> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
> Time series visualizations
# Plotting
***
source
### plot\_series
> ```text theme={null}
> plot_series (df:Optional[~DFType]=None,
> forecasts_df:Optional[~DFType]=None,
> ids:Optional[List[str]]=None, plot_random:bool=True,
> max_ids:int=8, models:Optional[List[str]]=None,
> level:Optional[List[float]]=None,
> max_insample_length:Optional[int]=None,
> plot_anomalies:bool=False, engine:str='matplotlib',
> palette:Optional[str]=None, id_col:str='unique_id',
> time_col:str='ds', target_col:str='y', seed:int=0,
> resampler_kwargs:Optional[Dict]=None, ax:Union[matplotlib.ax
> es._axes.Axes,numpy.ndarray,ForwardRef('plotly.graph_objects
> .Figure'),NoneType]=None)
> ```
*Plot forecasts and insample values.*
| | **Type** | **Default** | **Details** |
| --------------------- | ------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| df | Optional | None | DataFrame with columns \[`id_col`, `time_col`, `target_col`]. |
| forecasts\_df | Optional | None | DataFrame with columns \[`id_col`, `time_col`] and models. |
| ids | Optional | None | Time Series to plot.
If None, time series are selected randomly. |
| plot\_random | bool | True | Select time series to plot randomly. |
| max\_ids | int | 8 | Maximum number of ids to plot. |
| models | Optional | None | Models to plot. |
| level | Optional | None | Prediction intervals to plot. |
| max\_insample\_length | Optional | None | Maximum number of train/insample observations to be plotted. |
| plot\_anomalies | bool | False | Plot anomalies for each prediction interval. |
| engine | str | matplotlib | Library used to plot. ‘plotly’, ‘plotly-resampler’ or ‘matplotlib’. |
| palette | Optional | None | Name of the matplotlib colormap to use for the plots. If None, uses the current style. |
| id\_col | str | unique\_id | Column that identifies each serie. |
| time\_col | str | ds | Column that identifies each timestep, its values can be timestamps or integers. |
| target\_col | str | y | Column that contains the target. |
| seed | int | 0 | Seed used for the random number generator. Only used if plot\_random is True. |
| resampler\_kwargs | Optional | None | 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. |
| ax | Union | None | Object where plots will be added. |
| **Returns** | **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')
```
