> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Plotting
> Time series visualizations
### `plot_series`
```python theme={null}
plot_series(df=None, forecasts_df=None, ids=None, plot_random=True, max_ids=8, models=None, level=None, max_insample_length=None, plot_anomalies=False, engine='matplotlib', palette=None, id_col='unique_id', time_col='ds', target_col='y', seed=0, resampler_kwargs=None, ax=None)
```
Plot forecasts and insample values.
**Parameters:**
| Name | Type | Description | Default |
| --------------------- | ----------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- |
| `df` | pandas or polars DataFrame | DataFrame with columns \[`id_col`, `time_col`, `target_col`]. Defaults to None. | None |
| `forecasts_df` | pandas or polars DataFrame | DataFrame with columns \[`id_col`, `time_col`] and models. Defaults to None. | None |
| `ids` | list of str | Time Series to plot. If None, time series are selected randomly. Defaults to None. | None |
| `plot_random` | [bool](#bool) | Select time series to plot randomly. Defaults to True. | True |
| `max_ids` | [int](#int) | Maximum number of ids to plot. Defaults to 8. | 8 |
| `models` | list of str | Models to plot. Defaults to None. | None |
| `level` | list of float | Prediction intervals to plot. Defaults to None. | None |
| `max_insample_length` | [int](#int) | Maximum number of train/insample observations to be plotted. Defaults to None. | None |
| `plot_anomalies` | [bool](#bool) | Plot anomalies for each prediction interval. Defaults to False. | False |
| `engine` | [str](#str) | Library used to plot. 'plotly', 'plotly-resampler' or 'matplotlib'. Defaults to 'matplotlib'. | 'matplotlib' |
| `palette` | [str](#str) | Name of the matplotlib colormap to use for the plots. If None, uses the current style. Defaults to None. | None |
| `id_col` | [str](#str) | Column that identifies each serie. Defaults to 'unique\_id'. | 'unique\_id' |
| `time_col` | [str](#str) | Column that identifies each timestep, its values can be timestamps or integers. Defaults to 'ds'. | 'ds' |
| `target_col` | [str](#str) | Column that contains the target. Defaults to 'y'. | 'y' |
| `seed` | [int](#int) | Seed used for the random number generator. Only used if plot\_random is True. Defaults to 0. | 0 |
| `resampler_kwargs` | [dict](#dict) | Keyword arguments to be passed to plotly-resampler constructor. For further custumization ("show\_dash") call the method, store the plotting object and add the extra arguments to its `show_dash` method. Defaults to None. | None |
| `ax` | matplotlib axes, array of matplotlib axes or plotly Figure | Object where plots will be added. Defaults to None. | None |
**Returns:**
| Type | Description |
| ------------------------------------------ | ----------- |
| matplotlib or plotly figure: Plot's figure | |
```python theme={null}
from utilsforecast.data import generate_series
```
```python theme={null}
level = [80, 95]
series = generate_series(4, freq='D', equal_ends=True, with_trend=True, n_models=2, level=level)
test_pd = series.groupby('unique_id', observed=True).tail(10).copy()
train_pd = series.drop(test_pd.index)
```
```python theme={null}
plt.style.use('ggplot')
fig = plot_series(
train_pd,
forecasts_df=test_pd,
ids=[0, 3],
plot_random=False,
level=level,
max_insample_length=50,
engine='matplotlib',
plot_anomalies=True,
)
fig.savefig('imgs/plotting.png', bbox_inches='tight')
```
