# Hello World Example

You can launch this hello-world example in Jupyter notebook or Colab.

## Installation

### MAC OS and Linux, Jupyter notebook

Install **Evidently** using the pip package manager:

```bash
$ pip install evidently
```

Install and enable Jupyter **nbextension**. Run the two following commands in the terminal from the Evidently directory:

```
$ jupyter nbextension install --sys-prefix --symlink --overwrite --py evidently
$ jupyter nbextension enable evidently --py --sys-prefix
```

### Colab

Install **Evidently**:

```python
!pip install evidently
```

## Imports

Import toy data and required Evidently components:

```python
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 toy data into two batches and compare them:

```python
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 
```

## Get a report

Get a visual report to explore the feature distribution drift in detail:

```python
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?

You can explore a more detailed [Getting Started tutorial](https://francesco.gitbook.io/docs.evidentlyai.com/get-started/tutorial).