apple-search-ads-client 1.0.7

A Python client for Apple Search Ads API v5

Apple Search Ads Python Client

A Python client library for Apple Search Ads API v5, providing a simple and intuitive interface for managing and reporting on Apple Search Ads campaigns.

Features

• 🔐 OAuth2 authentication with JWT
• 📊 Campaign performance reporting
• 🏢 Multi-organization support
• 💰 Spend tracking by app
• ⚡ Built-in rate limiting
• 🐼 Pandas DataFrames for easy data manipulation
• 🔄 Automatic token refresh
• 🎯 Type hints for better IDE support
• ✅ 100% test coverage

Installation

pip install apple-search-ads-client

Quick Start

from apple_search_ads import AppleSearchAdsClient

# Initialize the client
client = AppleSearchAdsClient(
    client_id="your_client_id",
    team_id="your_team_id",
    key_id="your_key_id",
    private_key_path="/path/to/private_key.p8"
)

# Get all campaigns
campaigns = client.get_campaigns()

# Get daily spend for the last 30 days
spend_df = client.get_daily_spend(days=30)
print(spend_df)

Authentication

Prerequisites

1. An Apple Search Ads account with API access
2. API credentials from the Apple Search Ads UI:
   • Client ID
   • Team ID
   • Key ID
   • Private key file (.p8)

Setting up credentials

You can provide credentials in three ways:

1. Direct parameters (recommended)

client = AppleSearchAdsClient(
    client_id="your_client_id",
    team_id="your_team_id",
    key_id="your_key_id",
    private_key_path="/path/to/private_key.p8"
)

2. Environment variables

export APPLE_SEARCH_ADS_CLIENT_ID="your_client_id"
export APPLE_SEARCH_ADS_TEAM_ID="your_team_id"
export APPLE_SEARCH_ADS_KEY_ID="your_key_id"
export APPLE_SEARCH_ADS_PRIVATE_KEY_PATH="/path/to/private_key.p8"

client = AppleSearchAdsClient()  # Will use environment variables

3. Private key content

# Useful for environments where file access is limited
with open("private_key.p8", "r") as f:
    private_key_content = f.read()

client = AppleSearchAdsClient(
    client_id="your_client_id",
    team_id="your_team_id",
    key_id="your_key_id",
    private_key_content=private_key_content
)

Usage Examples

Get all organizations

# List all organizations you have access to
orgs = client.get_all_organizations()
for org in orgs:
    print(f"{org['orgName']} - {org['orgId']}")

Get campaign performance report

from datetime import datetime, timedelta

# Get campaign performance for the last 7 days
end_date = datetime.now()
start_date = end_date - timedelta(days=7)

report_df = client.get_campaign_report(
    start_date=start_date,
    end_date=end_date,
    granularity="DAILY"  # Options: DAILY, WEEKLY, MONTHLY
)

# Display key metrics
print(report_df[['date', 'campaign_name', 'spend', 'installs', 'taps']])

Track spend by app

# Get daily spend grouped by app
app_spend_df = client.get_daily_spend_by_app(
    start_date="2024-01-01",
    end_date="2024-01-31",
    fetch_all_orgs=True  # Fetch from all organizations
)

# Group by app and sum
app_totals = app_spend_df.groupby('app_id').agg({
    'spend': 'sum',
    'installs': 'sum',
    'impressions': 'sum'
}).round(2)

print(app_totals)

Get campaigns from all organizations

# Fetch campaigns across all organizations
all_campaigns = client.get_all_campaigns()

# Filter active campaigns
active_campaigns = [c for c in all_campaigns if c['status'] == 'ENABLED']

print(f"Found {len(active_campaigns)} active campaigns across all orgs")

Working with specific organization

# Get campaigns for a specific org
org_id = "123456"
campaigns = client.get_campaigns(org_id=org_id)

# The client will use this org for subsequent requests

Working with ad groups

# Get ad groups for a campaign
campaign_id = "1234567890"
adgroups = client.get_adgroups(campaign_id)

for adgroup in adgroups:
    print(f"Ad Group: {adgroup['name']} (Status: {adgroup['status']})")

API Reference

Client initialization

AppleSearchAdsClient(
    client_id: Optional[str] = None,
    team_id: Optional[str] = None,
    key_id: Optional[str] = None,
    private_key_path: Optional[str] = None,
    private_key_content: Optional[str] = None,
    org_id: Optional[str] = None
)

Methods

Organizations

• get_all_organizations() - Get all organizations
• get_campaigns(org_id: Optional[str] = None) - Get campaigns for an organization
• get_all_campaigns() - Get campaigns from all organizations

Reporting

• get_campaign_report(start_date, end_date, granularity="DAILY") - Get campaign performance report
• get_daily_spend(days=30, fetch_all_orgs=True) - Get daily spend for the last N days
• get_daily_spend_with_dates(start_date, end_date, fetch_all_orgs=True) - Get daily spend for date range
• get_daily_spend_by_app(start_date, end_date, fetch_all_orgs=True) - Get spend grouped by app

Campaign Management

• get_campaigns_with_details(fetch_all_orgs=True) - Get campaigns with app details
• get_adgroups(campaign_id) - Get ad groups for a specific campaign

DataFrame Output

All reporting methods return pandas DataFrames for easy data manipulation:

# Example: Calculate weekly totals
daily_spend = client.get_daily_spend(days=30)
daily_spend['week'] = pd.to_datetime(daily_spend['date']).dt.isocalendar().week
weekly_totals = daily_spend.groupby('week')['spend'].sum()

Rate Limiting

The client includes built-in rate limiting to respect Apple's API limits (10 requests per second). You don't need to implement any additional rate limiting.

Error Handling

from apple_search_ads.exceptions import (
    AuthenticationError,
    RateLimitError,
    OrganizationNotFoundError
)

try:
    campaigns = client.get_campaigns()
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except RateLimitError as e:
    print(f"Rate limit exceeded: {e}")
except Exception as e:
    print(f"An error occurred: {e}")

Best Practices

1. Reuse client instances: Create one client and reuse it for multiple requests
2. Use date ranges wisely: Large date ranges may result in slower responses
3. Cache organization IDs: If working with specific orgs frequently, cache their IDs
4. Monitor rate limits: Although built-in rate limiting is included, be mindful of your usage
5. Use DataFrame operations: Leverage pandas for data aggregation and analysis

Requirements

• Python 3.8 or higher
• See requirements.txt for package dependencies

Testing

This project maintains 100% test coverage. The test suite includes:

• Unit tests with mocked API responses
• Exception handling tests
• Edge case coverage
• Legacy API format compatibility tests
• Comprehensive integration tests

Running Tests

# Run all tests with coverage report
pytest tests -v --cov=apple_search_ads --cov-report=term-missing

# Run tests in parallel for faster execution
pytest tests -n auto

# Generate HTML coverage report
pytest tests --cov=apple_search_ads --cov-report=html

# Run integration tests (requires credentials)
pytest tests/test_integration.py -v

For detailed testing documentation, see TESTING.md.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• 🐛 Issues: GitHub Issues
• 📖 Documentation: Read the Docs

Changelog

See CHANGELOG.md for a list of changes.

Acknowledgments

• Apple for providing the Search Ads API
• The Python community for excellent libraries used in this project