calculate grouped data covariance between two columns in a given dataframe
Assume, you have a dataframe and the result for calculating covariance from grouped data and corresponding column as,
Grouped data covariance is: mark1 mark2 subjects maths mark1 25.0 12.500000 mark2 12.5 108.333333 science mark1 28.0 50.000000 mark2 50.0 233.333333 Grouped data covariance between two columns: subjects maths 12.5 science 50.0 dtype: float64
Solution
To solve this, we will follow the steps given below −
Define a dataframe
Apply groupby function inside dataframe subjects column
df.groupby('subjects')Apply covariance function to grouped data and store insied group_data,
group_data = df.groupby('subjects').cov()Apply lambda function for mark1 and mark2 columns with groupby records from the subjects column. It is defined below,
df.groupby('subjects').apply(lambda x: x['mark1'].cov(x['mark2']Example
Let’s see the below code to get a better understanding −
import pandas as pd
df =
pd.DataFrame({'subjects':['maths','maths','maths','science','science','science'],
'mark1':[80,90,85,95,93,85],
'mark2':[85,90,70,75,95,65]})
print("DataFrame is:\n",df)
group_data = df.groupby('subjects').cov()
print("Grouped data covariance is:\n", group_data)
result = df.groupby('subjects').apply(lambda x: x['mark1'].cov(x['mark2']))
print("Grouped data covariance between two columns:\n",result)Output
DataFrame is: subjects mark1 mark2 0 maths 80 85 1 maths 90 90 2 maths 85 70 3 science 95 75 4 science 93 95 5 science 85 65 Grouped data covariance is: mark1 mark2 subjects maths mark1 25.0 12.500000 mark2 12.5 108.333333 science mark1 28.0 50.000000 mark2 50.0 233.333333 Grouped data covariance between two columns: subjects maths 12.5 science 50.0 dtype: float64
Updated on: 25-Feb-2021
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