Let's dive right into an exercise, where we'll continue working on the country data we scraped earlier. Recall that we extracted the central bank interest rates and populations of each country, and saved the results in CSV files. We'll load the data from these files and merge them into a DataFrame, which will then be used as the data source for the interactive visualizations to follow.
- In the chapter-3-workbook.ipynb Jupyter Notebook, scroll to the Subtopic Building a DataFrame to store and organize data .
We are first going to load the data from the CSV files, so that it's back to the state it was in after scraping. This will allow us to practice building DataFrames from Python objects, as opposed ...
