Crime Types

Let’s dive deeper and look into the different crime types. What do you think are the most common crime types? To answer this question, we want to plot the ten most common crime types in descending order.

• How many different crime types are in this data set?

• Plot the ten most common crime types in descending order.

We reduced the different crime types by dividing them into superordinate groups in order to get a better overview, but you don't have to do this. The result looks like this:

With these 10 most common crime types, let's look at the gender distribution for these crime types.

• Plot the percentage of men and women affected by the 10 most common crime types.

  

Your plots don't have to look exactly like this. Actually, we encourage you to realize your own ideas. Most of the times you will find a solution to your problem by using a search engine of your choice or websites like stackoverflow.com.

🏴‍☠️: geom_col() or geom_bar() will make a stacked barplot by default. But sometimes, you may want to have the bars side by side for ease of comparability. This is done by the position argument.

🐍: When analyzing the frequency of categories, use nunique() to count the distinct types in a column, or value_counts() to rank them by occurrence. To visualize the top categories, use plot(kind='barh') for a horizontal bar chart, which works well for long category names. For grouped data, groupby() combined with size() can calculate category counts for each group, and unstack() pivots the data for better visualization. When customizing plots, specify colors explicitly with a dictionary (e.g., for gender categories), and ensure that less common categories are represented distinctly. Experiment with grid() and title() to improve readability and aesthetics!