legend | Legend provides an end-to-end data platform experience

We can loop over the list 'l' with lapply, then get the table for each of the columns by looping over the columns with sapply after converting the column to factor with levels specified as 'u', get the proportions, transpose, convert to data.frame (as.data.frame), split by row (asplit - MARGIN = 1), then use transpose from purrr to change the structure so that each column from all the list elements will be blocked as a single unit, bind them with bind_rows

when using a calculated column for setColumns,
you can use a custom function, instead of the calc: "stringify"

the function will receive two arguments,
the data table and the row index.
the function should return the value to be displayed (the annotation).

You have to provide both annotations as object in 1 array, not an array containing objects containing arrays, see example:

It might be preferable to create an expression instead of a character string.

If you want to turn a character string into an expression, you need to parse it:

Try this query

$sql = "insert into donator (first_name,last_name,email,phone_no,amount) values('$fname','$lname','$email','$phn','$amount')";

And keep form action empty

There are several themes you can try out. If you use 'solar', you will get the color you want. The source of the theme can be found here for your reference.

You can customize you chart, You can use this. I use map for test and date for example ({"val": 100, "year": 2000}), so you can use your favorite type, Set or anything else.

• 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.

I think you should assign