Evidently OSS Quickstart

Run your first evaluation using Evidently open-source, for tabular data.

You can launch this hello-world example in Jupyter notebook, Colab or other Python environment.

Installation

MAC OS and Linux, Jupyter notebook

Install Evidently using the pip package manager:

$ pip install evidently

Colab

Install Evidently:

!pip install evidently

Imports

Import the Evidently components and a toy “Iris” dataset:

import pandas as pd

from sklearn import datasets

from evidently.test_suite import TestSuite
from evidently.test_preset import DataStabilityTestPreset

from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

Run a Test Suite

Split the data into two batches. Run a set of pre-built data quality Tests to compare them:

data_stability= TestSuite(tests=[
    DataStabilityTestPreset(),
])
data_stability.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_stability 

This will automatically generate tests on share of nulls, out-of-range values, etc. – with test conditions generated based on the first "reference" dataset.

Get a Report

Get a Data Drift Report to see if the data distributions shifted between two datasets:

data_drift_report = Report(metrics=[
    DataDriftPreset(),
])

data_drift_report.run(current_data=iris_frame.iloc[:60], reference_data=iris_frame.iloc[60:], column_mapping=None)
data_drift_report

Want to see more?

• Take the complete Report & Test Suite Tutorial to learn how to run checks like this in detail (15 minutes). You can also evaluate ML model quality, e.g., for classification, regression, and ranking models, and work with text data.

• Start with ML monitoring. Go through the Evidently Cloud Quickstart (2 min) to get a dashboard to track metrics over time.

• Working with LLMs? See an LLM Evaluation Quicktart to see how to run checks for text data.