📅 2023-09-28 — Session: Developed and Tested Data Aggregation Pipeline

🕒 16:00–16:40
🏷️ Labels: Data Aggregation, Python, Pandas, Data Processing, Csv Export
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary goal of this session was to develop and test a data aggregation pipeline for multiple datasets, focusing on money-related columns and addressing common data processing issues.

Key Activities

  • Data Aggregation Plan: Outlined a structured plan for aggregating datasets by characteristics and year, focusing on money-related columns.
  • Key Columns Identification: Identified key columns for datasets df_wb, df_aiddata_china, and df_aiddata_wb for further aggregation.
  • Python Function Development: Developed Python functions for data aggregation using pandas, resolving common DataFrame issues such as SettingWithCopyWarning and aggregation duplication.
  • Loop and Data Inspection: Implemented a loop to print money column values for data inspection, and addressed execution in a local environment for better inspection.
  • Data Cleaning: Parsed numeric columns and handled duplicate entries in DataFrames, ensuring proper data formatting and aggregation.
  • Datetime and CSV Export: Ensured consistent datetime formatting and exported aggregated data to CSV files.

Achievements

  • Successfully developed and tested a comprehensive data aggregation pipeline using Python and pandas.
  • Resolved common issues related to DataFrame manipulation and aggregation.
  • Prepared cross-section datasets for review by Eric and Raolin.

Pending Tasks

  • Await feedback from Eric and Raolin on the prepared datasets to make any necessary modifications.