In Seaborn v0.8.0 (July 2017) was added the ability to use error bars to show standard deviations rather than bootstrap confidence intervals in most statistical functions by putting ci="sd". So this now works
sns.tsplot(data=data, ci="sd")
For previous Seaborn versions a workaround for plotting standard deviation could be to use matplotlib errorbar on top of seaborn tsplot:
import numpy as np;
import seaborn as sns;
import pandas as pd
import matplotlib.pyplot as plt
# create a group of time series
num_samples = 90
group_size = 10
x = np.linspace(0, 10, num_samples)
group = np.sin(x) + np.linspace(0, 2, num_samples) + np.random.rand(group_size, num_samples) + np.random.randn(group_size, 1)
df = pd.DataFrame(group.T, index=range(0,num_samples))
# plot time series with seaborn
ax = sns.tsplot(data=df.T.values) #, err_style="unit_traces")
# Add std deviation bars to the previous plot
mean = df.mean(axis=1)
std = df.std(axis=1)
ax.errorbar(df.index, mean, yerr=std, fmt='-o') #fmt=None to plot bars only
plt.show()
