class ray.data.preprocessors.OrdinalEncoder(columns: List[str], *, encode_lists: bool = True)[source]#

Bases: Preprocessor

Encode values within columns as ordered integer values.

OrdinalEncoder encodes categorical features as integers that range from \(0\) to \(n - 1\), where \(n\) is the number of categories.

If you transform a value that isn’t in the fitted datset, then the value is encoded as float("nan").

Columns must contain either hashable values or lists of hashable values. Also, you can’t have both scalars and lists in the same column.


Use OrdinalEncoder to encode categorical features as integers.

>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import OrdinalEncoder
>>> df = pd.DataFrame({
...     "sex": ["male", "female", "male", "female"],
...     "level": ["L4", "L5", "L3", "L4"],
... })
>>> ds = ray.data.from_pandas(df)  
>>> encoder = OrdinalEncoder(columns=["sex", "level"])
>>> encoder.fit_transform(ds).to_pandas()  
   sex  level
0    1      1
1    0      2
2    1      0
3    0      1

If you transform a value not present in the original dataset, then the value is encoded as float("nan").

>>> df = pd.DataFrame({"sex": ["female"], "level": ["L6"]})
>>> ds = ray.data.from_pandas(df)  
>>> encoder.transform(ds).to_pandas()  
   sex  level
0    0    NaN

OrdinalEncoder can also encode categories in a list.

>>> df = pd.DataFrame({
...     "name": ["Shaolin Soccer", "Moana", "The Smartest Guys in the Room"],
...     "genre": [
...         ["comedy", "action", "sports"],
...         ["animation", "comedy",  "action"],
...         ["documentary"],
...     ],
... })
>>> ds = ray.data.from_pandas(df)  
>>> encoder = OrdinalEncoder(columns=["genre"])
>>> encoder.fit_transform(ds).to_pandas()  
                            name      genre
0                 Shaolin Soccer  [2, 0, 4]
1                          Moana  [1, 2, 0]
2  The Smartest Guys in the Room        [3]
  • columns – The columns to separately encode.

  • encode_lists – If True, encode list elements. If False, encode whole lists (i.e., replace each list with an integer). True by default.

See also


Another preprocessor that encodes categorical data.

PublicAPI (alpha): This API is in alpha and may change before becoming stable.



Load the original preprocessor serialized via self.serialize().


Fit this Preprocessor to the Dataset.


Fit this Preprocessor to the Dataset and then transform the Dataset.


Batch format hint for upstream producers to try yielding best block format.


Return this preprocessor serialized as a string.


Transform the given dataset.


Transform a single batch of data.