How add asymmetric errorbars to Pandas grouped barplot?
After some trial and error I figured it out. As you mentioned:
matplotlib allows for asymmetric errorbars using a
yerr=[ylo, yhi]construct
But it turns out that since you have two bar groups (i.e. "2015 2Q" and "2016 1Q"), the shape matplotlib expects is (2, 2, 4), or: [number of groups] x 2 x [number of bars per group].
This is my code, starting after the definition of errLo and errHi in your code:
err = []for col in errLo: # Iterate over bar groups (represented as columns) err.append([errLo[col].values, errHi[col].values])err = np.abs(err) # Absolute error values (you had some negatives)pprint(err)print 'Shape:', np.shape(err)df.pivot(index='ID', columns='quarter', values='Percent').plot(kind='bar', yerr=err)plt.show()Output:
array([[[ 0.378046, 0.06185 , 0.866025, 0.028012], [ 1.776511, 1.123492, 2.645141, 0.82596 ]], [[ 0.057962, 0.027515, 0.057962, 0.092614], [ 1.054612, 0.810851, 1.054612, 0.541513]]])Shape: (2L, 2L, 4L)