boxplot

Say I have a boxplot like the one shown below: I would like to achieve a different coloring: Fri , Sat and Sun would be colored blue; Thur would be colored red I would like to differentiate hues with a pattern in the box (stripped or something) I can't find a way to do this manipulation using seaborn palette. I know I can mess with the boxes of a boxplot in matplotlib directly but seaborn gives me the axes back. How can I get the boxes in this case? The boxes end up in the artists list on the returned axes, so you just need to manipulate the attributes on those objects: ax = sns.boxplot(data=x

I have a plot like the following (using plt.boxplot() ): Now, what I want is plotting a number how often those outliers occured (preferably to the top right of each outlier). Is that somehow achievable? ax.boxplot returns a dictionary of all the elements in the boxplot. The key you need here from that dict is 'fliers' . In boxdict['fliers'] , there are the Line2D instances that are used to plot the fliers. We can grab their x and y locations using .get_xdata() and .get_ydata() . You can find all the unique y locations using a set , and then find the number of fliers plotted at that location

Following up on this question , I am trying to make boxplots and pairwise comparisons to show levels of significance (only for the significant pairs) again, but this time I have more than 2 groups to compare and more complicated facets. I am going to use the iris dataset here for illustration purposes. Check the MWE below where I add an additional "treatment" variable. library(reshape2) library(ggplot2) data(iris) iris$treatment <- rep(c("A","B"), length(iris$Species)/2) mydf <- melt(iris, measure.vars=names(iris)[1:4]) ggplot(mydf, aes(x=variable, y=value, fill=Species)) + geom_boxplot() +

I use the following code to produce multiple boxplots, ranked by the mean value of the variables: zx <- replicate (5, rnorm(50)) zx_means <- (colMeans(zx, na.rm = TRUE)) colnames (zx) <- seq_len (ncol (zx)) boxplot(zx [, order (zx_means)], horizontal = FALSE, outline = FALSE) points(zx_means [ order (zx_means)], pch = 22, col = "darkgrey", lwd = 7) ( See this post for more details ) When I change the code to horizontal = TRUE , I'm not able to make the points line up with the boxplots. Any ideas for how to properly add points to horizontal boxplots? cbeleites You need to give both x and y

Following up on this question I posted some days ago, I want to perform something similar. Given the following MWE: ############################## ##MWE library(ggplot2) library(ggsignif) set.seed(1) alpha.subA <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''), Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)), Value=rnorm(n=163)) alpha.subA$DB <- "DATABASE1" set.seed(2) alpha.subB <- data.frame(Sample.ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000

Is there a straightforward way to remove the whiskers from a box-whisker-plot in ggplot2 in R ? I'd like to keep only the boxes themselves. MWE: library("ggplot2") p <- ggplot(mtcars, aes(factor(cyl), mpg)) p + geom_boxplot(outlier.size = 0) We only need to add the argument coef = 0 : library(ggplot2) p <- ggplot(mtcars, aes(factor(cyl), mpg)) p + geom_boxplot(outlier.size = 0, coef = 0) One way would be to use stat_summary_df() to calculate meadian, 25 and 75 percentiles and then plot those data with geom="crossbar" . Automatically it can be done with "median_hilow" inside the stat_summary_df

I am trying to set a space between the boxplots (between the green and orange boxes) created with Python Seaborn module's sns.boxplot() . Please see attached the graph, that the green and orange subplot boxes are stuck to each other, making it visually not the most appealing. Can't find a way to do that, anyone could find a way (code attached)? import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") sns.set(style="ticks", palette='Set2', font='Roboto Condensed') sns.set_context("paper", font_scale=1.1