📅 2023-09-28 — Session: Implemented Data Processing for Country Name Merging
🕒 18:10–19:10
🏷️ Labels: Python, Data Processing, CSV, Pandas, Data Cleaning
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to implement a robust data processing workflow to handle country name discrepancies across multiple datasets, using Python and its libraries.
Key Activities
- Opening .dta Files: Explored various methods to open Stata data files (.dta) using Python with pandas, R with the haven package, and other statistical tools.
- Merging Datasets: Developed and executed a Python script using pandas to merge multiple datasets, focusing on identifying and resolving discrepancies in country names.
- Data Cleaning: Implemented a Python code snippet to fix duplicated country names in a DataFrame by splitting and retaining only the first part of the ‘countryname’ column.
- Dataframe Logic: Created a script to merge dataframes on country names, sum money columns, and display results with merge indicators.
- CSV Generation: Generated a CSV file containing unique country names from datasets to facilitate manual matching.
Achievements
- Successfully merged datasets and identified discrepancies in country names.
- Cleaned data by correcting duplicated country names.
- Created a CSV for manual country name matching, aiding future data processing tasks.
Pending Tasks
- Manually match country names using the generated CSV to ensure consistency across datasets.