Tables

Use W&B Tables to visualize and query tabular data. For example:

• Compare how different models perform on the same test set
• Identify patterns in your data
• Look at sample model predictions visually
• Query to find commonly misclassified examples

The above image shows a table with semantic segmentation and custom metrics. View this table here in this sample project from the W&B ML Course.

How it works

A Table is a two-dimensional grid of data where each column has a single type of data. Tables support primitive and numeric types, as well as nested lists, dictionaries, and rich media types.

Log a Table

Log a table with a few lines of code:

• wandb.init(): Create a run to track results.
• wandb.Table(): Create a new table object.
  • columns: Set the column names.
  • data: Set the contents of the table.
• run.log(): Log the table to save it to W&B.

import wandb

run = wandb.init(project="table-test")
my_table = wandb.Table(columns=["a", "b"], data=[["a1", "b1"], ["a2", "b2"]])
run.log({"Table Name": my_table})

How to get started

• Quickstart: Learn to log data tables, visualize data, and query data.
• Tables Gallery: See example use cases for Tables.