PyDeck is the easiest way to turn GeoJSON into interactive visualizations in Python
Uber’s open source deck.gl is a WebGL-powered framework for visual exploratory data analysis of large datasets.
pydeck lets Python users create deck.gl maps without having to know Javascript and makes it super easy to created stunning 3D map visualizations in Python environments. It integrates well with the stack already used by data professionals such as Pandas.
In this example canvas we take data containing the center points of each zip code of Massachusetts and use pydeck to plot a Column illustrating the number of people living in that zip code. All is fully interactive and you can use your mouse to pan, zoom and pitch them map.
import pydeck as pdk
from urllib.request import urlopen
import json
import pandas as pd
from shapely.geometry import shape
import matplotlib.colors as colors
import matplotlib.cm as cmx
import matplotlib.pyplot as plt
# load massachusetts zip geo json
with urlopen('https://raw.githubusercontent.com/OpenDataDE/State-zip-code-GeoJSON/master/ma_massachusetts_zip_codes_geo.min.json') as response:
state_geo = json.load(response)
# load data with zip to population mapping
df = pd.read_csv('https://raw.githubusercontent.com/einblick-ai/data/main/mass-zip-population.csv')
df['zip'] = df['zip'].astype(str).str.pad(width=5, side='left', fillchar='0')
# compute centroids of each zip code
features = state_geo["features"]
centroids = {}
for feature in features:
s = shape(feature["geometry"])
centroids[feature['properties']['ZCTA5CE10']] = s.centroid
# map centroid to zip code in the df
df["zip_lat"] = 0
df["zip_lon"] = 0
for index, row in df.iterrows():
if row["zip"] in centroids:
df.loc[index, 'zip_lat'] = centroids[row["zip"]].y
df.loc[index, 'zip_lon'] = centroids[row["zip"]].x
# setup default view box
view = pdk.data_utils.compute_view(df[["zip_lon", "zip_lat"]])
view.pitch = 75
# create mapping from population to color
plasma = cm = plt.get_cmap('plasma')
cNorm = colors.Normalize(vmin=df["population"].min(), vmax=df["population"].max())
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=plasma)
df["color"] = df.apply (lambda row: scalarMap.to_rgba(row["population"]), axis=1)
# create column layer in pydeck
column_layer = pdk.Layer(
"ColumnLayer",
data=df,
get_position=["zip_lon", "zip_lat"],
get_elevation="population",
elevation_scale=1,
radius=300,
pickable=True,
get_fill_color="[color[0] * 255, color[1] * 255, color[2] * 255, color[3] * 255]",
auto_highlight=True,
)
# add tooltip
tooltip = {
"html": "<b>{population}</b> people in {zip}",
"style": {"background": "grey", "color": "white", "font-family": '"Helvetica Neue", Arial', "z-index": "10000"},
}
r = pdk.Deck(
column_layer,
initial_view_state=view,
tooltip=tooltip
)
r.to_html("column_layer.html", notebook_display=True)About
Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.