hue | small package for interacting with a Phillips Hue bridge | REST library

• Calculate the mean for each group, and then add them to the existing ax with a seaborn.lineplot
• Set dodge=False in the seaborn.boxplot
• Remember that the line in the boxplot is the median, not the mean.
  • Add the means to boxplot with showmeans=True, and then remove marker='o' from the lineplot, if desired.
• As pointed out JohanC's answer:
  • sns.pointplot(data=dfm, x='variable', y='value', hue='parametrized_factor', ax=ax) can be used without the need for calculating dfm_mean, however there isn't a legend=False parameter, which then requires manually managing the legend.
  • Also, I think it's more straightforward to use dodge=False than to calculate the offsets.
  • Either answer is viable, depending on your requirements.

You are missing the col_order=cols argument to the grid = sns.FacetGrid(...) call.

If you are intrested in a different idea here is another one without JS and only few line of CSS and with transparency:

When calculating an eigenvector you may change its sign and the solution will also be a valid one.

So any PCA axis can be reversed and the solution will be valid.

Nevertheless, you may wish to impose a positive correlation of a PCA axis with one of the original variables in the dataset, inverting the axis if needed.

You could create two stripplots, one for each sex and draw them as the same spot. The double entries of the legend can be removed via get_legend_handles_labels() and taking a subset of the handles and the labels.

Here is an example using the titanic dataset:

It is not recommend nor supported to load the Google Maps JS API twice on a single page. You will get a console warning about this.

Problem:

• double billing
• collision and unexpected behaviors

Solutions:

• Use same key for both maps on page(not sure why this wouldn't be preferred).
• If you really must, one of the maps could be embedded in an iframe.

Here is one way:

The issue is, you are already passing preprocessing_function once here

• Given the test dataframe, df, in the OP, the easiest way to plot the dataframe is to use pandas.DataFrame.transpose, and plot with seaborn.relplot using a wide format.
  • This automatically uses the dataframe index as the x-axis, and the column headers for hue.
  • The visualization can also be produced with sns.lineplot(data=df, marker='o') instead of using relplot.