Integration with geopandas geometries

geopandas is a package that reads many type of geometric datasets, including geojson, topojson and shapefiles (albeit through fiona) and parses it into a pandas dataframe, where the geometry is parsed as a separate column.

It would be great if this is geometry type is recognized in Altair to make a map easily.

Problems might arise with projections as Vega doesn’t support all EPSG projections, but EPSG:4326 equals to Mercator and that is supported.

[mattijn]

Got it working 😃

import altair as alt
import geopandas as gpd
import pandas as pd
import json
%matplotlib inline

Load two datasets

counties = r'/Users/mattijnvanhoek/Desktop/us-10m.json'
unemp_data = r'/Users/mattijnvanhoek/Desktop/unemployment.tsv'

df = pd.read_csv(unemp_data, sep='\t')
# make sure you have Fiona <= 1.8a2 or >= 1.8.5
# since the versions in between did not include the `TopoJSON` driver
gdf = gpd.read_file(counties, driver='TopoJSON')
gdf.id = gdf.id.astype(int)

Apply inner-join on GeoDataFrame and DataFrame (gdf should be on ‘left’ side and df on the right to maintain geometry properties in the resulting joined GeoDataFrame)

gdf_merged = gdf.merge(df, left_on='id', right_on='id', how='inner')

Plot the GeoDataFrame using matplotlib and print the head(). The rate column is joined to the DataFrame.

gdf_merged.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x119e79128>

gdf_merged.head()

<div>

	geometry	id	rate
0	()	22051	.065
1	(POLYGON ((-90.1077214366575 30.19168413151698...	22051	.065
2	(POLYGON ((-120.8536146368232 49.0001146177235...	53073	.078
3	POLYGON ((-106.1123837970986 48.99904031068445...	30105	.046
4	POLYGON ((-114.0698488011574 48.99904031068445...	30029	.088

</div>

Prepare GeoDataFrame for Altair

# dump as json
json_gdf = gdf_merged.to_json()
# load as a GeoJSON object.
json_features = json.loads(json_gdf)

Make the Choropleth Map

# parse variable `features` from json_features to `alt.Data`
data_geo = alt.Data(values=json_features['features'])

# plot map, where variables ares nested within `properties`, 
alt.Chart(data_geo).mark_geoshape(
    fill='lightgray',
    stroke='white'
).properties(
    projection={'type': 'albersUsa'},
    width=700,
    height=400
).encode(
    color='properties.rate:Q')

👍

[iliatimofeev]

I think it’s little bit simpler

import altair as alt
import pandas as pd
import geopandas as gpd

alt.renderers.enable('notebook')


world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world  = world[world.continent!='Antarctica'] # do not display Antarctica

data  = alt.InlineData(values = world.to_json(), #geopandas to geojson string
                       # root object type is "FeatureCollection" but we need its features
                       format = alt.DataFormat(property='features',type='json')) 
alt.Chart(data).mark_geoshape(
).encode( 
    color='properties.pop_est:Q', # DataFrame fields are accessible through a "properties" object 
    tooltip='properties.name:N'
).properties( 
    projection={"type":'mercator'},
    width=500,
    height=300
)

But it will crush if we add Timestamp type field to DataFrame. To to avoid crashing it could be sanitized by alt.InlineData(values = alt.utils.core.sanitize_dataframe(world).to_json(),

In general case will be great to support any object with geo_interface that is widely supported by python GIS libraries. I suggest to have a special class for this case something like: data = alt.GeoData(world). I could make a PR, if it is needed.