stats

stats

stats provides a Starlark module for comprehensive statistics functions. It is a wrapper around the Go package: https://github.com/montanaflynn/stats

Functions

euclidean_distance(data1, data2) float

Calculates the straight line distance between two points in Euclidean space.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate Euclidean distance between two points.

load("stats", "euclidean_distance")
print(euclidean_distance([3, 4], [0, 0]))  # Output: 5.0

manhattan_distance(data1, data2) float

Computes the sum of the absolute differences of their coordinates.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate Manhattan distance between two points.

load("stats", "manhattan_distance")
print(manhattan_distance([3, 4], [0, 0]))  # Output: 7.0

softmax(data) list

Applies the Softmax function, useful for converting scores to probabilities.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Apply Softmax function to a dataset.

load("stats", "softmax")
print(softmax([1, 1, 1]))  # Output: [0.3333333333333333, 0.3333333333333333, 0.3333333333333333]

sigmoid(data) list

Applies the Sigmoid function, often used for binary classification.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Apply Sigmoid function to a dataset.

load("stats", "sigmoid")
print(sigmoid([0, 2, 4]))  # Output: [0.5, 0.8807970779778823, 0.9820137900379085]

mode(data) list

Determines the most frequently occurring data points in a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate mode of a dataset.

load("stats", "mode")
print(mode([1, 2, 2, 3, 4]))  # Output: [2.0]

sum(data) float

Computes the sum of a series of numbers.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate sum of a dataset.

load("stats", "sum")
print(sum([1, 2, 3, 4]))  # Output: 10.0

max(data) float

Finds the maximum value in a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Find maximum value in a dataset.

load("stats", "max")
print(max([1, 2, 3, 4]))  # Output: 4.0

min(data) float

Finds the minimum value in a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Find minimum value in a dataset.

load("stats", "min")
print(min([1, 2, 3, 4]))  # Output: 1.0

midrange(data) float

Calculates the midrange, the average of the maximum and minimum values.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate midrange of a dataset.

load("stats", "midrange")
print(midrange([1, 2, 3, 4]))  # Output: 2.5

average(data) float

Alias for mean. Calculates the arithmetic mean of a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate average of a dataset.

load("stats", "average")
print(average([1, 2, 2, 3]))  # Output: 2.0

mean(data) float

Calculates the arithmetic mean of a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate mean of a dataset.

load("stats", "mean")
print(mean([1, 2, 3, 4]))  # Output: 2.5

geometric_mean(data) float

Computes the geometric mean, useful for datasets with exponential growth.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate geometric mean of a dataset.

load("stats", "geometric_mean")
print(geometric_mean([1, 2, 3, 4]))  # Output: 2.213363839400643

harmonic_mean(data) float

Computes the harmonic mean, effective for rates and ratios.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate harmonic mean of a dataset.

load("stats", "harmonic_mean")
print(harmonic_mean([1, 2, 3, 6]))  # Output: 2.0

trimean(data) float

Calculates the trimean, a measure of a dataset's tendency.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate trimean of a dataset.

load("stats", "trimean")
print(trimean([1, 2, 3, 4, 5]))  # Output: 3.0

median(data) float

Finds the middle value of a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate median of a dataset.

load("stats", "median")
print(median([1, 2, 3, 4, 5]))  # Output: 3.0

percentile(data, p) float

Determines the value below which a given percentage of observations in a group of observations falls.

Parameters

name	type	description
data	list	Dataset of numerical values.
p	float	The percentile to compute.

Examples

basic

Calculate 50th percentile of a dataset.

load("stats", "percentile")
print(percentile([1, 2, 3, 4, 5], 50))  # Output: 2.5

percentile_nearest_rank(data, p) float

Determines the value below which a given percentage of observations in a group of observations falls using the nearest rank method.

Parameters

name	type	description
data	list	Dataset of numerical values.
p	float	The percentile to compute.

Examples

basic

Calculate 50th percentile using nearest rank method.

load("stats", "percentile_nearest_rank")
print(percentile_nearest_rank([1, 2, 3, 4, 5], 50))  # Output: 3.0

variance(data) float

Calculates the variance of a dataset, a measure of dispersion.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate variance of a dataset.

load("stats", "variance")
print(variance([1, 2, 3, 4, 5]))  # Output: 2.0

covariance(data1, data2) float

Measures how changes in one variable are associated with changes in another variable.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate covariance between two datasets.

load("stats", "covariance")
print(covariance([1, 2, 3], [4, 5, 6]))  # Output: 1.0

covariance_population(data1, data2) float

Calculates the covariance for an entire population.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate population covariance between two datasets.

load("stats", "covariance_population")
print(covariance_population([1, 2, 3], [4, 5, 6]))  # Output: 1.0

population_variance(data) float

Computes the variance of an entire population.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate population variance of a dataset.

load("stats", "population_variance")
print(population_variance([1, 2, 3, 4, 5]))  # Output: 2.0

sample_variance(data) float

Computes the variance of a sample from the population.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate sample variance of a dataset.

load("stats", "sample_variance")
print(sample_variance([1, 2, 3, 4, 5]))  # Output: 2.5

correlation(data1, data2) float

Computes the correlation coefficient between two datasets.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate correlation between two datasets.

load("stats", "correlation")
print(correlation([1, 2, 3], [1, 2, 3]))  # Output: 1.0

pearson(data1, data2) float

Computes Pearson's correlation coefficient.

Parameters

name	type	description
data1	list	First dataset of numerical values.
data2	list	Second dataset of numerical values.

Examples

basic

Calculate Pearson's correlation coefficient.

load("stats", "pearson")
print(pearson([1, 2, 3], [1, 2, 3]))  # Output: 1.0

standard_deviation(data) float

Calculates the standard deviation of a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate standard deviation of a dataset.

load("stats", "standard_deviation")
print(standard_deviation([1, 2, 3, 4, 5]))  # Output: 1.4142135623730951

stddev(data) float

Alias for standard_deviation. Calculates the standard deviation of a dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate stddev of a dataset.

load("stats", "stddev")
print(stddev([1, 2, 3, 4, 5]))  # Output: 1.4142135623730951

stddev_sample(data) float

Calculates the standard deviation of a sample.

Parameters

name	type	description
data	list	Dataset of numerical values.

Examples

basic

Calculate standard deviation of a sample.

load("stats", "stddev_sample")
print(stddev_sample([1, 2, 3, 4, 5]))  # Output: 1.5811388300841898

sample(data, take, replace=False) list

Randomly samples elements from the dataset.

Parameters

name	type	description
data	list	Dataset of numerical values.
take	int	Number of elements to sample.
replace	bool	Optional. If True, sampling is done with replacement.

Examples

basic

Sample elements from a dataset.

load("stats", "sample")
print(sample(data=[1, 2, 3, 4], take=3, replace=False))