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Plotly and Datashader in Python

How to use datashader to rasterize large datasets, and visualize the generated raster data with plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

datashader creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.

Passing datashader rasters as a mapbox image layer

We visualize here the spatial distribution of taxi rides in New York City. A higher density is observed on major avenues. For more details about mapbox charts, see the mapbox layers tutorial. No mapbox token is needed here.

In [1]:
import pandas as pd
df = pd.read_csv('')
dff = df.query('Lat < 40.82').query('Lat > 40.70').query('Lon > -74.02').query('Lon < -73.91')

import datashader as ds
cvs = ds.Canvas(plot_width=1000, plot_height=1000)
agg = cvs.points(dff, x='Lon', y='Lat')
# agg is an xarray object, see for more details
coords_lat, coords_lon = agg.coords['Lat'].values, agg.coords['Lon'].values
# Corners of the image, which need to be passed to mapbox
coordinates = [[coords_lon[0], coords_lat[0]],
               [coords_lon[-1], coords_lat[0]],
               [coords_lon[-1], coords_lat[-1]],
               [coords_lon[0], coords_lat[-1]]]

from colorcet import fire
import datashader.transfer_functions as tf
img = tf.shade(agg, cmap=fire)[::-1].to_pil()

import as px
# Trick to create rapidly a figure with mapbox axes
fig = px.scatter_mapbox(dff[:1], lat='Lat', lon='Lon', zoom=12)
# Add the datashader image as a mapbox layer image
                 mapbox_layers = [
                    "sourcetype": "image",
                    "source": img,
                    "coordinates": coordinates

Exploring correlations of a large dataset

Here we explore the flight delay dataset from In order to get a visual impression of the correlation between features, we generate a datashader rasterized array which we plot using a Heatmap trace. It creates a much clearer visualization than a scatter plot of (even a fraction of) the data points, as shown below.

Note that instead of datashader it would theoretically be possible to create a 2d histogram with plotly but this is not recommended here because you would need to load the whole dataset (5M rows !) in the browser for plotly.js to compute the heatmap, which is practically not tractable. Datashader offers the possibility to reduce the size of the dataset before passing it to the browser.

In [2]:
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import datashader as ds
df = pd.read_parquet('')
fig = go.Figure(go.Scattergl(x=df['SCHEDULED_DEPARTURE'][::200],
fig.update_layout(title_text='A busy plot')
In [3]:
import as px
import pandas as pd
import numpy as np
import datashader as ds
df = pd.read_parquet('')

cvs = ds.Canvas(plot_width=100, plot_height=100)
zero_mask = agg.values == 0
agg.values = np.log10(agg.values, where=np.logical_not(zero_mask))
agg.values[zero_mask] = np.nan
fig = px.imshow(agg, origin='lower', labels={'color':'Log10(count)'})
fig.update_layout(coloraxis_colorbar=dict(title='Count', tickprefix='1.e'))