Trying to add the average value to each category in the plot. I have been trying to add these average values independently, per category, but without success. Is there a way that catplot can average the values from the data set and plot that extra value with a different color? My goal is to add and differentiate the average value from the individual values so can be visually identified.

plt.rcParams["figure.figsize"] = [5.50, 5.50]
plt.rcParams["figure.autolayout"] = True

ax = sns.catplot(x="Sample Set", y="Values [%]", data=df)
ax.set_xticklabels(rotation=90)
ax.despine(right=True, top=True)

sp = 100
delta = 5
plt.axhline(y=sp, color='gray', linestyle='--', label='Target')

plt.axhline(y=sp*((100 (delta*2))/100), color='r', linestyle='--', label='10%')
plt.axhline(y=sp*((100-(delta*2))/100), color='r', linestyle='--')

plt.ylim(80, 120)
plt.title('Sample Location[enter image description here][1]', fontsize = 14, y=1.05)
    
plt.legend(frameon=False, loc ="lower right")
plt.savefig(outputFileName, dpi=300, bbox_inches = 'tight')
plt.show()
plt.draw()

CodePudding user response：

You probably run into strange error messages, as you named the return value of sns.catplot as ax.

CodePudding user response：

According to plotting with seaborn using the matplotlib object-oriented interface as catplot is a Figure-leveltype of graph, will be much harder than doing it comparing to some other types of graph.

The second group of functions (Figure-level) are distinguished by the fact that the resulting plot can potentially include several Axes which are always organized in a "meaningful" way. That means that the functions need to have total control over the figure, so it isn't possible to plot, say, an lmplot onto one that already exists. Calling the function always initializes a figure and sets it up for the specific plot it's drawing.