Python implementation of an online parallel statistics calculator. This library will calculate the total, mean, variance, standard deviation, skewness and kurtosis. There are additional options for calculating covariance and correlation between two sequences of data points.
Online calculation is appropriate when you don't yet have the entire dataset in order to calculate the mean (e.g. in a streaming environment). It is more processor-intensive than the traditional methods however.
When combined with parallel computation, it can also be useful when the data is very large as it works in a single pass and can be distributed.
pip install opstats
For calculating the mean, variance (and standard deviation), skewness and kurtosis, use the MomentCalculator.
import random
from opstats import MomentCalculator
data_points = random.sample(range(1, 100), 20)
calc = MomentCalculator()
for d in data_points:
calc.add(d)
result = calc.get()
The result will be a NamedTuple containing the computed moments up until this point. More data can subsequently be added and the result can be retrieved again.
Data can be split into multiple parts and processed in parallel. The resulting statistics can be combined using the aggregate_moments function.
from opstats import aggregate_moments
# Divide the sample data in half.
left_data = data_points[:len(data_points)//2]
right_data = data_points[len(data_points)//2:]
# Create stats for each half.
left = MomentCalculator()
for d in left_data:
left.add(d)
right = MomentCalculator()
for d in right_data:
right.add(d)
# Combine the results.
result = aggregate_moments([left.get(), right.get()])
The CovarianceCalculator class and aggregate_covariance function work in the same manner as above for calculating the covariance and correlation between two sequences of data points.
When installed with pip install opstats[extended], cardinality and percentiles can also be estimated. Cardinality will be estimated with HyperLogLog and percentiles with T-Digest.
from opstats.extended import ExtendedCalculator
calc = ExtendedCalculator()
for d in data_points:
calc.add(d)
result = calc.get()
This can also be calculated in parallel. Note the changes to using get_parallel() which returns an intermediate object and calculate() which computes the final values.
from opstats.extended import aggregate_extended
# Divide the sample data in half.
left_data = data_points[:len(data_points)//2]
right_data = data_points[len(data_points)//2:]
# Create stats for each half.
left = ExtendedCalculator()
for d in left_data:
left.add(d)
right = ExtendedCalculator()
for d in right_data:
right.add(d)
# Combine the results.
result = aggregate_extended([left.get_parallel(), right.get_parallel()]).calculate()
Online calculator adapted from: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance (Terriberry, Timothy B)
Aggregation translated from: https://rdrr.io/cran/utilities/src/R/sample.decomp.R
Python HyperLogLog implementation: https://github.com/svpcom/hyperloglog
Python T-Digest implementation: https://github.com/CamDavidsonPilon/tdigest