Encoding Categorical Data in Sklearn

Categorical data is a common occurrence in many datasets especially in fields like marketing, finance and social sciences. Unlike numerical data, categorical data represents discrete values or categories such as gender, country or product type. Machine learning algorithms require numerical input, making it essential to convert categorical data into a numerical format. This process is known as encoding.

Implementation

Lets see the use of various techniques on a real-life dataset.

Step 1: Loading the Dataset

Here we will load our dataset. Click here to download the used dataset.

import pandas as pd

df = pd.read_csv('data.csv')
print(df.head())

Step 2: Label Encoding

Label encoding is used to map categories to integers.

• fit_transform: Learns and applies the mapping.
• .classes_: Shows the mapping order.

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()

df['class_encoded'] = le.fit_transform(df['class'])

print("Class labels mapping:", dict(zip(le.classes_, le.transform(le.classes_))))
print(df[['class', 'class_encoded']].head())

Step 3: One-Hot Encoding

One-Hot encoding converts nominal/categorical variables like buying, maint, lug_boot, safety, etc into binary columns.

• fit_transform: Finds all unique categories and encodes them to binary columns.
• df_ohe.drop(columns=categorical_cols, inplace=True): Drop original categorical columns if you proceed with encoded values only

from sklearn.preprocessing import OneHotEncoder

categorical_cols = ['buying', 'maint',
                    'doors', 'persons', 'lug_boot', 'safety']
ohe = OneHotEncoder(sparse_output=False)

ohe_array = ohe.fit_transform(df[categorical_cols])
print("OHE feature names:", ohe.get_feature_names_out(categorical_cols))

ohe_df = pd.DataFrame(
    ohe_array, columns=ohe.get_feature_names_out(categorical_cols))
df_ohe = pd.concat([df.reset_index(drop=True), ohe_df], axis=1)
print(df_ohe.head())

Step 4: Ordinal Encoding

Ordinal encoding is use for features where order matters (here, let's treat safety as ordinal: low < med < high). Explicitly supplies category order to ensure model sees the true underlying order.

from sklearn.preprocessing import OrdinalEncoder

ordinal_cols = ['safety']
categories_order = [['low', 'med', 'high']]

oe = OrdinalEncoder(categories=categories_order)
df['safety_ord'] = oe.fit_transform(df[['safety']])

print(df[['safety', 'safety_ord']].head())

Step 5: Putting Data Together with ColumnTransformer

• This approach cleanly manages both ordinal and nominal encoding and fits directly into any sklearn modeling pipeline.
• Suitable for any supervised learning (classification/regression) with categorical inputs.

from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

ordinal_features = ['safety']
ordinal_categories = [['low', 'med', 'high']]

nominal_features = ['buying', 'maint', 'doors', 'persons', 'lug_boot']

preprocessor = ColumnTransformer(
    transformers=[
        ('ord', OrdinalEncoder(categories=ordinal_categories), ordinal_features),
        ('nom', OneHotEncoder(sparse_output=False), nominal_features)
    ]
)

features = ordinal_features + nominal_features
X = df[features]
X_prepared = preprocessor.fit_transform(X)

print("Transformed shape:", X_prepared.shape)

Transformed shape: (1728, 19)

Step 6: Inspection and Resulted Dataset

• Always use the same encoder objects on train and test data to ensure consistency.
• For categorical variable exploration and encoding in a deployed or production ML pipeline, prefer maintaining category order explicitly for any ordinal features.

import numpy as np

final_df = pd.DataFrame(
    np.hstack([X_prepared, df[['class_encoded']].values]),
    columns = list(preprocessor.get_feature_names_out()) + ['class_encoded']
)
print(final_df.head())

Output:

Advantages and Disadvantages of each Encoding Technique

Encoding categorical data is essential to make it usable for machine learning models. By applying label, one-hot or ordinal encoding, we turn categories into numbers, helping models learn effectively and produce accurate results.