# WebGL vs SVG in Python

Implement WebGL for increased speed, improved interactivity, and the ability to plot even more data!

If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a
Workspace Jupyter notebook.

Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.

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## 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.

Here we show that it is possible to represent millions of points with WebGL. For larger datasets, or for a clearer visualization of the density of points, it is also possible to use datashader.

#### Compare WebGL and SVG¶

Checkout this notebook to compare WebGL and SVG scatter plots with 75,000 random data points

#### WebGL with Plotly Express¶

The `rendermode`

argument to supported Plotly Express functions can be used to enable WebGL rendering.

Here is an example that creates a 100,000 point scatter plot using Plotly Express with WebGL rendering enabled.

```
import plotly.express as px
import pandas as pd
import numpy as np
np.random.seed(1)
N = 100000
df = pd.DataFrame(dict(x=np.random.randn(N),
y=np.random.randn(N)))
fig = px.scatter(df, x="x", y="y", render_mode='webgl')
fig.update_traces(marker_line=dict(width=1, color='DarkSlateGray'))
fig.show()
```