Two seaborn distplots one same axis

Question

I am trying to figure a nice way to plot two distplots (from seaborn) on the same axis. It is not coming out as pretty as I want since the histogram bars are covering each other. And I don't want to use countplot or barplot simply because they don't look as pretty. Naturally if there is no other way I shall do it in that fashion, but distplot looks very good. But, as said, the bars are now covering each other (see pic).

Thus is there any way to fit two distplot frequency bars onto one bin so that they do not overlap? Or placing the counts on top of each other? Basically I want to do this in seaborn:

Any ideas to clean it up are most welcome. Thanks.

MWE:

sns.set_context("paper",font_scale=2)
sns.set_style("white")
rc('text', usetex=False)
fig, ax = plt.subplots(figsize=(7,7),sharey=True)
sns.despine(left=True)

mats=dict()
mats[0]=[1,1,1,1,1,2,3,3,2,3,3,3,3,3]
mats[1]=[3,3,3,3,3,4,4,4,5,6,1,1,2,3,4,5,5,5]
N=max(max(set(mats[0])),max(set(mats[1])))

binsize = np.arange(0,N+1,1)
B=['Thing1','Thing2']
for i in range(len(B)):
    ax = sns.distplot(mats[i],
                      kde=False,
                      label=B[i],
                      bins=binsize)

ax.set_xlabel('My label')
ax.get_yaxis().set_visible(False)
ax.legend()
plt.show()

Answer 1

As @mwaskom has said seaborn is wrapping matplotlib plotting functions (well to most part) to deliver more complex and nicer looking charts.

What you are looking for is "simple enough" to get it done with matplotlib:

sns.set_context("paper", font_scale=2)
sns.set_style("white")
plt.rc('text', usetex=False)
fig, ax = plt.subplots(figsize=(4,4))
sns.despine(left=True)

# mats=dict()
mats0=[1,1,1,1,1,2,3,3,2,3,3,3,3,3]
mats1=[3,3,3,3,3,4,4,4,5,6,1,1,2,3,4,5,5,5]
N=max(mats0 + mats1)

# binsize = np.arange(0,N+1,1)
binsize = N
B=['Thing1','Thing2']

ax.hist([mats0, mats1], binsize, histtype='bar', 
        align='mid', label=B, alpha=0.4)#, rwidth=0.6)

ax.set_xlabel('My label')
ax.get_yaxis().set_visible(False)
# ax.set_xlim(0,N+1)
ax.legend()
plt.show()

Which yields:

You can uncomment ax.set_xlim(0,N+1) to give more space around this histogram.