I have been using Python for data analysis for about a year now. In the last couple of months, I have started to work with Tableau. Tableau is a visualization tool, which I find to be a very different experience from using the Python Panda’s library. The visualizations for today’s analysis can be found below and on Tableau Public.
Datasets and data prep
The UN has interesting (and free) datasets available on their website that look at countries around the world. For today’s data analysis, I first used a dataset that lists the share (%) of women in parliament around the world. And second, I used a dataset that provides the ratios between girls and boys for different levels of education (primary, secondary, and tertiary). Before I created graphs in Tableau, I slightly changed the format of the datasets using Tableau Prep. The country/region variable included different levels of countries and regions. Therefore, both continents (e.g. Africa) and countries (e.g. India) were included in the variable. I created a variable that only includes regions on a country level. Furthermore, the ‘share of women’ in parliament was in a format which Tableau won’t recognize as a percentage. This meant that I had to create a new variable for the share of women as well.
See the interactive visualization here. The first map displays the share of women in parliament around the world. You can select a specific year using the dropdown menu next to the map. Hover over the map to see the percentage, year, and country name. The higher the percentage, the higher share of women in that country’s parliament.
The second map displays the ratio (girls:boys) in education around the world. Using the dorpdown menus, you can filter for year and education level. A ratio above 1 indicates that there are more women in that level of education than men. A ratio below 1 indicates the opposite, more men in education than women.
The third graph displays the datapoints for each country, based on the share of women in parliament and the ratio of girls to boys in education. Countries below the horizontal reference line (of 1), have more men than women in enrolled in education. Countries above this reference line have more women enrolled. The countries to the left of the vertical reference line have a share of less than 50% women in parliament. Countries on the right side of the vertical reference line have parliaments that are made up of at least 50% women.
The third graph can be filtered using the ‘select education level’ filter (next to the second map). When you select different education levels, you will see that the share of women in parliament does not necessarily correlate with the ratio of girls to boys in education.