Here are the corresponding visualizations for the python code.
Figure 1: Heatmap to show missing data in the data set.
Figure 2: San Francisco Police Department demographics of race description by sex (gender). Where M indicates Male, F indicates Female, and U is Unidentified. The vast majority of the traffic incidents in 2017 were by white males. Nearly 2 times as much as the next race description category.
Figure 3: This is a count plot with seaborn showing the distribution of race description.
Figure 4: This is a count plot with seaborn showing the distribution of sex (gender).
Figure 5: This is a histogram of age of individuals who had traffic violations in 2017.
Figure 6: Boxplot with race description and age. The horizontal line in the boxplot indicates the mean.
Figure 7: This is another way of displaying the race description data.
Figure 8: Hexbin plot of race description and age. The cluster of the darker colors show tighter correlation.
Figure 9: This is a lmplot preformed with Seaborn. Comparing sex, race, and time of day.