Seaborn/Matplotlib create custom error band along line

Question

I have an experiment with 10 participants and 96 accuracies collected for each. acc_i represents the participant's overall accuracy at timestep i. Therefore, I have a 10x96 numpy matrix which looks like this:

[[acc_0,acc_1,acc_2,...acc_95]
 [acc_0,acc_1,acc_2,...acc_95]
 [acc_0,acc_1,acc_2,...acc_95]
            .
            .
            .
 [acc_0,acc_1,acc_2,...acc_95]]

I want to plot a line of the average accuracy among all participants at each timestep, along with an error band that shows the average +- 1 standard deviation. I can calculate the average and standard deviations separately using pd.Series(np.average(human_accuracies, axis=0)) and pd.Series(np.std(human_accuracies, axis=0)). However, this gives me two separate lines on a graph when I use:

sns.lineplot(data=avg_accuracies)
sns.lineplot(data=sd_accuracies)

This is shown below:

How can I make my plot into something more like what is shown here:

I'd like the error band to be calculated using the standard deviation at each time step i +- the average accuracy at each time step i.

related: https://stackoverflow.com/questions/55368485/draw-error-shading-bands-on-line-plot-python?noredirect=1&lq=1 — Charlie Parker, Nov 08 '21 at 22:26

score 3 · Accepted Answer · answered Sep 02 '20 at 15:17

You can use plt.fill_between like this:

plt.fill_between(x=np.arange(len(avg_accuracies)),
                 y1=avg_accuracies - sd_accuracies,
                 y2=avg_accuracies + sd_accuracies,
                 alpha=0.25
                 )
plt.plot(np.arange(len(avg_accuracies)), avg_accuracies)

Output:

Seaborn/Matplotlib create custom error band along line

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