Sometimes it is desirable to get the values that are plotted for reference (using seaborn as a calculation package, as well as plotting).
Currently, it appears (please correct me if I'm just oblivious to it) that there is no easy way to access the values that are calculated for the plots (such as the mean, confidence intervals, etc.). In order to access these, I had to explicitly create my own _BarPlotter (for instance) to get at it's parameters.
This is something that other people have expressed interest in as well (see this Stack Overflow question and my answer). Is this functionality available, or are there any plans to make it accessible?
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Is it that unreasonable to output some information that is already calculated (could be a switch that defaults to 'off')? I don't think it adds much complexity, and these are values that people are interested in, clearly. It's not really statistical testing, so much as getting a quantitative value out of your plot...
When I need to do something like that, I end up writing the code myself. Here's some examples for a count-style FacetGrid annotated with values other than the values provided, e.g. here I wanted to plot percentages but annotate with absolute values. The code for the plot below is here
I wanted to provide additional motivation why it is important to provide access to the statistics that were computed while generating plots in seaborn. These figures often end up in scientific reports, where it is important to be precise about the analysis that was performed. Without an access to the details how a particular statistic (e.g., a regression line) was computed, it becomes more difficult to establish that the statistic shown in the figure is exactly the same as the one computed externaly via pandas / statsmodels. In other words, to use seaborn-generated figures in my reports, I need to rerun statistical analyses myself, visualize them with matplotlib and make sure they match seaborn's output exactly.
@mwaskom There's clearly demand. Naturally, some users that plot statistical plots would want access to the underlying statistics that generated the plot.
It doesn't save me any time to use Seaborn if I know I'll need the underlying statistics to the plots that Seaborn generates internally but doesn't provide.
To provide statistical plots, and not the underlying statistics optionally in the output, and then say that Seaborn is a visualization librbary or use the "out of scope" defense is not adequate or believable. It leaves us with one response - use Seaborn, or don't.