gmaps is a plugin for including interactive Google maps in the IPython Notebook.
Let's plot a heatmap of taxi pickups in San Francisco:
In [1]: import gmaps
import gmaps.datasets
gmaps.configure(api_key="AI...") # Your Google API key
# load a Numpy array of (latitude, longitude) pairs
In [2]: locations = gmaps.datasets.load_dataset("taxi_rides")
In [3]: m = gmaps.Map()
m.add_layer(gmaps.heatmap_layer(locations))
m
We can also plot chloropleth maps using GeoJSON:
In [1]: from matplotlib.cm import viridis
from matplotlib.colors import to_hex
import gmaps
import gmaps.datasets
import gmaps.geojson_geometries
gmaps.configure(api_key="AI...") # Your Google API key
In [2]: countries_geojson = gmaps.geojson_geometries.load_geometry('countries') # Load GeoJSON of countries
In [3]: rows = gmaps.datasets.load_dataset('gini') # 'rows' is a list of tuples
country2gini = dict(rows) # dictionary mapping 'country' -> gini coefficient
min_gini = min(country2gini.values())
max_gini = max(country2gini.values())
gini_range = max_gini - min_gini
def calculate_color(gini):
"""
Convert the GINI coefficient to a color
"""
# make gini a number between 0 and 1
normalized_gini = (gini - min_gini) / gini_range
# invert gini so that high inequality gives dark color
inverse_gini = 1.0 - normalized_gini
# transform the gini coefficient to a matplotlib color
mpl_color = viridis(inverse_gini)
# transform from a matplotlib color to a valid CSS color
gmaps_color = to_hex(mpl_color, keep_alpha=False)
return gmaps_color
# Calculate a color for each GeoJSON feature
colors = []
for feature in countries_geojson['features']:
country_name = feature['properties']['name']
try:
gini = country2gini[country_name]
color = calculate_color(gini)
except KeyError:
# no GINI for that country: return default color
color = (0, 0, 0, 0.3)
colors.append(color)
In [4]: m = gmaps.Map(height="600px")
gini_layer = gmaps.geojson_layer(
countries_geojson,
fill_color=colors,
stroke_color=colors,
fill_opacity=0.8)
m.add_layer(gini_layer)
m
Or, for coffee fans, a map of all Starbucks in the UK:
In [1]: import gmaps
import gmaps.datasets
gmaps.configure(api_key="AI...") # Your Google API key
In [2]: locations = gmaps.datasets.load_dataset("starbucks_uk")
In [3]: m = gmaps.Map()
starbucks_layer = gmaps.symbol_layer(
locations, fill_color="green", stroke_color="green", scale=2)
m.add_layer(starbucks_layer)
m
The current version of gmaps is only tested with IPython 4.2 or later and ipywidgets 5.2.2 and traitlets 4.3.0 or later. To upgrade to the latest versions, use:
$ pip install -U jupyter
Make sure that you have enabled widgets extensions to Jupyter:
$ jupyter nbextension enable --py --sys-prefix widgetsnbextension
Install the Python component using:
$ pip install gmaps
Then tell Jupyter to load the extension with:
$ jupyter nbextension enable --py --sys-prefix gmaps
To access Google maps, gmaps needs a Google API key. This key tells Google who you are, presumably so it can keep track of rate limits and such things. To create an API key, follow the instructions in the documentation. Once you have an API key, pass it to gmaps before creating widgets:
gmaps.configure(api_key="AI...")Documentation for gmaps is available here.
The current version of this library is inspired by the ipyleaflet notebook widget extension. This extension aims to provide much of the same functionality as gmaps, but for leaflet maps, not Google maps.
Jupyter-gmaps is built for data scientists. Data scientists should be able to visualize geographical data on a map with minimal friction. Beyond just visualization, they should be able to integrate gmaps into their widgets so they can build interactive applications.
We see the priorities of gmaps as:
- being able to export maps (both as HTML and as rendered widgets).
- responding to events, like user clicks, so that maps can be used interactively.
Report issues using the github issue tracker.
Contributions are welcome. Read the CONTRIBUTING guide to learn how to contribute.