py2adobe-reporting 0.0.20

Data Engineering tool for accessing Adobe CJA Reporting API

py2adobe_reporting

A Python wrapper for Adobe Customer Journey Analytics (CJA) Reporting API that enables analysts to easily extract and analyze data programmatically.

New in v0.0.20

JSON Extractor Tool

A visual web-based tool that extracts table and visualization metadata from Adobe CJA Analysis Workspace projects and automatically generates API request bodies.

• Import Workspace Projects – Fetch any project by ID and extract all metadata
• 10+ Visualization Types – Generates request bodies for Freeform Tables, Area Charts, Bar Charts, Key Metric Summary, and more
• Smart Source Resolution – Visualizations automatically inherit data from linked source tables
• Component Mining – Recursively extracts all segments, metrics, dimensions, and date ranges
• Modern UI – Dark theme with color-coded badges and collapsible sections

from py2adobe_reporting.cja_functions.reporting_management import Reporting

reporting = Reporting()
url = reporting.json_extractor_launch(headers)
# Browser opens to the JSON Extractor UI

# When done:
reporting.json_extractor_stop()

See JSON Extractor Documentation for full details.

Features

• Time Series Reports - Daily and monthly aggregated metrics
• Multi-Metric Analysis - Pull up to 5 metrics in a single report
• Dimension Breakdowns - Break down metrics by multiple dimensions
• Top Items Analysis - Get top 10 dimension items with optional search
• Pagination Support - Handle large datasets with automatic pagination (up to 50,000 rows per page)
• Server-to-Server Authentication - Secure OAuth 2.0 authentication

Installation

pip install py2adobe_reporting

Or install from source:

git clone https://github.com/jaytmii/py2adobe_reporting_package.git
cd py2adobe_reporting_package
pip install -e .

Requirements

• Python >= 3.8
• Adobe CJA API credentials (OAuth 2.0 Server-to-Server)

Quick Start

1. Set Up Authentication

Create a JSON configuration file with your Adobe credentials:

{
  "client_secret": "your-client-secret",
  "company_id": "your-company-id",
  "ims_host": "ims",
  "token_url": "token_url",
  "default_headers": {
    "x-api-key": "your-api-key",
    "x-gw-ims-org-id": "your-org-id"
  },
  "scopes": "api_scopes",
}

2. Authenticate

from py2adobe_reporting.auth import s2s_auth

# Authenticate and get environment object
env = s2s_auth("path/to/config.json")

3. Generate Headers

from py2adobe_reporting.auth import cja_oauth_headers

# Create headers for API calls
headers = cja_oauth_headers("path/to/config.json", env.token)

4. Pull a Report

from py2adobe_reporting.cja_functions.reporting_management import Reporting

# Initialize reporting class
reporting = Reporting()

# Get daily time series report
df = reporting.daily_time_series_report(
    headers=headers,
    data_view_id="your-data-view-id",
    start_date="2024-01-01",
    end_date="2024-01-31",
    metric="metrics/visits"
)

print(df.head())

Available Functions

Time Series Reports

• monthly_time_series_report() - Month-level aggregated metrics
• daily_time_series_report() - Day-level aggregated metrics

Multi-Dimensional Reports

• five_metrics_report() - Pull 5 metrics with a dimension
• breakdown_report() - Break down a metric by two dimensions
• get_all_rows_report() - Get all rows for custom request body

Utility Functions

• get_single_call_report() - Single API call with custom body
• get_total_table_rows() - Get row count for a table
• get_total_table_pages() - Calculate total pages needed
• get_top_ten_dimension_items() - Get top 10 items for a dimension

Documentation

For detailed function parameters and examples, see:

• Reporting Management Documentation

Example Use Cases

Get Daily Visits

df = reporting.daily_time_series_report(
    headers=headers,
    data_view_id="your-data-view-id",
    start_date="2024-01-01",
    end_date="2024-01-31",
    metric="metrics/visits"
)

Get Monthly Visits

df = reporting.monthly_time_series_report(
    headers=headers,
    data_view_id="dv_123",
    start_date="2024-01-01",
    end_date="2024-12-31",
    metric="metrics/visits"
)

Breakdown by Multiple Dimensions

df = reporting.breakdown_report(
    headers=headers,
    data_view_id="dv_123",
    start_date="2024-01-01",
    end_date="2024-01-31",
    dimension="variables/page",
    metric="metrics/visits",
    dim_rows_per_page=50,
    breakdown_dim="variables/browser",
    breakdown_rows_per_page=50
)

Search for Dimension Items

df = reporting.get_top_ten_dimension_items(
    headers=headers,
    data_view_id="dv_123",
    start_date="2024-01-01",
    end_date="2024-01-31",
    dimension="variables/page",
    search_type="contains",
    contains_term="( CONTAINS 'product' )"
)

Contributing

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

License

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

Author

James Mitchell - [e-mail]