OneHotEncoder¶
API Reference¶

class
feature_engine.encoding.
OneHotEncoder
(top_categories=None, variables=None, drop_last=False)[source]¶ One hot encoding consists in replacing the categorical variable by a combination of binary variables which take value 0 or 1, to indicate if a certain category is present in an observation. The binary variables are also known as dummy variables.
For example, from the categorical variable “Gender” with categories “female” and “male”, we can generate the boolean variable “female”, which takes 1 if the observation is female or 0 otherwise. We can also generate the variable “male”, which takes 1 if the observation is “male” and 0 otherwise.
The encoder can create k binary variables per categorical variable, k being the number of unique categories, or alternatively k1 to avoid redundant information. This behaviour can be specified using the parameter
drop_last
.The encoder has the additional option to generate binary variables only for the top n most popular categories, that is, the categories that are shared by the majority of the observations in the dataset. This behaviour can be specified with the parameter
top_categories
.Note
Only when creating binary variables for all categories of the variable, we can specify if we want to encode into k or k1 binary variables, where k is the number if unique categories. If we encode only the top n most popular categories, the encoder will create only n binary variables per categorical variable. Observations that do not show any of these popular categories, will have 0 in all the binary variables.
The encoder will encode only categorical variables (type ‘object’). A list of variables can be passed as an argument. If no variables are passed as argument, the encoder will find and encode categorical variables (object type).
The encoder first finds the categories to be encoded for each variable (fit). The encoder then creates one dummy variable per category for each variable (transform).
Note
New categories in the data to transform, that is, those that did not appear in the training set, will be ignored (no binary variable will be created for them). This means that observations with categories not present in the train set, will be encoded as 0 in all the binary variables.
Also Note
The original categorical variables are removed from the returned dataset when we apply the transform() method. In their place, the binary variables are returned.
 Parameters
 top_categoriesint, default=None
If None, a dummy variable will be created for each category of the variable. Alternatively, we can indicate in
top_categories
the number of most frequent categories to encode. In this case, dummy variables will be created only for those popular categories and the rest will be ignored, i.e., they will show the value 0 in all the binary variables. variableslist
The list of categorical variables to encode. If None, the encoder will find and select all object type variables in the train set.
 drop_lastboolean, default=False
Only used if
top_categories = None
. It indicates whether to create dummy variables for all the categories (k dummies), or if set toTrue
, it will ignore the last binary variable of the list (k1 dummies).
Attributes
encoder_dict_ :
Dictionary with the categories for which dummy variables will be created.
Notes
If the variables are intended for linear models, it is recommended to encode into k1 or top categories. If the variables are intended for tree based algorithms, it is recommended to encode into k or top n categories. If feature selection will be performed, then also encode into k or top n categories. Linear models evaluate all features during fit, while tree based models and many feature selection algorithms evaluate variables or groups of variables separately. Thus, if encoding into k1, the last variable / category will not be examined.
References
One hot encoding of top categories was described in the following article:
 1
NiculescuMizil, et al. “Winning the KDD Cup Orange Challenge with Ensemble Selection”. JMLR: Workshop and Conference Proceedings 7: 2334. KDD 2009 http://proceedings.mlr.press/v7/niculescu09/niculescu09.pdf
Methods
fit:
Learn the unique categories per variable
transform:
Replace the categorical variables by the binary variables.
fit_transform:
Fit to the data, then transform it.

fit
(X, y=None)[source]¶ Learns the unique categories per variable. If top_categories is indicated, it will learn the most popular categories. Alternatively, it learns all unique categories per variable.
 Parameters
 Xpandas dataframe of shape = [n_samples, n_features]
The training input samples. Can be the entire dataframe, not just seleted variables.
 ypandas series, default=None
Target. It is not needed in this encoded. You can pass y or None.
 Returns
 self
 Raises
 TypeError
If the input is not a Pandas DataFrame.
If any user provided variable is not categorical
 ValueError
If there are no categorical variables in the df or the df is empty
If the variable(s) contain null values

transform
(X)[source]¶ Replaces the categorical variables by the binary variables.
 Parameters
 Xpandas dataframe of shape = [n_samples, n_features]
The data to transform.
 Returns
 Xpandas dataframe.
The transformed dataframe. The shape of the dataframe will be different from the original as it includes the dummy variables in place of the of the original categorical ones.
 rtype
DataFrame
..
 Raises
 TypeError
If the input is not a Pandas DataFrame
 ValueError
If the variable(s) contain null values.
If the dataframe is not of same size as that used in fit()
Example¶
The OneHotEncoder() replaces categorical variables by a set of binary variables, one per unique category. The encoder has the option to create k or k1 binary variables, where k is the number of unique categories.
The encoder can also create binary variables for the n most popular categories, n being determined by the user. This means, if we encode the 6 more popular categories, we will only create binary variables for those categories, and the rest will be dropped.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from feature_engine.encoding import OneHotEncoder
# Load dataset
def load_titanic():
data = pd.read_csv('https://www.openml.org/data/get_csv/16826755/phpMYEkMl')
data = data.replace('?', np.nan)
data['cabin'] = data['cabin'].astype(str).str[0]
data['pclass'] = data['pclass'].astype('O')
data['embarked'].fillna('C', inplace=True)
return data
data = load_titanic()
# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
data.drop(['survived', 'name', 'ticket'], axis=1),
data['survived'], test_size=0.3, random_state=0)
# set up the encoder
encoder = OneHotEncoder( top_categories=2, variables=['pclass', 'cabin', 'embarked'], drop_last=False)
# fit the encoder
encoder.fit(X_train)
# transform the data
train_t= encoder.transform(X_train)
test_t= encoder.transform(X_test)
encoder.encoder_dict_
{'pclass': [3, 1], 'cabin': ['n', 'C'], 'embarked': ['S', 'C']}