Tutorial: Log tables, visualize and query data
2 minute read
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.
To construct and log a new Table, you will use:
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 each row.
run.log()
: Log the table to save it to W&B.
Here’s an example:
import wandb
run = wandb.init(project="table-test")
# Create and log a new table.
my_table = wandb.Table(columns=["a", "b"], data=[["a1", "b1"], ["a2", "b2"]])
run.log({"Table Name": my_table})
Pass a Pandas Dataframe to wandb.Table()
to create a new table.
import wandb
import pandas as pd
df = pd.read_csv("my_data.csv")
run = wandb.init(project="df-table")
my_table = wandb.Table(dataframe=df)
wandb.log({"Table Name": my_table})
For more information on supported data types, see the wandb.Table
in the W&B API Reference Guide.
2. Visualize tables in your project workspace
View the resulting table in your workspace.
- Navigate to your project in the W&B App.
- Select the name of your run in your project workspace. A new panel is added for each unique table key.
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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, we show how to combine rows from multiple different versions in the same table.
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Use the table filter, sort, and grouping features to explore and evaluate model results.
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