> ## 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.
# bar()
export const GitHubLink = ({url}) =>
GitHub source
;
### function `bar`
```python theme={null}
bar(
table: 'wandb.Table',
label: 'str',
value: 'str',
title: 'str' = '',
split_table: 'bool' = False
) → CustomChart
```
Constructs a bar chart from a wandb.Table of data.
**Args:**
* `table`: A table containing the data for the bar chart.
* `label`: The name of the column to use for the labels of each bar.
* `value`: The name of the column to use for the values of each bar.
* `title`: The title of the bar chart.
* `split_table`: Whether the table should be split into a separate section in the W\&B UI. If `True`, the table will be displayed in a section named "Custom Chart Tables". Default is `False`.
**Returns:**
* `CustomChart`: A custom chart object that can be logged to W\&B. To log the chart, pass it to `wandb.log()`.
**Example:**
```python theme={null}
import random
import wandb
# Generate random data for the table
data = [
["car", random.uniform(0, 1)],
["bus", random.uniform(0, 1)],
["road", random.uniform(0, 1)],
["person", random.uniform(0, 1)],
]
# Create a table with the data
table = wandb.Table(data=data, columns=["class", "accuracy"])
# Initialize a W&B run and log the bar plot
with wandb.init(project="bar_chart") as run:
# Create a bar plot from the table
bar_plot = wandb.plot.bar(
table=table,
label="class",
value="accuracy",
title="Object Classification Accuracy",
)
# Log the bar chart to W&B
run.log({"bar_plot": bar_plot})
```