📅 2023-11-07 — Session: Structured Categorization of Economic Data Distributions
🕒 04:10–05:00
🏷️ Labels: Economic Data, Categorization, Python, Dataframe, Macroeconomics
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop a structured categorization framework for economic data distributions, focusing on organizing and analyzing economic indicators from the Ministry of Economy.
Key Activities
- Categorización de Temas de Series de Tiempo: An initial thematic categorization was proposed to organize time series data into major themes and subthemes, enhancing accessibility and analysis.
- Categorization of Economic Distributions: Key economic distributions relevant to macroeconomics, such as GDP and sectoral gross value added, were categorized to highlight significant economic indicators.
- Correction of DataFrame Example Data: Identified and corrected errors in example data for a DataFrame constructor, ensuring consistent array lengths.
- Python Function for Metadata Summary: Developed a Python function to retrieve and summarize metadata for specific distribution IDs within a DataFrame.
- Categorization of Economic Data Distributions: A structured framework was established to categorize economic data into themes like Economic Activity and Growth, Labor Market, and Sectoral Analysis.
Achievements
- Established a comprehensive framework for categorizing economic data, facilitating better organization and analysis.
- Corrected data inconsistencies in DataFrame examples, improving data integrity.
- Developed a Python function for effective metadata retrieval and summarization.
Pending Tasks
- Further refinement and validation of the categorization framework to ensure it meets analytical needs.
- Implementation of the Python function in a real-world dataset to test its effectiveness.