This is a comprehensive guide on using the GO API to seamlessly integrate and analyze the data in Power BI/ Excel and Python. This documentation serves as a step by step manual, empowering the users to establish a robust connection with the GO API and unlock valuable insights from the GO disaster information. This API makes the disaster information universally accessible and useful to IFRC responders for data driven decision making.
- Connection Setup: The documentation provides clear and concise instructions to ensure a smooth integration process, enabling seamless data retrieval for further analysis in PowerBI/ Excel and python.
- Available Data: Explore a rich variety of data accessible through the GO API. While API boasts of numerous endpoints, our documentation provides examples and insights for a select few, namely Project, Event, Appeal and Surge Alerts. it offers a glimpse into the breath and depth of information coming from the GO disaster information data.
- Power BI Integration: The documentation provides examples and walkthroughs that will guide you in transforming raw data from the GO API into visually compelling reports and dashboards.
- Python Integration: The documentation provides with Python code snippets and examples to effortlessly retrieve, manipulate and analyze the data from the GO API endpoints, providing you with flexibility to customize your data processing pipeline.
- Data Analysis and visualization examples: We also provide illustrative examples, showcasing how to perform essential analytics and visualization tasks on the retrieved data, enabling you to derive actionable insights.
- Pagination Handling: The GO API data is organized in Paginated endpoints. Our documentation provides insights into efficiently managing large datasets, ensuring a smooth and efficient data retrieval process.
- Data Dictionary: We also provide data dictionaries of the major API endpoints which is a key resource that provides detailed information about variables and their descriptions for major API endpoints. It enables a thorough understanding of the data structure, facilitation efficient analysis and interpretation.
- Generating Authentication Token: Users can generate an API authentication token by visiting the “Generating an API token” section. This fundamental step ensures a secure access to the GO API. Users can follow the detailed instructions to acquire the token and establish a secure connection to the GO API.
- Handling Pagination: Once users have generated the authentication token, they should explore the “Pagination Handling” section to understand the techniques for efficiently navigating through the paginated API data. This section also provides information on how users can also use parameters to filter out the data from the API endpoints.
- Multilingual Support: Users can retrieve data in supported languages namely English, Spanish, Arabic and French. refer to the “Get API response in a supported language” section to understand how to receive responses in the above mentioned languages. This feature enhances accessibility and usability for a diverse user base.
- Power BI and Python Integration: Users can explore the Sample API Queries section to establish connection with the GO API endpoints in Power BI and Python. Every example under this section provides a detailed explanation of the API endpoint used as well as the data retrieved. This section provides with Power Query m code which can be used inside Power Query editor to retrieve data from Go API endpoints. This code handles the API connection along with pagination and use of parameters to filter out the data if required. Python code snippets are also provided for establishing connection with the API and data retrieval along with pagination handling and data filtering using parameters.
- Data Analysis Examples: The Sample API Queries section also provides with some data analysis on the data retrieved in Power BI and python. Users can use the GO API - Data Dictionary section to perform essential analytics tasks, enabling them to derive actionable insights for decision-making.