# Tutorial

This Python client communicates with the Avatar platform.

For more information about the Avatar method and process, check out our main docs at <https://docs.octopize.io>

## Step 1: Setup and Authentication

First, import the necessary libraries and set up your authentication.

```python
import os
from avatars.manager import Manager

# Initialize the Manager with an API key
manager = Manager(api_key=os.environ.get("AVATAR_API_KEY"))
```

Make sure to configure your API key in your environment variables.

## Step 2: Uploading Your Data

Once authenticated, you can upload your data table to the server. In this example, we’ll use a CSV file `wbcd.csv`.

```python
# Initialize the runner
runner = manager.create_runner("test_wbcd")

# Add the table to the runner
runner.add_table("wbcd", "fixtures/wbcd.csv")
```

Here, the wbcd dataset is uploaded and is now ready for anonymization.

## Step 3: Avatarization Parameters and Execution

Next, set the **avatarization parameters** and run the avatarization process:

```python
# Set anonymization parameters
runner.set_parameters("wbcd", k=10)  # Adjust k value for privacy level

# Run the anonymization pipeline
runner.run()

# Get all results after running the anonymization
results = runner.get_all_results()
```

Here, `k=10` is the number of nearest neighbors used in the [KNN-based anonymization](https://docs.octopize.io/docs/principles/method/tabular/)). You can adjust this value based on your desired privacy level.
If you want to know more about anonymization parameters, please refer [to the user guide](https://docs.octopize.io/docs/user_guide/step_by_step/run/)

## Step 4: Retrieve Avatarized Data

Once the anonymization is complete, you can retrieve and inspect the avatarized data:

```python
# Print a preview of the avatarized data
print("Avatar data:")
print(runner.shuffled("wbcd").head())
```

## Step 5: Retrieve Privacy and Signal Metrics

Octopize provides privacy and signal metrics to evaluate the quality of the synthetic data produced:

```python
# Print privacy metrics
for key, value in runner.privacy_metrics("wbcd")[0].items():
    print(f"{key}: {value}")

# Print signal metrics
for key, value in runner.signal_metrics("wbcd")[0].items():
    print(f"{key}: {value}")
```

## Step 6: Download Avatarization Report

Once you found the optimal set of parameters for your use case, you can download a comprehensive report of the anonymization process.
This report compiles the privacy and utility metrics obtained, providing evidence of both anonymity and the preservation of statistical properties.

```python
# Download the anonymization report as a PDF
runner.download_report('my_report.pdf')
```

Once you've followed these steps, you're ready to explore further and fine-tune your anonymization process!

## Further Resources

You can explore more features of the Avatar solution by following our [notebook tutorials](https://python.docs.octopize.io/latest/tutorial.html).