# vDataFrame.cdt¶

In [ ]:

```
vDataFrame.cdt(columns: list = [],
max_cardinality: int = 20,
nbins: int = 10,
tcdt: bool = True,)
```

Returns the complete disjunctive table of the vDataFrame. Numerical features are transformed to categorical using the 'discretize' method. Applying PCA on TCDT leads to MCA (Multiple correspondence analysis).

**⚠ Warning:** This method can become computationally expensive when used with categorical variables with many categories.

### Parameters¶

Name | Type | Optional | Description |
---|---|---|---|

columns | list | ❌ | List of the vColumns names. |

max_cardinality | int | ✓ | For any categorical variable, keeps the most frequent categories and merges the less frequent categories into a new unique category. |

nbins | int | ✓ | Number of bins used for the discretization (must be > 1). |

tcdt | list | ✓ | If set to True, returns the transformed complete disjunctive table (TCDT). |

In [11]:

```
from verticapy.datasets import load_titanic
titanic = load_titanic()
titanic.cdt(tcdt=False)
```

Out[11]: