Activity 12: Data Wrangling Task – Fixing UN Data
Suppose the agenda of the data analysis is to find out whether the enrolment in primary, secondary, or tertiary education has increased with the improvement of per capita GDP in the past 15 years. For this task, we will first need to clean or wrangle the two datasets, that is, the Education Enrolment and GDP data.
The UN data is available on https://github.com/TrainingByPackt/Data-Wrangling-with-Python/blob/master/Chapter09/Activity12-15/SYB61_T07_Education.csv.
Note
If you download the CSV file and open it using Excel, then you will see that the Footnotes column sometimes contains useful notes. We may not want to drop it in the beginning. If we are interested in a particular country's data (like we are in this task), then it may well turn out that Footnotes will be NaN, that is, blank. In that case, we can drop it at the end. But for some countries or regions, it may contain information.
These steps will guide you to find the solution:
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