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 is a 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
This exercise is part of the course
Statistical Thinking in Python (Part 1)
Build the foundation you need to think statistically and to speak the language of your data.
In this chapter, you will compute useful summary statistics, which serve to concisely describe salient features of a dataset with a few numbers.
Exercise 1: Introduction to summary statistics: The sample mean and medianExercise 2: Means and mediansExercise 3: Computing meansExercise 4: Percentiles, outliers, and box plotsExercise 5: Computing percentilesExercise 6: Comparing percentiles to ECDFExercise 7: Box-and-whisker plotExercise 8: Variance and standard deviationExercise 9: Computing the varianceExercise 10: The standard deviation and the varianceExercise 11: Covariance and the Pearson correlation coefficientExercise 12: Scatter plotsExercise 13: Variance and covariance by lookingExercise 14: Computing the covarianceExercise 15: Computing the Pearson correlation coefficientWhat is DataCamp?
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