Blob plots, like violin plots, but more precise

Violin plots are beautiful, useful and much clearer than drawing several histograms on the same plot.
In my opinion, the smoothing function that they implement with python does not really compensate the loss of precision with the visual appealing.
Why not plotting the whole distribution, then?

I modified the violin plots source code that I found here HERE

And produced... let's called them blob plots:

Here is the code.

	import math
	import matplotlib as mpl
	import matplotlib.pyplot as plt
	import numpy as np
	def blob_plot(ax,data,list_pos, extra=False):
	lextra=[]
	for d,p in zip(data,list_pos):
	#this makes the histogram that constitutes the blob plot
	m=min(d)
	M=max(d)
	nbins=20
	x = np.linspace(m,M,nbins) # support for histogram
	his,bins = np.histogram(d, bins=nbins)
	#scale the histogram to a proper size (you may have to fiddle with this), then place it at position 'pos'
	scale_blob=2.
	shift_his_plus_pos = [ p + h*scale_blob/float(len(d)) for h in his]
	shift_his_minus_pos = [ p - h*scale_blob/float(len(d)) for h in his]
	facecolor,alpha=color_alpha_blobs # assign color and transparency
	#this is the matplotlib function that does the trick
	ax.fill_betweenx(x,shift_his_minus_pos, shift_his_plus_pos, linewidth=0.0, facecolor= facecolor, alpha=alpha)
	#calculates median or mean, if you want
	if extra=='median': lextra.append( np.median(d) )
	elif extra=='mean': lextra.append( np.mean(d) )
	#and plots it
	if extra != False:
	color = 'orangered'
	ax.plot(list_pos,lextra, color=color_mean_or_median,linestyle='-',marker='D',markersize=5, lw=1.5)
	# MAKE UP SOME DATA
	list_positions=[0,1,2,3,4] #this is their position on the x-axis
	data_for_blobplot=[] # this will contain a list for each blob we want in the plot
	extra='median'
	for pos in list_positions:
	if pos <= 2: some_data = np.random.gumbel(pos,1, 1000) # generate some data
	else: some_data = np.random.normal(pos,1, 1000) # generate some other data
	data_for_blobplot.append(some_data) # and append it to the list
	# PLOTTING
	color_alpha_blobs=('midnightblue',0.7) #this is the color of the blobplot
	color_mean_or_median='orangered' #this is the colot of the median or mean, whatever you specify
	fig = plt.figure() # makes a figure
	ax = fig.add_subplot(111) # adds a single subplot
	#this is the blobplotting function
	blob_plot(ax, data_for_blobplot, list_pos=list_positions, extra=extra) #makes blob plots, if extra is not spcified, makes no median/mean
	ax.set_xlim(-1., len(list_positions)) #to display all of them
	ax.set_ylim(-2., 12.)
	plt.title('Beautiful blob plots',fontsize=18)
	plt.xlabel('$\mu$ (yup, that is Latex)', fontsize=14)
	plt.ylabel('distribution of my variable', fontsize=14)
	####################################################
	# So far this is all you need to make the blob plot
	# Below is if you want a legend for the medain/mean
	####################################################
	# draw temporary dot to use it for a legend, later set to invisible
	h = ax.scatter( [1],[1], marker='D', color=color_mean_or_median, label='extra')
	#draws the legend, with larger symbols
	leg=plt.legend(loc= 'upper left',scatterpoints=1)
	for legobj in leg.legendHandles:
	legobj.set_linewidth(10.0)
	h.set_visible(False)
	plt.savefig('blobplot.png') # saving to png, alternatively you can write 'blobplot.pdf', or other formats
	plt.show() #prints to screen your splendid blobplot

view raw blobplot.py hosted with ❤ by GitHub

Feel free to take it and do whatever you want with it.
If you have ideas to improve it let me know!
If you like the result, go say thanks to the violin plot maker (I did very little)