Making maps in Python

Seventh in a series on scikit-learn and GeoPandas

Posted by Michelle Fullwood on July 15, 2015

Previous articles in this series:

A web map in two lines of Python

Here’s how to make a map from a GeoPandas GeoDataFrame in one step:

ax = df.plot(column='classification', colormap='accent')

Basic map

where classification was the name of the column with our new Malay/Chinese/British/Indian/Generic/Other labels on each road (row).

What if we want to make this nice and interactive, like a Leaflet map? So we can pan and zoom and actually see street names? There’s a library called mplleaflet, by Jake Wasserman, that can do this for you:

import mplleaflet
mplleaflet.display(fig=ax.figure,, tiles='cartodb_positron')

(If you don’t see colours on that map, just reload the page.)

To export it to an HTML page, you can do this:,, tiles='cartodb_positron', path='sgmap.html')

We don’t have much control over colours here, but it would be nice to theme them, associating Chinese with its traditional red, Malay with its traditional green, etc. Here’s a hacky way to do it:

labels = list(set(df['classification'].values))
# [u'British', u'Chinese', u'Generic', u'Indian', u'Malay', u'Other']
# this is the order in which colours from a colourmap will be applied

# British -> blue, Chinese -> red, etc...
my_colors = ['blue', 'red', 'gray', 'yellow', 'green', 'purple'])

# create a colour map with these colours
from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('my cmap', my_colors)

# do the plot
ax2 = df.plot(column='classification', colormap=cmap),, tiles='cartodb_positron', path='sgmap2.html')


mplleaflet is awesome for exploratory data analysis, but you might want to have more control over how your map looks. For this, I recommend using one of the following:

  • QGIS (C++ but has Python bindings)
  • Mapnik (C++ but has Python bindings)
  • Tilemill (GUI built on top of Mapnik)
  • Folium (maybe, haven’t investigated fully)

A nice feature of Tilemill is that it allows you to define your map styling using CartoCSS. For example, here’s how we would define the colours:

[classification='Malay']{ line-color: green; }
[classification='British']{ line-color: blue; }
[classification='Chinese']{ line-color: red; }
[classification='Indian']{ line-color: yellow; }
[classification='Other']{ line-color: purple; }
[classification='Generic']{ line-color: gray; }

You can also control the line width at various zoom levels:

line-opacity: 0.7;
[zoom>18] {line-width: 10;}
[zoom=18] {line-width: 7;}
[zoom=17] {line-width: 6;}
[zoom=16] {line-width: 5;}
[zoom=15] {line-width: 3.5;}
[zoom=14] {line-width: 3;}
[zoom=13] {line-width: 1.5;}
[zoom<13] {line-width: 1;}

If these are too fiddly, some web mapping solutions also use CartoCSS. I really like CartoDB, which is how I made my main map:

We can browse this map to look at clusters of street names, which are now conveniently colour-coded for our analysis!


It’s remarkably easy to make maps with GeoPandas and ancillary libraries like mplleaflet, thanks to the developers of these libraries :)

That’s all the technical stuff in this series. Next time, I’ll round everything off and talk about what I learned about Singapore street names from doing this project.