Implementation:Online ml River Stats Count

Knowledge Sources	Online_ml_River
Domains	Online_Learning, Statistics
Last Updated	2026-02-08 16:00 GMT

Overview

Count is a simple counter that tracks the number of observations seen in a data stream.

Description

This statistic maintains a running count of the number of observations processed. It increments by one with each update call, regardless of the value passed (the value parameter is optional). This is one of the most fundamental statistics used in online learning for tracking sample size.

Usage

Use Count when you need to track how many observations have been processed in a stream. This is essential for calculating other statistics that depend on sample size, monitoring data throughput, or implementing sampling strategies based on observation counts.

Code Reference

Source Location

• Repository: Online_ml_River
• File: river/stats/count.py

Signature

class Count(stats.base.Univariate):
    def __init__(self):
        self.n = 0

Import

from river import stats

I/O Contract

Inputs

Name	Type	Required	Description
x	Any	No	Value to update with (ignored, can be None)

Outputs

Name	Type	Description
get()	int	Current count of observations

Usage Examples

from river import stats

# Create a counter
count = stats.Count()

# Update with values (values are optional)
data = [1, 2, 3, 4, 5]
for x in data:
    count.update(x)
    print(f"Processed {count.get()} observations")

# Output:
# Processed 1 observations
# Processed 2 observations
# Processed 3 observations
# Processed 4 observations
# Processed 5 observations

# Can also update without values
count_simple = stats.Count()
for _ in range(10):
    count_simple.update()
print(f"Total count: {count_simple.get()}")
# Output: Total count: 10

Related Pages

• Environment:Online_ml_River_Python_Runtime_Environment