In this section, we're going to take a look at some of the tools we have for descriptive statistics. Some of these are built into the ndarray crate that we're already familiar with, but some of them require another crate, ndarray-stats . This crate provides more advanced statistical methods for the array data structures provided by ndarray .

The currently available methods include:

Order statistics (minimum, maximum, median, quantiles, etc.);

(minimum, maximum, median, quantiles, etc.); Summary statistics (mean, skewness, kurtosis, central moments, etc.)

(mean, skewness, kurtosis, central moments, etc.) Partitioning ;

; Correlation analysis (covariance, pearson correlation);

(covariance, pearson correlation); Measures from information theory (entropy, KL divergence, etc.);

(entropy, KL divergence, etc.); Measures of deviation (count equal, L1, L2 distances, mean squared err etc.);

(count equal, L1, L2 distances, mean squared err etc.); Histogram computation.

For now, we'll focus on the first few methods we would normally use when interrogating a numerical dataset, e.g. central tendency and variance.