> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> Explore how to use W&B Tables with this 5 minute Quickstart.

# Tutorial: Log tables, visualize and query data

The following Quickstart demonstrates how to log data tables, visualize data, and query data.

Select the button below to try a PyTorch Quickstart example project on MNIST data.

## 1. Log a table

Log a table with W\&B. You can either construct a new table or pass a Pandas Dataframe.

<Tabs>
  <Tab title="Construct a table">
    To construct and log a new Table, you will use:

    * [`wandb.init()`](/models/ref/python/functions/init): Create a [run](/models/runs/) to track results.
    * [`wandb.Table()`](/models/ref/python/data-types/table): Create a new table object.
      * `columns`: Set the column names.
      * `data`: Set the contents of each row.
    * [`wandb.Run.log()`](/models/ref/python/experiments/run.md/#method-runlog): Log the table to save it to W\&B.

    Here's an example:

    ```python theme={null}
    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})
    ```
  </Tab>

  <Tab title="Pandas Dataframe">
    Pass a Pandas Dataframe to `wandb.Table()` to create a new table.

    ```python theme={null}
    import wandb
    import pandas as pd

    df = pd.read_csv("my_data.csv")

    with wandb.init(project="df-table") as run:
        # Create a new table from the DataFrame
        # and log it to W&B.
      my_table = wandb.Table(dataframe=df)
      run.log({"Table Name": my_table})
    ```

    For more information on supported data types, see the [`wandb.Table`](/models/ref/python/data-types/table) in the W\&B API Reference Guide.
  </Tab>
</Tabs>

## 2. Visualize tables in your project workspace

View the resulting table in your workspace.

1. Navigate to your project in the W\&B App.
2. Select the name of your run in your project workspace. A new panel is added for each unique table key.

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541/88iR80mZ8tuFCZUU/images/data_vis/wandb_demo_logged_sample_table.png?fit=max&auto=format&n=88iR80mZ8tuFCZUU&q=85&s=ea6c316c96db26caeba464622a9a7b0c" alt="Sample table logged" width="1762" height="880" data-path="images/data_vis/wandb_demo_logged_sample_table.png" />
</Frame>

In this example, `my_table`, is logged under the key `"Table Name"`.

## 3. Compare across model versions

Log sample tables from multiple W\&B Runs and compare results in the project workspace. In this [example workspace](https://wandb.ai/carey/table-test?workspace=user-carey), we show how to combine rows from multiple different versions in the same table.

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541/88iR80mZ8tuFCZUU/images/data_vis/wandb_demo_toggle_on_and_off_cross_run_comparisons_in_tables.gif?s=a1da4671329b7d038cda71c91c56045e" alt="Cross-run table comparison" width="1754" height="1026" data-path="images/data_vis/wandb_demo_toggle_on_and_off_cross_run_comparisons_in_tables.gif" />
</Frame>

Use the table filter, sort, and grouping features to explore and evaluate model results.

<Frame>
  <img src="https://mintcdn.com/wb-21fd5541/88iR80mZ8tuFCZUU/images/data_vis/wandb_demo_filter_on_a_table.png?fit=max&auto=format&n=88iR80mZ8tuFCZUU&q=85&s=21ac42731b4da719d0baef5bcfc03ad2" alt="Table filtering" width="1602" height="606" data-path="images/data_vis/wandb_demo_filter_on_a_table.png" />
</Frame>
