By specifying ci='sd', you are indicating at what range you could be 95% sure that a sample of the population data contains the population standard deviation. From graphpad:
Interpreting the CI of the SD is straightforward. If you assume that your data were randomly and independently sampled from a Gaussian distribution, you can be 95% sure that the CI contains the true population SD.
Also, with only 137 rows of data, you are going to have a wider range for the confidence interval of your standard deviation than if you had let's say 1,000 + rows; because, generally, with a lower size
n -- the higher the variance of the data.
Seaborn uses a technique to make inferences about population statistics using "Bootstrapping" per the documentation:
This is a basic concept of bootstrapping.
"The basic idea of bootstrapping is that inference about a population from sample data (sample → population) can be modelled by resampling the sample data and performing inference about a sample from resampled data (resampled → sample). As the population is unknown, the true error in a sample statistic against its population value is unknown."
I hope this helps. I took stats in college and and understand the concept on a high-level, but I am not an expert in this space; so, I hope someone can offer some conclusions on how to interpret your results. If I were to make a conclusion (an extremely general one at that), you have a decent amount of variance in your data but also a small-medium sample size, which increases the range of your confidence interval. Finally, ci='95%' is a much more common way to analyze confidence rather than ci='sd' from some reading on the topic. For example, this article says "It's not done often, but it is certainly possible to compute a CI for a SD."