What is an ordinal encoder in pandas?

In the context of pandas and machine learning, an ordinal encoder is a technique used for transforming categorical data into numerical data while maintaining the order or rank of the categories. It’s particularly useful when the categories have a natural order, like "Low," "Medium," and "High".

Here’s a more detailed explanation:

Core Function:

- An ordinal encoder maps each unique category in a categorical feature to a unique integer. Crucially, it assigns these integers in a way that reflects the inherent order of the categories. For example, "Low" might be encoded as 0, "Medium" as 1, and "High" as 2. It's essential to define this mapping correctly.

Why Ordinal Encoding?

- Many machine learning algorithms work best with numerical data. Ordinal encoding allows you to incorporate ordered categorical features into your models. If you have features like educational levels (e.g., "High School," "Bachelor's," "Master's," "Ph.D.") or satisfaction ratings (e.g., "Very Dissatisfied," "Dissatisfied," "Neutral," "Satisfied," "Very Satisfied"), using numerical values is beneficial.

How to do it in Pandas and Scikit-learn:

- Pandas itself doesn’t have a direct function called "ordinal_encoder," but you can use the OrdinalEncoder class from Scikit-learn within your pandas workflows. You will first need to install the scikit-learn library using pip : pip install scikit-learn.

- Here is an example:

import pandas as pd
from sklearn.preprocessing import OrdinalEncoder

# Sample DataFrame
df = pd.DataFrame({'size': ['Small', 'Medium', 'Large', 'Medium', 'Small']})

# Define the order of categories
categories = [['Small', 'Medium', 'Large']]

# Initialize the OrdinalEncoder with categories
encoder = OrdinalEncoder(categories=categories)

# Fit and transform the data
df['size_encoded'] = encoder.fit_transform(df[['size']])

print(df) # Expected output
# size size_encoded
# 0 Small 0.0
# 1 Medium 1.0
# 2 Large 2.0
# 3 Medium 1.0
# 4 Small 0.0

Important Considerations:

- Category Order: The most critical part of using an ordinal encoder is ensuring that the numerical mappings correspond to the true order of the categories. The list of categories passed to the OrdinalEncoder must be in the desired order.

- Data Type: The encoder outputs numerical data as float by default but you can control output data type using an argument if you need integer.

- Unknown Categories: If your dataset contains categories not defined in the initial order list, the encoder will throw an error by default; but, you can tell it to deal with unknown categories.

- Alternatives: If categories do not have a particular order, an alternative such as one-hot encoding would be more appropriate.

In Summary, an ordinal encoder is a valuable tool in pandas for converting ordered categorical data into a numerical format that is suitable for machine learning models. This helps algorithms understand the underlying order or rank of the categories, which can lead to more accurate and meaningful results. Always be mindful to configure the encoding correctly based on the natural order of your data and using the scikit-learn library.

Learn more about this