CountFrequencyEncoder

API Reference

class feature_engine.encoding.CountFrequencyEncoder(encoding_method='count', variables=None)

The CountFrequencyEncoder() replaces categories by either the count or the percentage of observations per category.

For example in the variable colour, if 10 observations are blue, blue will be replaced by 10. Alternatively, if 10% of the observations are blue, blue will be replaced by 0.1.

The CountFrequencyEncoder() will encode only categorical variables (type ‘object’). A list of variables to encode can be passed as argument. Alternatively, the encoder will find and encode all categorical variables (object type).

The encoder first maps the categories to the counts or frequencies for each variable (fit). The encoder then replaces the categories by those mapped numbers (transform).

Parameters

encoding_methodstr, default=’count’

Desired method of encoding.

‘count’: number of observations per category

‘frequency’: percentage of observations per category

variableslist

The list of categorical variables that will be encoded. If None, the encoder will find and transform all object type variables.

Attributes

encoder_dict_:

Dictionary with the count or frequency} per category, per variable.

See also

feature_engine.encoding.RareLabelEncoder

Notes

NAN are introduced when encoding categories that were not present in the training dataset. If this happens, try grouping infrequent categories using the RareLabelEncoder().

Methods

fit:	Learn the count or frequency per category, per variable.
transform:	Encode the categories to numbers.
fit_transform:	Fit to the data, then transform it.
inverse_transform:	Encode the numbers into the original categories.

fit(X, y=None)

Learn the counts or frequencies which will be used to replace the categories.

Parameters

Xpandas dataframe of shape = [n_samples, n_features]

The training dataset. Can be the entire dataframe, not just the variables to be transformed.

ypandas Series, default = None

y is not needed in this encoder. 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

inverse_transform(X)

Convert the encoded variable back to the original values.

Parameters

Xpandas dataframe of shape = [n_samples, n_features].

The transformed dataframe.

Returns

Xpandas dataframe of shape = [n_samples, n_features].

The un-transformed dataframe, with the categorical variables containing the original values.

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()

transform(X)

Replace categories with the learned parameters.

Parameters

Xpandas dataframe of shape = [n_samples, n_features].

The dataset to transform.

Returns

Xpandas dataframe of shape = [n_samples, n_features].

The dataframe containing the categories replaced by numbers.

rtype

DataFrame ..

Raises

TypeError

If the input is not a Pandas DataFrame

ValueError

• If the variable(s) contain null values

• If dataframe is not of same size as that used in fit()

Warning

If after encoding, NAN were introduced.

Example

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 CountFrequencyEncoder

# 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 = CountFrequencyEncoder(encoding_method='frequency',
                         variables=['cabin', 'pclass', 'embarked'])

# 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_

{'cabin': {'n': 0.7663755458515283,
  'C': 0.07751091703056769,
  'B': 0.04585152838427948,
  'E': 0.034934497816593885,
  'D': 0.034934497816593885,
  'A': 0.018558951965065504,
  'F': 0.016375545851528384,
  'G': 0.004366812227074236,
  'T': 0.001091703056768559},
 'pclass': {3: 0.5436681222707423,
  1: 0.25109170305676853,
  2: 0.2052401746724891},
 'embarked': {'S': 0.7117903930131004,
  'C': 0.19759825327510916,
  'Q': 0.0906113537117904}}