NUnique

Approximate number of unique values counter.

This is basically an implementation of the HyperLogLog algorithm. Adapted from hypy. The code is a bit too terse but it will do for now.

Parameters

• error_rate

  Default → 0.01

  Desired error rate. Memory usage is inversely proportional to this value.

• seed

  Type → int | None

  Default → None

  Set the seed to produce identical results.

Attributes

• n_bits (int)

• n_buckets (int)

• buckets (list)

Examples

import string
from river import stats

alphabet = string.ascii_lowercase
n_unique = stats.NUnique(error_rate=0.2, seed=42)

n_unique.update('a').get()

1

n_unique.update('b').get()

2

for letter in alphabet:
    n_unique = n_unique.update(letter)
n_unique.get()

31

Lowering the error_rate parameter will increase the precision.

n_unique = stats.NUnique(error_rate=0.01, seed=42)
for letter in alphabet:
    n_unique = n_unique.update(letter)
n_unique.get()

26

Methods

get

Return the current value of the statistic.

update

Update and return the called instance.

Parameters

• x — 'numbers.Number'

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1. My favorite algorithm (and data structure): HyperLogLog ↩

2. Flajolet, P., Fusy, É., Gandouet, O. and Meunier, F., 2007, June. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. ↩