Control marker properties in seaborn pairwise boxplot - python-3.x

Question

I'm trying to plot a boxplot for two different datasets on the same plot. The x axis are the hours in a day, while the y axis goes from 0 to 1 (let's call it Efficiency). I would like to have different markers for the means of each dataset' boxes. I use the 'meanprops' for seaborn but that changes the marker style for both datasets at the same time. I've added 2000 lines of data in the excel that can be downloaded here. The values might not coincide with the ones in the picture but should be enough.
Basically I want the red squares to be blue on the orange boxplot, and red on the blue boxplot. Here is what I managed to do so far:
I tried changing the meanprops by using a dictionary with the labels as keys , but it seems to be entering a loop (in PyCharm is says Evaluating...)
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
#make sure you have your path sorted out
group1 = pd.read_excel('group1.xls')
ax,fig = plt.subplots(figsize = (20,10))
#does not work
#ax = sns.boxplot(data=group1, x='hour', y='M1_eff', hue='labels',showfliers=False, showmeans=True,\
# meanprops={"marker":{'7':"s",'8':'s'},"markerfacecolor":{'7':"white",'8':'white'},
#"markeredgecolor":{'7':"blue",'8':'red'})
#works but produces similar markers
ax = sns.boxplot(data=group1, x='hour', y='M1_eff', hue='labels',showfliers=False, showmeans=True,\
meanprops={"marker":"s","markerfacecolor":"white", "markeredgecolor":"blue"})
plt.legend(title='Groups', loc=2, bbox_to_anchor=(1, 1),borderaxespad=0.5)
# Add transparency to colors
for patch in ax.artists:
r, g, b, a = patch.get_facecolor()
patch.set_facecolor((r, g, b, .4))
ax.set_xlabel("Hours",fontsize=14)
ax.set_ylabel("M1 Efficiency",fontsize=14)
ax.tick_params(labelsize=10)
plt.show()
I also tried the FacetGrid but to no avail (Stops at 'Evaluating...'):
g = sns.FacetGrid(group1, col="M1_eff", hue="labels",hue_kws=dict(marker=["^", "v"]))
g = (g.map(plt.boxplot, "hour", "M1_eff")
.add_legend())
g.show()
Any help is appreciated!

Answer 1

I don't think you can do this using sns.boxplot() directly. I think you'll have to draw the means "by hand"
N=100
df = pd.DataFrame({'hour':np.random.randint(0,3,size=(N,)),
'M1_eff': np.random.random(size=(N,)),
'labels':np.random.choice([7,8],size=(N,))})
x_col = 'hour'
y_col = 'M1_eff'
hue_col = 'labels'
width = 0.8
hue_order=[7,8]
marker_colors = ['red','blue']
# get the offsets used by boxplot when hue-nesting is used
# https://github.com/mwaskom/seaborn/blob/c73055b2a9d9830c6fbbace07127c370389d04dd/seaborn/categorical.py#L367
n_levels = len(hue_order)
each_width = width / n_levels
offsets = np.linspace(0, width - each_width, n_levels)
offsets -= offsets.mean()
fig, ax = plt.subplots()
ax = sns.boxplot(data=df, x=x_col, y=y_col, hue=hue_col, hue_order=hue_order, showfliers=False, showmeans=False)
means = df.groupby([hue_col,x_col])[y_col].mean()
for (gr,temp),o,c in zip(means.groupby(level=0),offsets,marker_colors):
ax.plot(np.arange(temp.values.size)+o, temp.values, 's', c=c)