plotting multiple linear regressions on the same s

suppose i have the following:

x1 = np.random.randn(50)
y1 = np.random.randn(50) * 100
x2 = np.random.randn(50)
y2 = np.random.randn(50) * 100

df1 = pd.DataFrame({'x1':x1, 'y1': y1})
df2 = pd.DataFrame({'x2':x2, 'y2': y2})

sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)

this will create 2 separate plots. how can i add the data from df2 onto the SAME graph? all the seaborn examples i have found online seem to focus on how you can create adjacent graphs (say, via the 'hue' and 'col_wrap' options). also, i prefer not to use the dataset examples where an additional column might be present as this does not have a natural meaning in the project i am working on.

if there is a mixture of matplotlib/seaborn functions that are required to achieve this, i would be grateful if someone could help illustrate. thanks!

You could use seaborn's FacetGrid class to get desired result. You would need to replace your plotting calls with these lines:

# sns.lmplot('x1', 'y1', df1, fit_reg=True, ci = None)
# sns.lmplot('x2', 'y2', df2, fit_reg=True, ci = None)
df = pd.concat([df1.rename(columns={'x1':'x','y1':'y'})
                .join(pd.Series(['df1']*len(df1), name='df')), 
                df2.rename(columns={'x2':'x','y2':'y'})
                .join(pd.Series(['df2']*len(df2), name='df'))],
               ignore_index=True)

pal = dict(df1="red", df2="blue")
g = sns.FacetGrid(df, hue='df', palette=pal, size=5);
g.map(plt.scatter, "x", "y", s=50, alpha=.7, linewidth=.5, edgecolor="white")
g.map(sns.regplot, "x", "y", ci=None, robust=1)
g.add_legend();

This will yield this plot: