Categorical Plot Types

Intermediate Data Visualization with Seaborn

Chris Moffitt

Instructor

Categorical Data

• Data which takes on a limited and fixed number of values
• Normally combined with numeric data
• Examples include:
  • Geography (country, state, region)
  • Gender
  • Ethnicity
  • Blood type
  • Eye color

Plot types - show each observation

Plot types - abstract representations

Plot types - statistical estimates

Plots of each observation - stripplot

sns.stripplot(data=df, y="DRG Definition",
              x="Average Covered Charges",
              jitter=True)

Plots of each observation - swarmplot

sns.swarmplot(data=df, y="DRG Definition",
              x="Average Covered Charges")

Abstract representations - boxplot

sns.boxplot(data=df, y="DRG Definition",
            x="Average Covered Charges")

Abstract representation - violinplot

sns.violinplot(data=df, y="DRG Definition",
               x="Average Covered Charges")

Abstract representation - boxenplot

sns.boxenplot(data=df, y="DRG Definition",
              x="Average Covered Charges")

Statistical estimates - barplot

sns.barplot(data=df, y="DRG Definition", 
            x="Average Covered Charges",
            hue="Region")

Statistical estimates - pointplot

sns.pointplot(data=df, y="DRG Definition", 
              x="Average Covered Charges",
              hue="Region")

Statistical estimates - countplot

sns.countplot(data=df, y="DRG_Code", hue="Region")

Let's practice!

Intermediate Data Visualization with Seaborn