# Conditional Sampling

Do you have exact context that you'd like to include in the synthetic sequences? Using **conditional sampling** to provide this information.

## Condition on Known Context

Do you already know all the context for each sequence? The SDV can factor in the context columns and generate sequences based on them.

### 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.