 # Seaborn Plots with 2 Legends

Posted here because I will inevitably forget this painfully worked-out answer for having legends for two different types of plots in Seaborn…

```import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt```

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

```# Done plt.show(); ``` # The Full Stack: Tools & Processes for Urban Data Scientists

Recently, I was asked to give talks at both UCL’s CASA and the ETH Future Cities Lab in Singapore for students and staff new to ‘urban data science’ and the sorts of workflows involved in collecting, processing, analysing, and reporting on urban geo-data. Developing the talk proved to be a rather enjoyable opportunity to reflect on more than a decade in commercial data mining and academic research – not only did I realise how far I had come, I realised how far the domain had come in that time.

# Installing PostgreSQL Extensions on Mac OS X

I’ve been making a lot of use of PostgreSQL and PostGIS for working with geo-data over the past year and, having finally gotten over my hatred of the non-standard administrative commands, I am seriously impressed with what this setup makes possible. Even on a MacBook Air with just 8GB of RAM! However, one area where I’ve run into problems is the use of extensions on OS X so this post is intended as a handy reference for how to install them. # Hex Binning Land Registry Data

One of the known problems with choropleth maps is that small zones, even if they contain very significant values, tend to get lost in amongst much larger zones. A current example is that the ridings in London are much smaller than those outside of London, so it can be hard to tell what’s happening in the capital if you are looking at a map of the entire UK. One solution to this is the hexagonal bin. Continue reading