How to Calculate Covriance in dataframes | pandas.DataFrame.cov() function
pandas.DataFrame.cov(): This function compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame.
Syntax: DataFrame.cov(min_periods=None):
Compute pairwise covariance of columns, excluding NA/null values.
Both NA and null values are automatically excluded from the calculation. (See the note below about bias from missing values.) A threshold can be set for the minimum number of observations for each value created. Comparisons with observations below this threshold will be returned as NaN.
This method is generally used for the analysis of time series data to understand the relationship between different measures across time.
Parameters:
min_periods : int, optional
Minimum number of observations required per pair of columns to have a valid result.
Returns:
DataFrame
The covariance matrix of the series of the DataFrame.
pandas.DataFrame.cov() function example program
import numpy as np
import pandas as pd
df = pd.DataFrame({'A': [5, 2,56,67], 'B': [4, 8,88,99]})
print(df.cov())
Output:
A B A 1143.000000 1709.833333 B 1709.833333 2574.916667
