> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/sdv/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.sdv.dev/sdv/sampling/conditional/sequential.md).

# Sequential Conditional Sampling

Conditional sampling allows you to fix any values you'd like to see in the synthetic data. The rest of the synthetic data adapts to the values you provide. For sequential data, you can fix the *context* columns that remain static for the duration of the sequence.

## Condition on Known Context

{% hint style="info" %}
**Sampling prerequisites.** In order to create synthetic data, make sure you have created a synthesizer and trained it on your data using the `fit` function. For more information, see the [Modeling](/sdv/modeling/single-table-synthesizers.md) docs.
{% endhint %}

### sample\_sequential\_columns

Use this function to sample the sequences based on known context columns that do not change.

**Parameters**

* (required) `context_columns`: A [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) that contains the sequence key and all the context columns of your data that do not vary with respect to time. Each row corresponds to a sequence that you want to synthesize.
* `sequence_length`: An integer >0 describing the length of each sequence. If you provide `None`, the synthesizer will determine the lengths algorithmically, and the length may be different for each sequence. Defaults to `None`.

**Returns** A [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object with synthetic data. The synthetic data is based on the referenced, context columns.


---

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