Hands-On Exploratory Data Analysis with Python

Discrete categorical attributes

We have one discrete categorical column in our dataframe, wine_category.

Let's visualize it using a count plot using the seaborn library:

fig = plt.figure(figsize=(16, 8))sns.countplot(data=df_wines, x="quality", hue="wine_category") 

The output of the preceding code is given as follows:

Figure 12.14 - Visualizing the discrete categorical dataset

Figure 12.14 shows different categories of wine [3, 4, 5, 6, 7, 8, 9] and their frequency distributions over a nice count plot. It is a clearer illustration for end stakeholders.