Implementation:Evidentlyai Evidently DriftedColumnsCount MissingValueCount
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| Knowledge Sources | |
|---|---|
| Domains | ML_Monitoring, Data_Quality |
| Last Updated | 2026-02-14 12:00 GMT |
Overview
Concrete metric classes for dataset-level drift counting and missing value detection provided by the Evidently library.
Description
- DriftedColumnsCount: Tests all specified columns for drift and returns the count and share of drifted columns
- MissingValueCount: Counts missing (null/NaN) values in a specific column, returning count and share
Usage
Include in Report metrics lists for drift monitoring workflows.
Code Reference
Source Location
- Repository: evidently
- File: src/evidently/metrics/column_statistics.py
- Lines: L492-524 (MissingValueCount), L745-783 (DriftedColumnsCount via dataset_statistics re-export)
Signature
class DriftedColumnsCount(Metric):
columns: Optional[List[str]] = None
drift_share: float = 0.5
method: Optional[str] = None
class MissingValueCount(ColumnMetric, CountMetric):
# column: str (inherited from ColumnMetric)
Import
from evidently.metrics import MissingValueCount
from evidently.metrics.dataset_statistics import DriftedColumnsCount
I/O Contract
Inputs
| Metric | Key Parameters | Description |
|---|---|---|
| DriftedColumnsCount | columns: Optional[List[str]], drift_share: float, method: Optional[str] | Columns to check, share threshold, drift method |
| MissingValueCount | column: str | Column to check for missing values |
Outputs
| Metric | Returns | Description |
|---|---|---|
| DriftedColumnsCount | (count: int, share: float) | Number and share of drifted columns |
| MissingValueCount | (count: int, share: float) | Number and share of missing values |
Usage Examples
from evidently import Report
from evidently.metrics import ValueDrift, MissingValueCount
from evidently.metrics.dataset_statistics import DriftedColumnsCount
report = Report([
ValueDrift(column="age"),
ValueDrift(column="salary"),
ValueDrift(column="department"),
DriftedColumnsCount(), # Summary: how many columns drifted
MissingValueCount(column="age"),
MissingValueCount(column="salary"),
])
snapshot = report.run(current_dataset, reference_dataset)
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