> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

Get started with the Elementary Python SDK in minutes. This guide shows you how to define data quality tests using decorators and automatically report them to Elementary Cloud.

## Step 1: Install the SDK

```bash theme={null}
pip install elementary-python-sdk
```

## Step 2: Import Required Modules

```python theme={null}
import pandas as pd
from elementary_python_sdk.core.cloud.cloud_client import ElementaryCloudClient
from elementary_python_sdk.core.tests import (
    boolean_test,
    elementary_test_context,
    expected_range,
    expected_values,
    row_count,
)
from elementary_python_sdk.core.types.asset import TableAsset
```

## Step 3: Define Your Tests Using Decorators

The SDK provides decorators to define tests. Here are examples:

```python theme={null}
# Define a boolean test (pass/fail)
@boolean_test(
    name="unique_ids",
    description="All user IDs must be unique",
    column_name="id",
    severity="ERROR",
)
def test_unique_ids(df: pd.DataFrame) -> bool:
    ids = df["id"].dropna().tolist()
    return len(ids) == len(set(ids))

# Define a range test
@expected_range(
    name="average_age",
    min=18,
    max=50,
    description="Average age should be between 18 and 50",
    column_name="age",
    severity="ERROR",
)
def test_average_age(df: pd.DataFrame) -> float:
    return df["age"].mean()

# Define a row count test
@row_count(
    name="user_count_range",
    min=1,
    max=1000000,
    severity="WARNING",
    description="Validate user count is within expected range",
)
def test_users_row_count(df: pd.DataFrame) -> pd.DataFrame:
    """Return the DataFrame; the decorator calls len() on it."""
    return df

# Define an expected values test
@expected_values(
    name="only_valid_countries",
    expected=["Germany", "France", "Italy"],
    severity="ERROR",
    description="Should contain only valid countries",
    column_name="country",
)
def test_only_valid_countries(df: pd.DataFrame) -> pd.Series:
    return df["country"]
```

## Step 4: Create Your Data Asset

```python theme={null}
# Define the tested asset
asset = TableAsset(
    name="users",
    database_name="prod",
    schema_name="public",
    table_name="users",
    description="Users table",
    owners=["data-team"],
    tags=["pii", "production"],
    depends_on=["prod.public.customers", "prod.public.orders"]
)
```

## Step 5: Run Tests and Send Results

```python theme={null}
def main():
    # Create sample data
    users_df = pd.DataFrame(
        {
            "id": [1, 2, 3, 4, 5, 6, 7, 8],
            "age": [23, 30, 46, 76, 76, 123, 45, 32],
            "country": ["Germany", "France", "Germany", "France", "", "Italy", "France", "Germany"],
        }
    )
    
    # Use the test context to automatically capture test results
    with elementary_test_context(asset=asset) as ctx:
        # Run tests - results are automatically captured
        test_average_age(users_df)
        test_unique_ids(users_df)
        test_users_row_count(users_df)
        test_only_valid_countries(users_df)
        
        # Send results to Elementary Cloud
        PROJECT_ID = "my-python-project"  # Your Python project identifier (used to deduplicate and identify assets)
        API_KEY = "your-api-key"
        URL = "https://app.elementary-data.com/sdk-ingest/{env_id}/batch"
        
        client = ElementaryCloudClient(PROJECT_ID, API_KEY, URL)
        client.send_to_cloud(ctx)

if __name__ == "__main__":
    main()
```

## Complete Example

Here's the complete example from the [Elementary blog post](https://www.elementary-data.com/post/data-quality-in-python-pipelines):

```python theme={null}
import pandas as pd
from elementary_python_sdk.core.cloud.cloud_client import ElementaryCloudClient
from elementary_python_sdk.core.tests import (
    boolean_test,
    elementary_test_context,
    expected_range,
)
from elementary_python_sdk.core.types.asset import TableAsset

# Define "unique ids" test
@boolean_test(
    name="unique_ids",
    description="All user IDs must be unique",
    column_name="id",
)
def test_unique_ids(df: pd.DataFrame) -> bool:
    ids = df["id"].dropna().tolist()
    return len(ids) == len(set(ids))

# Define "average age" test
@expected_range(
    name="average_age",
    min=18,
    max=50,
    description="Average age should be between 18 and 50",
    column_name="age",
)
def test_average_age(df: pd.DataFrame) -> float:
    return df["age"].mean()

def main():
    # Create sample data
    users_df = pd.DataFrame(
        {
            "id": [1, 2, 3, 4, 5, 6, 7, 8],
            "age": [23, 30, 46, 76, 76, 123, 45, 32],
            "country": ["Germany", "France", "Germany", "France", "", "Italy", "France", "Germany"],
        }
    )
    
    # Define the tested asset
    asset = TableAsset(
        name="users",
        database_name="prod",
        schema_name="public",
        table_name="users",
        description="Users table",
        owners=["data-team"],
        tags=["pii", "production"],
        depends_on=["prod.public.customers", "prod.public.orders"]
    )
    
    # Run tests and report the results
    with elementary_test_context(asset=asset) as ctx:
        test_average_age(users_df)
        test_unique_ids(users_df)
        
        # Initialize client and send results
        PROJECT_ID = "my-python-project"  # Your Python project identifier (used to deduplicate and identify assets)
        API_KEY = "your-api-key"
        URL = "https://app.elementary-data.com/sdk-ingest/{env_id}/batch"
        
        client = ElementaryCloudClient(PROJECT_ID, API_KEY, URL)
        client.send_to_cloud(ctx)

if __name__ == "__main__":
    main()
```

**Note:**

* Replace `API_KEY` and `URL` with your actual credentials. The `URL` should be the full SDK ingest endpoint including your environment ID.
* `PROJECT_ID` is your Python project identifier - choose any string to identify your code project. This will appear in the metadata of assets you report and is used for deduplication.

## What Happens Next?

Once you send test results to Elementary Cloud:

* **Tests appear in the test overview** - View execution history, test queries, and configuration
* **Alerts fire automatically** - Get notified via Slack, PagerDuty, or email when tests fail
* **Incidents are created** - Automatic incident creation with Jira ticket integration
* **Lineage is connected** - Python assets link to dbt models, warehouse tables, and ML outputs
* **Assets are discoverable** - All tables, views, and data entities appear in the Elementary catalog

## What's Next?

* [API Reference](/python-sdk/api-reference/overview) - Learn about all available objects and methods
* [Test Decorators](/python-sdk/api-reference/test-decorators) - Complete reference for all test decorators
* [Table Assets](/python-sdk/api-reference/table-assets) - Learn about table asset structure