Weekly Visualizations

I used pandas to create a dataframe from the csv file of missing persons in South Dakota from the NamUS database. I cleaned up the data slightly by deleting certain columns and longer race descriptions that did not fit into the title. My aim was to use python to graph the high rate of American Indian/Alaska Native missing people in South Dakota. I found that while 23.5% of all missing people in South Dakota are Native, they make up only 8% of the state's population. I hope to expand on this case study in my final project. Immediately below is the produced countplot, and further down you'll find the more extensive work behind it. I tried multiple methods to replace the x-axis category label of "American Indian / Alaska Native" to "Indigenous," and "White / Caucasion" to "White" in order to make the bottom of the graph easier to read and visually more appealing, but had no luck. I tried dataframe.replace() first, but that ended up deleting the rest of the data besides Natives. Next I tried using mutate from the tidyverse package (learned this in stat20) but found that was not compatible with the code I was writing (I wonder if that only works in R?) I plan to make the graph using a different method than sns.countplot for my final project.