Categorical Plots

Bar Charts

We can use sns.barplot() to create bar charts.

By default Seaborn represents the mean of the data as the height of the bar and represents the dispersion of the data with a small grey line that crosses through the top of the bar. The top and bottom of that line represent the 95% confidence interval.

# create a view into the dataframe restricted to States that start with 'I'
dfi = df.loc[df.loc[:, 'State'].str.startswith('I')]

sns.barplot(x = "State",
            y = "Rates.Violent.All",
            data = dfi)
plt.show()



To get a horizontal bar chart, we just flip the variable assigned to the axes.

sns.barplot(y = "State",
            x = "Rates.Violent.All",
            data = dfi)
plt.show()



Grouped Bar Charts

By adding the hue arguments, we can create grouped bar charts.

dfi = df.loc[df.loc[:, 'State'].str.startswith('I')]

sns.barplot(x = "State",
            y = "Rates.Violent.All",
            hue= 'Decade',
            data = dfi)
plt.show()



dfi = df.loc[df.loc[:, 'State'].str.startswith('I')]

sns.barplot(x = "Decade",
            y = "Rates.Violent.All",
            hue= 'State',
            data = dfi)
plt.show()



Count Plots

We can make plots of the frequency of categorical data using sns.countplot().

Note that we are only supplying an argument for the category we wanted to be counted. Seaborn handles actually doing the count.

# countplot
sns.countplot(x = "Region",
              data = df_stateinfo)
plt.show()



We can make a horizontal version of the frequency count by specifying the categorical data using the argument y rather than x.

# horizontal countplot
sns.countplot(y = "Division",
              data = df_stateinfo)
plt.show()