Unable to generate error bars with seaborn
I found this question when trying to find a way to add my own precalculated, custom error bars (standard deviations) to a Seaborn barplot with grouped values. I finally managed to find a workaround... The idea is to duplicate the observations drawing from a normal distribution with the desired mean and standard deviation. Then, the built-in "ci" option in sns.barplot does the rest. Not the cleanest solution... but it does the trick (at least for small datasets). An example:

import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

dfBarPlot = pd.read_csv('my_precalculated_values.csv')
#duplicate observations to get good std bars
dfCopy = dfBarPlot.copy()
duplicates = 30 # increase this number to increase precision
for index, row in dfBarPlot.iterrows():
    for times in range(duplicates):
        new_row = row.copy()
        new_row['Y'] = np.random.normal(row['Y'],row['precomputed_std']) 
        dfCopy = dfCopy.append(new_row, ignore_index=True)

# Now Seaborn does the rest
sns.set_style("whitegrid")
fig = sns.barplot(x='X',
                  y='Y',
                  hue='Cases',
                  ci='sd',
                  data=dfCopy)

plt.legend(loc='upper right')
sns.set(rc={'figure.figsize':(8,5)})
plt.show()