> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-update-regex-mention.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# View Eval Tables
When you create an Eval Table, specify which columns contain inputs, outputs, and scores. W\&B groups the columns into corresponding sections in the Eval Table.
The following example shows the basic structure of an EvalTable object:
```python theme={null}
import wandb
wandb.EvalTable(
input_columns=["column1", "column2"],
output_columns=["output_column1", "output_column2"],
score_columns=["score_column1", "score_column2"],
)
```
Pass one or more column names to each of the following arguments:
* `input_columns`: Data provided to the model, such as an image, prompt, or ground-truth label.
* `output_columns`: Values produced by the model, such as a prediction or generated response.
* `score_columns`: Metrics or other values used to evaluate the output.
W\&B displays these columns in the Inputs, Outputs, and Scores sections of the Eval Table.
The examples on this page use code from [Create an Eval Table](/models/evaltables/create-an-evaluation-table). Expand **View code** to view or copy the complete example.
```python theme={null}
import pandas as pd
import wandb
df = pd.DataFrame(
[
{
"image_id": "img_001",
"true_label": "cat",
"predicted_label": "cat",
"confidence": 0.97,
"correct": True,
},
{
"image_id": "img_002",
"true_label": "dog",
"predicted_label": "cat",
"confidence": 0.72,
"correct": False,
},
{
"image_id": "img_003",
"true_label": "car",
"predicted_label": "car",
"confidence": 0.89,
"correct": True,
},
]
)
df["correct"] = df["correct"].astype(object) # preserves native Python bools
with wandb.init(project="classifier-eval-table-demo") as run:
eval_table = wandb.EvalTable(
dataframe=df,
input_columns=["image_id", "true_label"],
output_columns=["predicted_label"],
score_columns=["correct", "confidence"],
)
run.log({"validation_predictions_eval": eval_table})
```
For example, consider the code example from [Create an Eval Table](/models/evaltables/create-an-evaluation-table):
```python theme={null}
import wandb
with wandb.init(project="classifier-eval-table-demo") as run:
eval_table = wandb.EvalTable(
dataframe=df,
input_columns=["image_id", "true_label"],
output_columns=["predicted_label"],
score_columns=["correct", "confidence"],
)
run.log({"validation_predictions_eval": eval_table})
```
In that example:
* The **Inputs** section contains the `image_id` and `true_label` columns.
* The **Outputs** section contains the `predicted_label` column.
* The **Scores** section contains the `correct` and `confidence` columns.
The following image highlights these sections:
## Detail view
Select a row to open a detailed view of that example. The detail view shows the example's inputs, outputs, and scores.
The following image shows the detail view for the first row in the Eval Table:
Use the detail view to inspect examples when the table contains many columns or when you compare results across multiple runs.
Select the up or down arrow above the **Inputs** section to move between examples in the current table view.
## Filter data
Use filters to display only rows that match specific conditions.
To add a filter:
1. Select the **Filter** button above the table.
2. From the dropdown, select the column to filter.
3. Select an operator.
4. Specify a value.
W\&B automatically applies the filter to the table and displays only rows that match the condition.
You can apply multiple filters. To remove a filter, select the `X` next to it.
## Show or hide columns
Use the **Columns** menu to control which columns appear in the Eval Table. The available columns depend on the data logged to the table.
To show or hide a column:
1. Select **Columns** above the table.
2. Select a column to show it, or clear the column to hide it.
The following image shows a Columns menu in which all available columns are selected:
