📅 2023-03-16 — Session: Analyzed World Bank project data for job investments
🕒 20:35–21:00
🏷️ Labels: World Bank, Data Analysis, Pandas, Optimization, Job Investments
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore and analyze World Bank project data to identify job-related investments in Africa and Asia. This involved using Python libraries like pandas and datetime for data manipulation.
Key Activities
- Data Analysis Setup: Initiated a notebook for analyzing World Bank projects, focusing on job-related investments in Africa and Asia.
- Title Suggestions: Generated a list of suggested titles for files related to World Bank job-related investments.
- Data Processing: Executed Python scripts using pandas to load, process, and filter World Bank project data, specifically targeting projects approved after January 1, 2000, in Africa and Asia.
- Project Analysis: Analyzed project data to display counts of projects by region and lending instrument, and examined project development objectives.
- Sampling and Export: Created dataframes for project and location information, and exported results to CSV files.
- Optimization: Improved data processing efficiency by optimizing dataframe merging and grouping operations.
Achievements
- Successfully processed and analyzed World Bank project data, focusing on job-related investments in Africa and Asia.
- Optimized data processing scripts for better performance and efficiency.
Pending Tasks
- Further analysis on the impact of these investments on job creation in specific regions.
- Integration of additional data sources for a more comprehensive analysis.