Computing the covariance
The covariance may be computed using the Numpy function np.cov(). For example, we have two sets of data x and y, np.cov(x, y) returns a 2D array where entries [0,1] and [1,0] are the covariances. Entry [0,0] is the variance of the data in x, and entry [1,1] is the variance of the data in y. This 2D output array is called the covariance matrix, since it organizes the self- and covariance.
To remind you how the I. versicolor petal length and width are related, we include the scatter plot you generated in a previous exercise.
This exercise is part of the course
Statistical Thinking in Python (Part 1)
Exercise instructions
- Use
np.cov()to compute the covariance matrix for the petal length (versicolor_petal_length) and width (versicolor_petal_width) of I. versicolor. - Print the covariance matrix.
- Extract the covariance from entry
[0,1]of the covariance matrix. Note that by symmetry, entry[1,0]is the same as entry[0,1]. - Print the covariance.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Compute the covariance matrix: covariance_matrix
# Print covariance matrix
# Extract covariance of length and width of petals: petal_cov
# Print the length/width covariance