> ## 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.
# line()
export const GitHubLink = ({url}) =>
GitHub source
;
### function `line`
```python theme={null}
line(
table: 'wandb.Table',
x: 'str',
y: 'str',
stroke: 'str | None' = None,
title: 'str' = '',
split_table: 'bool' = False
) → CustomChart
```
Constructs a customizable line chart.
**Args:**
* `table`: The table containing data for the chart.
* `x`: Column name for the x-axis values.
* `y`: Column name for the y-axis values.
* `stroke`: Column name to differentiate line strokes (e.g., for grouping lines).
* `title`: Title of the 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 math
import random
import wandb
# Create multiple series of data with different patterns
data = []
for i in range(100):
# Series 1: Sinusoidal pattern with random noise
data.append([i, math.sin(i / 10) + random.uniform(-0.1, 0.1), "series_1"])
# Series 2: Cosine pattern with random noise
data.append([i, math.cos(i / 10) + random.uniform(-0.1, 0.1), "series_2"])
# Series 3: Linear increase with random noise
data.append([i, i / 10 + random.uniform(-0.5, 0.5), "series_3"])
# Define the columns for the table
table = wandb.Table(data=data, columns=["step", "value", "series"])
# Initialize wandb run and log the line chart
with wandb.init(project="line_chart_example") as run:
line_chart = wandb.plot.line(
table=table,
x="step",
y="value",
stroke="series", # Group by the "series" column
title="Multi-Series Line Plot",
)
run.log({"line-chart": line_chart})
```