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This Quickstart walks you through logging data tables to W&B, visualizing them in your project workspace, and comparing results across runs. By the end, you have a logged table that you can explore and compare in the W&B App. Select the button below to try a PyTorch Quickstart example project on MNIST data.

Log a table

In this step, you create a table and log it to W&B so that it’s available for visualization later in the walkthrough. You can either construct a new table or pass a Pandas DataFrame.
To construct and log a new table, use the following:Here’s an example:
import wandb

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

Visualize tables in your project workspace

After logging a table, you can view it in the W&B App to confirm W&B recorded it correctly and to explore its contents.
  1. Navigate to your project in the W&B App.
  2. Select the name of your run in your project workspace. W&B adds a new panel for each unique table key.
Sample table logged
In this example, my_table is logged under the key "Table Name".

Compare across model versions

After you have logged tables from more than one run, you can use the project workspace to compare results side by side and evaluate how model versions differ. Log sample tables from multiple W&B Runs and compare results in the project workspace. This example workspace shows how to combine rows from multiple different versions in the same table.
Cross-run table comparison
Use the table filter, sort, and grouping features to explore and evaluate model results.
Table filtering