sdk_reference.mdx

---
output-file: nixtla_client.html
title: SDK Reference
icon: 'code'
---


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<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient

> ``` text
>  NixtlaClient (api_key:Optional[str]=None, base_url:Optional[str]=None,
>                timeout:Optional[int]=60, max_retries:int=6,
>                retry_interval:int=10, max_wait_time:int=360)
> ```

*Client to interact with the Nixtla API.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| api_key | Optional | None | The authorization api_key interacts with the Nixtla API.<br/>If not provided, will use the NIXTLA_API_KEY environment variable. |
| base_url | Optional | None | Custom base_url.<br/>If not provided, will use the NIXTLA_BASE_URL environment variable. |
| timeout | Optional | 60 | Request timeout in seconds. Set this to `None` to disable it. |
| max_retries | int | 6 | The maximum number of attempts to make when calling the API before giving up.<br/>It defines how many times the client will retry the API call if it fails.<br/>Default value is 6, indicating the client will attempt the API call up to 6 times in total |
| retry_interval | int | 10 | The interval in seconds between consecutive retry attempts.<br/>This is the waiting period before the client tries to call the API again after a failed attempt.<br/>Default value is 10 seconds, meaning the client waits for 10 seconds between retries. |
| max_wait_time | int | 360 | The maximum total time in seconds that the client will spend on all retry attempts before giving up.<br/>This sets an upper limit on the cumulative waiting time for all retry attempts.<br/>If this time is exceeded, the client will stop retrying and raise an exception.<br/>Default value is 360 seconds, meaning the client will cease retrying if the total time<br/>spent on retries exceeds 360 seconds.<br/>The client throws a ReadTimeout error after 60 seconds of inactivity. If you want to<br/>catch these errors, use max_wait_time \>\> 60. |

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<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.validate_api_key

> ``` text
>  NixtlaClient.validate_api_key (log:bool=True)
> ```

*Check API key status.*

|             | **Type** | **Default** | **Details**                   |
|-------------|----------|-------------|-------------------------------|
| log         | bool     | True        | Show the endpoint’s response. |
| **Returns** | **bool** |             | **Whether API key is valid.** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.forecast

> ``` text
>  NixtlaClient.forecast (df:~AnyDFType, h:typing.Annotated[int,Gt(gt=0)],
>                         freq:Union[str,int,pandas._libs.tslibs.offsets.Bas
>                         eOffset,NoneType]=None, id_col:str='unique_id',
>                         time_col:str='ds', target_col:str='y',
>                         X_df:Optional[~AnyDFType]=None,
>                         level:Optional[list[Union[int,float]]]=None,
>                         quantiles:Optional[list[float]]=None,
>                         finetune_steps:typing.Annotated[int,Ge(ge=0)]=0,
>                         finetune_depth:Literal[1,2,3,4,5]=1, finetune_loss
>                         :Literal['default','mae','mse','rmse','mape','smap
>                         e']='default',
>                         finetuned_model_id:Optional[str]=None,
>                         clean_ex_first:bool=True,
>                         hist_exog_list:Optional[list[str]]=None,
>                         validate_api_key:bool=False,
>                         add_history:bool=False, date_features:Union[bool,l
>                         ist[Union[str,Callable]]]=False, date_features_to_
>                         one_hot:Union[bool,list[str]]=False, model:Literal
>                         ['azureai','timegpt-1','timegpt-1-long-
>                         horizon']='timegpt-1', num_partitions:Optional[Ann
>                         otated[int,Gt(gt=0)]]=None,
>                         feature_contributions:bool=False)
> ```

*Forecast your time series using TimeGPT.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| df | AnyDFType |  | The DataFrame on which the function will operate. Expected to contain at least the following columns:<br/>- time_col:<br/> Column name in `df` that contains the time indices of the time series. This is typically a datetime<br/> column with regular intervals, e.g., hourly, daily, monthly data points.<br/>- target_col:<br/> Column name in `df` that contains the target variable of the time series, i.e., the variable we<br/> wish to predict or analyze.<br/>Additionally, you can pass multiple time series (stacked in the dataframe) considering an additional column:<br/>- id_col:<br/> Column name in `df` that identifies unique time series. Each unique value in this column<br/> corresponds to a unique time series. |
| h | Annotated |  | Forecast horizon. |
| freq | Union | None | Frequency of the timestamps. If `None`, it will be inferred automatically.<br/>See [pandas’ available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). |
| id_col | str | unique_id | Column that identifies each series. |
| 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. |
| X_df | Optional | None | DataFrame with \[`unique_id`, `ds`\] columns and `df`’s future exogenous. |
| level | Optional | None | Confidence levels between 0 and 100 for prediction intervals. |
| quantiles | Optional | None | Quantiles to forecast, list between (0, 1).<br/>`level` and `quantiles` should not be used simultaneously.<br/>The output dataframe will have the quantile columns<br/>formatted as TimeGPT-q-(100 \* q) for each q.<br/>100 \* q represents percentiles but we choose this notation<br/>to avoid having dots in column names. |
| finetune_steps | Annotated | 0 | Number of steps used to finetune learning TimeGPT in the<br/>new data. |
| finetune_depth | Literal | 1 | The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning,<br/>and 5 means that the entire model is finetuned. |
| finetune_loss | Literal | default | Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`. |
| finetuned_model_id | Optional | None | ID of previously fine-tuned model to use. |
| clean_ex_first | bool | True | Clean exogenous signal before making forecasts using TimeGPT. |
| hist_exog_list | Optional | None | Column names of the historical exogenous features. |
| validate_api_key | bool | False | If True, validates api_key before sending requests. |
| add_history | bool | False | Return fitted values of the model. |
| date_features | Union | False | Features computed from the dates.<br/>Can be pandas date attributes or functions that will take the dates as input.<br/>If True automatically adds most used date features for the<br/>frequency of `df`. |
| date_features_to_one_hot | Union | False | Apply one-hot encoding to these date features.<br/>If `date_features=True`, then all date features are<br/>one-hot encoded by default. |
| model | Literal | timegpt-1 | Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`.<br/>We recommend using `timegpt-1-long-horizon` for forecasting<br/>if you want to predict more than one seasonal<br/>period given the frequency of your data. |
| num_partitions | Optional | None | Number of partitions to use.<br/>If None, the number of partitions will be equal<br/>to the available parallel resources in distributed environments. |
| feature_contributions | bool | False |  |
| **Returns** | **AnyDFType** |  | **DataFrame with TimeGPT forecasts for point predictions and probabilistic<br/>predictions (if level is not None).** |

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<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.cross_validation

> ``` text
>  NixtlaClient.cross_validation (df:~AnyDFType,
>                                 h:typing.Annotated[int,Gt(gt=0)], freq:Uni
>                                 on[str,int,pandas._libs.tslibs.offsets.Bas
>                                 eOffset,NoneType]=None,
>                                 id_col:str='unique_id', time_col:str='ds',
>                                 target_col:str='y', level:Optional[list[Un
>                                 ion[int,float]]]=None,
>                                 quantiles:Optional[list[float]]=None,
>                                 validate_api_key:bool=False, n_windows:typ
>                                 ing.Annotated[int,Gt(gt=0)]=1, step_size:O
>                                 ptional[Annotated[int,Gt(gt=0)]]=None, fin
>                                 etune_steps:typing.Annotated[int,Ge(ge=0)]
>                                 =0, finetune_depth:Literal[1,2,3,4,5]=1, f
>                                 inetune_loss:Literal['default','mae','mse'
>                                 ,'rmse','mape','smape']='default',
>                                 finetuned_model_id:Optional[str]=None,
>                                 refit:bool=True, clean_ex_first:bool=True,
>                                 hist_exog_list:Optional[list[str]]=None,
>                                 date_features:Union[bool,list[str]]=False,
>                                 date_features_to_one_hot:Union[bool,list[s
>                                 tr]]=False, model:Literal['azureai','timeg
>                                 pt-1','timegpt-1-long-
>                                 horizon']='timegpt-1', num_partitions:Opti
>                                 onal[Annotated[int,Gt(gt=0)]]=None)
> ```

*Perform cross validation in your time series using TimeGPT.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| df | AnyDFType |  | The DataFrame on which the function will operate. Expected to contain at least the following columns:<br/>- time_col:<br/> Column name in `df` that contains the time indices of the time series. This is typically a datetime<br/> column with regular intervals, e.g., hourly, daily, monthly data points.<br/>- target_col:<br/> Column name in `df` that contains the target variable of the time series, i.e., the variable we<br/> wish to predict or analyze.<br/>Additionally, you can pass multiple time series (stacked in the dataframe) considering an additional column:<br/>- id_col:<br/> Column name in `df` that identifies unique time series. Each unique value in this column<br/> corresponds to a unique time series. |
| h | Annotated |  | Forecast horizon. |
| freq | Union | None | Frequency of the timestamps. If `None`, it will be inferred automatically.<br/>See [pandas’ available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). |
| id_col | str | unique_id | Column that identifies each series. |
| 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. |
| level | Optional | None | Confidence level between 0 and 100 for prediction intervals. |
| quantiles | Optional | None | Quantiles to forecast, list between (0, 1).<br/>`level` and `quantiles` should not be used simultaneously.<br/>The output dataframe will have the quantile columns<br/>formatted as TimeGPT-q-(100 \* q) for each q.<br/>100 \* q represents percentiles but we choose this notation<br/>to avoid having dots in column names. |
| validate_api_key | bool | False | If True, validates api_key before sending requests. |
| n_windows | Annotated | 1 | Number of windows to evaluate. |
| step_size | Optional | None | Step size between each cross validation window. If None it will be equal to `h`. |
| finetune_steps | Annotated | 0 | Number of steps used to finetune TimeGPT in the<br/>new data. |
| finetune_depth | Literal | 1 | The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning,<br/>and 5 means that the entire model is finetuned. |
| finetune_loss | Literal | default | Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`. |
| finetuned_model_id | Optional | None | ID of previously fine-tuned model to use. |
| refit | bool | True | Fine-tune the model in each window. If `False`, only fine-tunes on the first window.<br/>Only used if `finetune_steps` \> 0. |
| clean_ex_first | bool | True | Clean exogenous signal before making forecasts using TimeGPT. |
| hist_exog_list | Optional | None | Column names of the historical exogenous features. |
| date_features | Union | False | Features computed from the dates.<br/>Can be pandas date attributes or functions that will take the dates as input.<br/>If True automatically adds most used date features for the<br/>frequency of `df`. |
| date_features_to_one_hot | Union | False | Apply one-hot encoding to these date features.<br/>If `date_features=True`, then all date features are<br/>one-hot encoded by default. |
| model | Literal | timegpt-1 | Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`.<br/>We recommend using `timegpt-1-long-horizon` for forecasting<br/>if you want to predict more than one seasonal<br/>period given the frequency of your data. |
| num_partitions | Optional | None | Number of partitions to use.<br/>If None, the number of partitions will be equal<br/>to the available parallel resources in distributed environments. |
| **Returns** | **AnyDFType** |  | **DataFrame with cross validation forecasts.** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.detect_anomalies

> ``` text
>  NixtlaClient.detect_anomalies (df:~AnyDFType,
>                                 freq:Union[str,int,pandas._libs.tslibs.off
>                                 sets.BaseOffset,NoneType]=None,
>                                 id_col:str='unique_id', time_col:str='ds',
>                                 target_col:str='y',
>                                 level:Union[int,float]=99,
>                                 finetuned_model_id:Optional[str]=None,
>                                 clean_ex_first:bool=True,
>                                 validate_api_key:bool=False,
>                                 date_features:Union[bool,list[str]]=False,
>                                 date_features_to_one_hot:Union[bool,list[s
>                                 tr]]=False, model:Literal['azureai','timeg
>                                 pt-1','timegpt-1-long-
>                                 horizon']='timegpt-1', num_partitions:Opti
>                                 onal[Annotated[int,Gt(gt=0)]]=None)
> ```

*Detect anomalies in your time series using TimeGPT.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| df | AnyDFType |  | The DataFrame on which the function will operate. Expected to contain at least the following columns:<br/>- time_col:<br/> Column name in `df` that contains the time indices of the time series. This is typically a datetime<br/> column with regular intervals, e.g., hourly, daily, monthly data points.<br/>- target_col:<br/> Column name in `df` that contains the target variable of the time series, i.e., the variable we<br/> wish to predict or analyze.<br/>Additionally, you can pass multiple time series (stacked in the dataframe) considering an additional column:<br/>- id_col:<br/> Column name in `df` that identifies unique time series. Each unique value in this column<br/> corresponds to a unique time series. |
| freq | Union | None | Frequency of the timestamps. If `None`, it will be inferred automatically.<br/>See [pandas’ available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). |
| id_col | str | unique_id | Column that identifies each series. |
| 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. |
| level | Union | 99 | Confidence level between 0 and 100 for detecting the anomalies. |
| finetuned_model_id | Optional | None | ID of previously fine-tuned model to use. |
| clean_ex_first | bool | True | Clean exogenous signal before making forecasts<br/>using TimeGPT. |
| validate_api_key | bool | False | If True, validates api_key before sending requests. |
| date_features | Union | False | Features computed from the dates.<br/>Can be pandas date attributes or functions that will take the dates as input.<br/>If True automatically adds most used date features for the<br/>frequency of `df`. |
| date_features_to_one_hot | Union | False | Apply one-hot encoding to these date features.<br/>If `date_features=True`, then all date features are<br/>one-hot encoded by default. |
| model | Literal | timegpt-1 | Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`.<br/>We recommend using `timegpt-1-long-horizon` for forecasting<br/>if you want to predict more than one seasonal<br/>period given the frequency of your data. |
| num_partitions | Optional | None | Number of partitions to use.<br/>If None, the number of partitions will be equal<br/>to the available parallel resources in distributed environments. |
| **Returns** | **AnyDFType** |  | **DataFrame with anomalies flagged by TimeGPT.** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.usage

> ``` text
>  NixtlaClient.usage ()
> ```

*Query consumed requests and limits*

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.finetune

> ``` text
>  NixtlaClient.finetune
>                         (df:Union[pandas.core.frame.DataFrame,polars.dataf
>                         rame.frame.DataFrame], freq:Union[str,int,pandas._
>                         libs.tslibs.offsets.BaseOffset,NoneType]=None,
>                         id_col:str='unique_id', time_col:str='ds',
>                         target_col:str='y',
>                         finetune_steps:typing.Annotated[int,Ge(ge=0)]=10,
>                         finetune_depth:Literal[1,2,3,4,5]=1, finetune_loss
>                         :Literal['default','mae','mse','rmse','mape','smap
>                         e']='default', output_model_id:Optional[str]=None,
>                         finetuned_model_id:Optional[str]=None, model:Liter
>                         al['azureai','timegpt-1','timegpt-1-long-
>                         horizon']='timegpt-1')
> ```

*Fine-tune TimeGPT to your series.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| df | Union |  | The DataFrame on which the function will operate. Expected to contain at least the following columns:<br/>- time_col:<br/> Column name in `df` that contains the time indices of the time series. This is typically a datetime<br/> column with regular intervals, e.g., hourly, daily, monthly data points.<br/>- target_col:<br/> Column name in `df` that contains the target variable of the time series, i.e., the variable we<br/> wish to predict or analyze.<br/>Additionally, you can pass multiple time series (stacked in the dataframe) considering an additional column:<br/>- id_col:<br/> Column name in `df` that identifies unique time series. Each unique value in this column<br/> corresponds to a unique time series. |
| freq | Union | None | Frequency of the timestamps. If `None`, it will be inferred automatically.<br/>See [pandas’ available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). |
| id_col | str | unique_id | Column that identifies each series. |
| 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. |
| finetune_steps | Annotated | 10 | Number of steps used to finetune learning TimeGPT in the new data. |
| finetune_depth | Literal | 1 | The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning,<br/>and 5 means that the entire model is finetuned. |
| finetune_loss | Literal | default | Loss function to use for finetuning. Options are: `default`, `mae`, `mse`, `rmse`, `mape`, and `smape`. |
| output_model_id | Optional | None | ID to assign to the fine-tuned model. If `None`, an UUID is used. |
| finetuned_model_id | Optional | None | ID of previously fine-tuned model to use as base. |
| model | Literal | timegpt-1 | Model to use as a string. Options are: `timegpt-1`, and `timegpt-1-long-horizon`.<br/>We recommend using `timegpt-1-long-horizon` for forecasting<br/>if you want to predict more than one seasonal<br/>period given the frequency of your data. |
| **Returns** | **str** |  | **ID of the fine-tuned model** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.finetuned_models

> ``` text
>  NixtlaClient.finetuned_models (as_df:bool=False)
> ```

*List fine-tuned models*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| as_df | bool | False | Return the fine-tuned models as a pandas dataframe |
| **Returns** | **Union** |  | **List of available fine-tuned models.** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.finetuned_model

> ``` text
>  NixtlaClient.finetuned_model (finetuned_model_id:str)
> ```

*Get fine-tuned model metadata*

|  | **Type** | **Details** |
|--------|---------------------------|-------------------------------------|
| finetuned_model_id | str | ID of the fine-tuned model to get metadata from. |
| **Returns** | **FinetunedModel** | **Fine-tuned model metadata.** |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.delete_finetuned_model

> ``` text
>  NixtlaClient.delete_finetuned_model (finetuned_model_id:str)
> ```

*Delete a previously fine-tuned model*

|                    | **Type** | **Details**                               |
|--------------------|----------|-------------------------------------------|
| finetuned_model_id | str      | ID of the fine-tuned model to be deleted. |
| **Returns**        | **bool** | **Whether delete was successful.**        |

------------------------------------------------------------------------

<a
href="https://github.com/Nixtla/nixtla/blob/main/nixtla/nixtla_client.py#LNone"
target="_blank" style={{ float: "right", fontSize: "smaller" }}>source</a>

## NixtlaClient.plot

> ``` text
>  NixtlaClient.plot (df:Union[pandas.core.frame.DataFrame,polars.dataframe.
>                     frame.DataFrame,NoneType]=None, forecasts_df:Union[pan
>                     das.core.frame.DataFrame,polars.dataframe.frame.DataFr
>                     ame,NoneType]=None, id_col:str='unique_id',
>                     time_col:str='ds', target_col:str='y', unique_ids:Unio
>                     n[list[str],NoneType,numpy.ndarray]=None,
>                     plot_random:bool=True, max_ids:int=8,
>                     models:Optional[list[str]]=None,
>                     level:Optional[list[Union[int,float]]]=None,
>                     max_insample_length:Optional[int]=None,
>                     plot_anomalies:bool=False,
>                     engine:Literal['matplotlib','plotly','plotly-
>                     resampler']='matplotlib',
>                     resampler_kwargs:Optional[dict]=None, ax:Union[Forward
>                     Ref('plt.Axes'),numpy.ndarray,ForwardRef('plotly.graph
>                     _objects.Figure'),NoneType]=None)
> ```

*Plot forecasts and insample values.*

|  | **Type** | **Default** | **Details** |
|------|------------------|-------------------------|-------------------------|
| df | Union | None | The DataFrame on which the function will operate. Expected to contain at least the following columns:<br/>- time_col:<br/> Column name in `df` that contains the time indices of the time series. This is typically a datetime<br/> column with regular intervals, e.g., hourly, daily, monthly data points.<br/>- target_col:<br/> Column name in `df` that contains the target variable of the time series, i.e., the variable we<br/> wish to predict or analyze.<br/>Additionally, you can pass multiple time series (stacked in the dataframe) considering an additional column:<br/>- id_col:<br/> Column name in `df` that identifies unique time series. Each unique value in this column<br/> corresponds to a unique time series. |
| forecasts_df | Union | None | DataFrame with columns \[`unique_id`, `ds`\] and models. |
| id_col | str | unique_id | Column that identifies each series. |
| 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. |
| unique_ids | Union | None | Time Series to plot.<br/>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 | list of models to plot. |
| level | Optional | None | list of prediction intervals to plot if paseed. |
| max_insample_length | Optional | None | Max number of train/insample observations to be plotted. |
| plot_anomalies | bool | False | Plot anomalies for each prediction interval. |
| engine | Literal | matplotlib | Library used to plot. ‘matplotlib’, ‘plotly’ or ‘plotly-resampler’. |
| resampler_kwargs | Optional | None | Kwargs to be passed to plotly-resampler constructor.<br/>For further custumization (“show_dash”) call the method,<br/>store the plotting object and add the extra arguments to<br/>its `show_dash` method. |
| ax | Union | None | Object where plots will be added. |