google-ads-reports 1.2.1

ETL module for Google Ads API v20 with database-optimized DataFrame processing

Google Ads Reports Helper

A Python ETL driver for Google Ads API v20 data extraction and transformation. Simplifies the process of extracting Google Ads data and converting it to database-ready pandas DataFrames with comprehensive optimization features.

Features

• Google Ads API v20: Latest API version support with full compatibility
• Database-Ready DataFrames: Optimized data types and encoding for seamless database storage
• Flexible Column Naming: Choose between snake_case or camelCase column conventions
• 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
• Multiple Report Types: Pre-configured report models for common use cases
• Custom Reports: Create custom report configurations with full GAQL support
• Robust Error Handling: Comprehensive error handling with retry logic and specific exceptions
• Pagination Support: Automatic handling of large datasets with pagination
• Type Hints: Full type hint support for better IDE experience

Installation

pip install google-ads-reports

Quick Start

1. Set up credentials

Create a secrets/google-ads.yaml file with your Google Ads API credentials:

developer_token: "YOUR_DEVELOPER_TOKEN"
client_id: "YOUR_CLIENT_ID"
client_secret: "YOUR_CLIENT_SECRET"
refresh_token: "YOUR_REFRESH_TOKEN"

References:
https://developers.google.com/google-ads/api/docs/get-started/introduction https://developers.google.com/google-ads/api/docs/get-started/dev-token https://developers.google.com/workspace/guides/create-credentials#service-account

2. Basic usage

from datetime import date, timedelta
from google_ads_reports import GAdsReport, GAdsReportModel, load_credentials

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

# Configure report parameters
customer_id = "1234567890"
start_date = date.today() - timedelta(days=7)
end_date = date.today() - timedelta(days=1)

# Extract report data with database optimization
df = client.get_gads_report(
    customer_id=customer_id,
    report_model=GAdsReportModel.keyword_report,
    start_date=start_date,
    end_date=end_date,
    filter_zero_impressions=True,  # Remove rows with zero impressions
    column_naming="snake_case"     # Choose column naming: "snake_case" or "camelCase"
)

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

Column Naming Options

Choose between snake_case (database-friendly) or camelCase (API-consistent) column names:

# Snake case (default) - metrics.impressions → impressions
df_snake = client.get_gads_report(
    customer_id=customer_id,
    report_model=GAdsReportModel.keyword_report,
    start_date=start_date,
    end_date=end_date,
    column_naming="snake_case"  # Default
)

# CamelCase - metrics.impressions → metricsImpressions  
df_camel = client.get_gads_report(
    customer_id=customer_id,
    report_model=GAdsReportModel.keyword_report,
    start_date=start_date,
    end_date=end_date,
    column_naming="camelCase"
)

Available Report Models

• GAdsReportModel.adgroup_ad_report - Ad group ad performance
• GAdsReportModel.keyword_report - Keyword performance
• GAdsReportModel.search_terms_report - Search terms analysis
• GAdsReportModel.conversions_report - Conversion tracking
• GAdsReportModel.video_report - Video ad performance
• GAdsReportModel.assetgroup_report - Asset group performance

Custom Reports

Create custom report configurations:

from google_ads_reports import create_custom_report

custom_report = create_custom_report(
    report_name="campaign_performance",
    select=[
        "campaign.name",
        "campaign.status", 
        "segments.date",
        "metrics.impressions",
        "metrics.clicks",
        "metrics.cost_micros"
    ],
    from_table="campaign",
    where="metrics.impressions > 100"
)

df = client.get_gads_report(customer_id, custom_report, start_date, end_date)

Database Optimization Features

The package automatically optimizes DataFrames for database storage:

Data Type Optimization

• Automatic Date Conversion: String dates → datetime64[ns]
• Dynamic Metrics Conversion: Object metrics → int64 or float64 based on data
• Smart Integer Detection: Whole numbers become int64, decimals become float64

Missing Value Handling

• Preserve NULL Compatibility: NaN/NaT preserved for database NULL mapping
• Configurable by Type: Different strategies for numeric, datetime, and text columns
• Safe Conversion: Invalid values gracefully ignored

Character Encoding Cleanup

• ASCII Sanitization: Removes non-ASCII characters for database compatibility
• Null Byte Removal: Strips problematic null bytes (\x00)
• Length Limiting: Truncates text to 255 characters (configurable)
• Whitespace Trimming: Removes leading/trailing whitespace

Zero Impression Filtering

Handles multiple zero representations:

df = client.get_gads_report(
    customer_id=customer_id,
    report_model=report_model,
    start_date=start_date,
    end_date=end_date,
    filter_zero_impressions=True  # Removes: 0, "0", 0.0, "0.0", None, NaN
)

Flexible Column Naming

Choose your preferred column naming convention:

Snake Case (Default - Database Friendly):

• metrics.impressions → impressions
• segments.date → date
• adGroupCriterion.keyword → keyword

CamelCase (API Consistent):

• metrics.impressions → metricsImpressions
• segments.date → segmentsDate
• adGroupCriterion.keyword → adGroupCriterionKeyword

# Choose naming convention
df = client.get_gads_report(
    customer_id=customer_id,
    report_model=report_model,
    start_date=start_date,
    end_date=end_date,
    column_naming="snake_case"  # or "camelCase"
)

Error Handling

The package provides specific exception types for different scenarios:

from google_ads_reports import (
    GAdsReport, 
    AuthenticationError, 
    ValidationError, 
    APIError,
    DataProcessingError,
    ConfigurationError
)

try:
    df = client.get_gads_report(customer_id, report_model, start_date, end_date)
except AuthenticationError:
    # Handle credential issues
    pass
except ValidationError:
    # Handle input validation errors
    pass
except APIError:
    # Handle API errors (after retries)
    pass

Examples

Check the examples/ directory for comprehensive usage examples:

• basic_usage.py - Simple report extraction
• multiple_reports.py - Batch report processing
• custom_reports.py - Custom report creation
• error_handling.py - Error handling patterns

Configuration

Retry Settings

API calls automatically retry on transient errors with configurable settings:

• Max attempts: 3 (default)
• Base delay: 1 second
• Backoff factor: 2x exponential
• Max delay: 30 seconds

Logging

Configure logging level:

from google_ads_reports import setup_logging
import logging

setup_logging(level=logging.DEBUG)  # Enable debug logging

Requirements

• Python 3.9-3.12
• google-ads >= 24.0.0 (Google Ads API v20 support)
• pandas >= 2.0.0
• PyYAML >= 6.0.0
• python-dotenv >= 1.0.0
• tqdm >= 4.65.0

Development

For development installation:

git clone https://github.com/machado000/google-ads-reports
cd google-ads-reports
pip install -e ".[dev]"

License

MIT License. See LICENSE file for details.

Support

• Documentation
• Issues
• Examples

Contributing

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