📅 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
, anddf_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.