import<\/span> numpy as<\/span> np
\nimport<\/span> pandas as<\/span> pd
\nimport<\/span> seaborn as<\/span> sns
\nimport<\/span> matplotlib.pyplot as<\/span> plt<\/p>\n# We will need to access some of these matplotlib classes directly<\/span>
\nfrom<\/span> matplotlib.lines import<\/span> Line2D # For points and lines<\/span>
\nfrom<\/span> matplotlib.patches import<\/span> Patch #\u00a0For KDE and other plots<\/span>
\nfrom<\/span> matplotlib.legend import<\/span> Legend
\nfrom<\/span> matplotlib import<\/span> cm<\/p>\n# Initialise random number generator<\/span>
\nrng = np.random.default_rng(seed=42<\/span>)<\/p>\n# Generate sample of 25 numbers<\/span>
\nn = 25<\/span><\/p>\nclusters = []<\/p>\n
for<\/span> c in<\/span> range<\/span>(0<\/span>,3<\/span>):
\n\u00a0 \u00a0 # Crude way to get different distributions<\/span>
\n\u00a0 \u00a0 #\u00a0for each cluster<\/span>
\np = rng.integers(low=1<\/span>, high=6<\/span>, size=4<\/span>)
\ndf = pd.DataFrame({
\n'x'<\/span>: rng.normal(p[0<\/span>], p[1<\/span>], n),
\n'y'<\/span>: rng.normal(p[2<\/span>], p[3<\/span>], n),
\n'name'<\/span>: f\"Cluster {c+1<\/span>}<\/span>\"<\/span>
\n})
\nclusters.append(df)<\/p>\n# Flatten to a single data frame<\/span>
\nclusters = pd.concat(clusters)<\/p>\n# Now do the same for data to feed into<\/span>
\n# the second (scatter) plot... <\/span>
\nn = 8<\/span>
\npoints = []<\/p>\nfor<\/span> c in<\/span> range<\/span>(0<\/span>,2<\/span>):<\/p>\np = rng.integers(low=1<\/span>, high=6<\/span>, size=4<\/span>)<\/p>\ndf = pd.DataFrame({
\n'x'<\/span>: rng.normal(p[0<\/span>], p[1<\/span>], n),
\n'y'<\/span>: rng.normal(p[2<\/span>], p[3<\/span>], n),
\n'name'<\/span>: f\"Group {c+1<\/span>}<\/span>\"<\/span>
\n})
\npoints.append(df)<\/p>\npoints = pd.concat(points)<\/p>\n
# And create the figure<\/span>
\nf, ax = plt.subplots(figsize=(8<\/span>,8<\/span>))<\/p>\n# The KDE-plot generates a Legend 'as usual'<\/span>
\nk = sns.kdeplot(
\ndata=clusters,
\nx='x'<\/span>, y='y'<\/span>,
\nhue='name'<\/span>,
\nshade=True<\/span>,
\nthresh=0.05<\/span>,
\nn_levels=2<\/span>,
\nalpha=0.2<\/span>,
\nax=ax,
\n)<\/p>\n# Notice that we access this legend via the<\/span>
\n# axis to turn off the frame, set the title, <\/span>
\n# and adjust the patch alpha level so that<\/span>
\n# it closely matches the alpha of the KDE-plot<\/span>
\nax.get_legend().set_frame_on(False<\/span>)
\nax.get_legend().set_title(\"Clusters\"<\/span>)
\nfor<\/span> lh in<\/span> ax.get_legend().get_patches():
\nlh.set_alpha(0.2<\/span>)<\/p>\n# You would probably want to sort your data <\/span>
\n# frame or set the hue and style order in order<\/span>
\n# to ensure consistency for your own application<\/span>
\n# but this works for demonstration purposes<\/span>
\ngroups = points.name.unique()
\nmarkers = ['o'<\/span>, 'v'<\/span>, 's'<\/span>, 'X'<\/span>, 'D'<\/span>, '<'<\/span>, '>'<\/span>]
\ncolors = cm.get_cmap('Dark2'<\/span>).colors<\/p>\n# Generate the scatterplot: notice that Legend is<\/span>
\n# off (otherwise this legend would overwrite the <\/span>
\n# first one) and that we're setting the hue, style,<\/span>
\n# markers, and palette using the 'name' parameter <\/span>
\n# from the data frame and the number of groups in <\/span>
\n# the data.<\/span>
\np = sns.scatterplot(
\ndata=points,
\nx=\"x\"<\/span>,
\ny=\"y\"<\/span>,
\nhue='name'<\/span>,
\nstyle='name'<\/span>,
\nmarkers=markers[:len<\/span>(groups)],
\npalette=colors[:len<\/span>(groups)],
\nlegend=False<\/span>,
\ns=30<\/span>,
\nalpha=1.0<\/span>
\n)<\/p>\n# Here's the 'magic' -- we use zip to link together <\/span>
\n# the group name, the color, and the marker style. You<\/span>
\n# *cannot* retreive the marker style from the scatterplot<\/span>
\n# since that information is lost when rendered as a <\/span>
\n# PathCollection (as far as I can tell). Anyway, this allows<\/span>
\n# us to loop over each group in the second data frame and <\/span>
\n# generate a 'fake' Line2D plot (with zero elements and no<\/span>
\n# line-width in our case) that we can add to the legend. If<\/span>
\n# you were overlaying a line plot or a second plot that uses<\/span>
\n# patches you'd have to tweak this accordingly.<\/span>
\npatches = []
\nfor<\/span> x in<\/span> zip<\/span>(groups, colors[:len<\/span>(groups)], markers[:len<\/span>(groups)]):
\npatches.append(Line2D([0<\/span>],[0<\/span>], linewidth=0.0<\/span>, linestyle=''<\/span>,
\ncolor=x[1<\/span>], markerfacecolor=x[1<\/span>],
\nmarker=x[2<\/span>], label=x[0<\/span>], alpha=1.0<\/span>))<\/p>\n# And add these patches (with their group labels) to the new<\/span>
\n# legend item and place it on the plot.<\/span>
\nleg = Legend(ax, patches, labels=groups,
\nloc='upper left'<\/span>, frameon=False<\/span>, title='Groups'<\/span>)
\nax.add_artist(leg);<\/p>\n
![\"\"](\"http:\/\/www.reades.com\/wp-content\/uploads\/2021\/06\/2-Legends.png\")
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