Description

This repository was created as a part Google Data Analytics capstone project on Coursera. While the capstone was envisioned to be done with R language or Google Sheets, I opted for Python. The idea was to determine is there any correlation between birth rate drop and common social/financial factors, such as costs of living, health or education expenditures.
Most of the central .ipynb notebook was done with pandas and plotly.express packages. File labels.py serves an auxiliary, home-brewed package containing dictionaries which are used to rename/replace various index and column names.
Folder imported_data includes CSV files downloaded from Eurostat and "Our World in Data" web pages. #todo -> add DOI references. Folder exported_data contains filtered/cleaned up data in various formats. Notebook try_outs.ipynb served as a playground to test how various functions and methods in Python work.