> For the complete documentation index, see [llms.txt](https://docs.sdv.dev/rdt/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/rdt/usage/hypertransformer/preparation.md).

# Preparation

## Creating a HyperTransformer

Use a HyperTransformer to manage all the transformers you're applying to a multi-column dataset. 

Create one by importing it from the `rdt` library. There are no parameters.

```python
from rdt import HyperTransformer
ht = HyperTransformer()
```

## Loading your data

The RDT library uses pandas -- a popular open source library for data manipulation. The HyperTransformer expects your data is a [pandas DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) object. 

There are a variety of ways to load your data into the expected format. The most common case is your dataset being a csv file:

```python
import pandas as pd
customers = pd.read_csv('./datasets/customers.csv')
```

Refer to the pandas documentation for more information about [reading csv files](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) or [other types of files](https://pandas.pydata.org/docs/reference/io.html).


---

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