facebook-ads-reports 0.9.0

ETL module for Facebook Ads API v22 with database-optimized DataFrame processing

Facebook Ads Reports Helper

A Python ETL driver for Facebook Marketing API v22 data extraction and transformation. Simplifies the process of extracting Facebook Ads data and converting it to database-ready pandas DataFrames with comprehensive optimization features.

Features

• Facebook Marketing API v22: Latest API version support with full compatibility
• Robust Error Handling: Comprehensive error handling with retry logic and specific exceptions
• Multiple Report Types: Pre-configured report models for common use cases
• Custom Reports: Create custom report configurations
• Database-Ready DataFrames: Optimized data types and encoding for seamless database storage
• Smart Type Detection: Dynamic conversion of metrics to appropriate int64/float64 types
• Configurable Missing Values: Granular control over NaN/NaT handling by column type
• Character Encoding Cleanup: Automatic text sanitization for database compatibility
• Zero Impression Filtering: Robust filtering handling multiple zero representations
• Type Hints: Full type hint support for better IDE experience

Installation

pip install facebook-ads-reports

Quick Start

1. Set up credentials

Create a secrets/fb_business_config.json file with your Facebook Ads API credentials:

{
  "app_id": "YOUR_APP_ID",
  "access_token": "YOUR_ACCESS_TOKEN",
  "base_url": "https://graph.facebook.com/v22.0"
  "ad_account_id": "act_1234567890",
}

2. Basic usage

from datetime import date, timedelta
from facebook_ads_reports import MetaAdsReport, MetaAdsReportModel, load_credentials

# Load credentials
credentials = load_credentials()
client = MetaAdsReport(credentials)

# Configure report parameters
ad_account_id = "act_1234567890"
start_date = date.today() - timedelta(days=7)
end_date = date.today() - timedelta(days=1)

# Extract report data with database optimization
df = client.get_insights_report(
        ad_account_id=ad_account_id,
        report_model=MetaAdsReportModel.ad_performance_report,
        start_date=start_date,
        end_date=end_date
)

# Save to CSV
df.to_csv("ad_performance_report.csv", index=False)

Available Report Models

• MetaAdsReportModel.ad_dimensions_report - Ad dimensions and metadata
• MetaAdsReportModel.ad_performance_report - Ad performance and actions metrics

Custom Reports

Create custom report configurations:

from facebook_ads_reports import create_custom_report

custom_report = create_custom_report(
    report_name="my_custom_report",
    select=["ad_id", "impressions", "spend"],
    from_table="ad_insights"
)

# Usage:
# df = client.get_insights_report(ad_account_id, custom_report, start_date, end_date)

Database Optimization Features

• Automatic Date Conversion: String dates → datetime64[ns]
• Dynamic Metrics Conversion: Object metrics → int64 or float64 based on data
• Preserve NULL Compatibility: NaN/NaT preserved for database NULL mapping
• Safe Conversion: Invalid values gracefully ignored
• ASCII Sanitization: Removes non-ASCII characters for database compatibility
• Length Limiting: Truncates text to 255 characters (configurable)
• Whitespace Trimming: Removes leading/trailing whitespace

Examples

Check the examples/ directory for comprehensive usage examples:

• basic_usage.py - Simple report extraction

Requirements

• Python 3.10-3.12
• pandas >= 2.0.0
• python-dotenv >= 1.0.0
• requests >= 2.32.4
• tqdm >= 4.65.0

License

MIT License. See LICENSE file for details.

Support

• Documentation
• Issues
• Examples

Contributing

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