Weave

Weave is a visual development environment designed for building AI-powered software. It is also an open-source, interactive analytics toolkit for performant data exploration.

Use Weave to:

Spend less time waiting for datasets to load and more time exploring data, deriving insights, and building powerful data analytics
Interactively explore your data. Work with your data visually and dynamically to discover patterns that static graphs can not reveal, without using complicated APIs.
Monitor AI applications and models in production with real-time metrics, customizable visualizations, and interactive analysis.
Generate Boards to address common use cases when monitoring production models and working with LLMs

For more information about Weave, see the Weave Github Repo. To learn how to write your own queries interactively, see this report.

How it works

Use Weave to view your dataframe in your notebook with only a few lines of code:

First, install or update to the latest version of Weave with pip:

pip install weave --upgrade

Load your dataframe into your notebook.
View your dataframe with weave.show.

weave.ipynb
import weave
from sklearn.datasets import load_iris

# We load in the iris dataset for demonstrative purposes
iris = load_iris(as_frame=True)
df = iris.data.assign(target=iris.target_names[iris.target])

weave.show(df)

An interactive weave dashboard will appear, similar to the animation shown below:

The following animations show how you can interactively plot charts and publish your dashboard to share with your colleagues:

Plot a chart

Hover your mouse next to a panel and click Add a new panel.
Copy the Weave Expression for the dataset you want to plot. This Weave Expression is the path/location of the dataset object in the Weave compute graph.
Click on Table to change this Weave Panel type.
From the dropdown, select Plot.