How to convert Categorical variables into Numerical Variables using ColumnTransformer?

How to convert Categorical variables into Numerical Variables using ColumnTransformer?

How to convert Categorical variables into Numerical Variables using ColumnTransformer?

This recipe is a short example on how to convert categorical variables into numerical variables using ColumnTransformer


Recipe Objective

When we talk about categorical variables, we are talking about non numerical values like Strings or Text. For example, a city name, a store name - for that matter anything that is not a number is a categorical variable. For us humans, it is very easy to interpret and understand text - but for machines it is not the case.

In Machine Learning, all the algorithms in the sklearn library cannot handle categorical variables so before we give the data to the algorithm for training and predicting - we have to convert it to numbers.

So this recipe is a short example on how to convert categorical variables into numerical variables. Let's get started.

Step 1 - Import the library - ColumnTransformer

import numpy as np import pandas as pd from sklearn.preprocessing import OneHotEncoder from sklearn.compose import ColumnTransformer

Let's pause and look at these imports. Numpy and Pandas are the usual ones. If you've looked at the older recipes , LabelEncoder and OneHotEncoder was typically used to convert categorical variables. But here we are using ColumnTransformer - why?

In the newer versions of sklearn starting from 0.20 - they have introduced ColumnTransformer. Prior to this, doing this conversion was a 2 step process. First we have to label encode the data and then one hot encode it. But now with ColumnTransformer, they have combined this - so you can do the entire conversion with just one line of code as shown in Step 3 below.

Step 2 - Setup the Data

dataset = {'research': [15000, 90000, 250000, 175000, 88000, 210000], 'marketing': [5000, 25000, 31000, 44000, 19700, 21111], 'city': ['Texas', 'Delaware', 'Florida', 'Texas', 'Delaware','Florida'], 'profit': [9999, 5555, 3333, 4444, 1111, 2222] df = pd.DataFrame(dataset)

Let us create a simple dataset and convert it to a dataframe. This sample data shows how much a company spends on research and marketing. We will add the state where the company is present. The last variable is the profit that company makes. So in a real example, we will be predicting the profit. But in this recipe, we will not be going into that.

Now our dataset is ready

Step 3 - Create an object of ColumnTransformer class

Before we do that, let's look at the important parameters that we need to pass.

1) transformer
  • name: this is just a name that we pass to the transformer
  • transformer: here we need to pass an estimator that supports fit and transform. Since we want to encode the data, we will be passing OneHotEncoder here.
  • columns: the index of columns that contain the categorical values that we want to convert

2) remainder
This parameter basically tells the transformer what to do with the remaining columns other than the ones we have mentioned above for converting. The values can be "drop" or "passthrough". The default value is drop which means only the transformed columns will be returned by the transformer and the remaining columns will be dropped. But if we want the transformer to pass them - we have to use the value "passthrough"

Now that we understand, let's create the object

columnTransformer = ColumnTransformer([('encoder', OneHotEncoder(), [2])], remainder='passthrough')
  • As you can see, we have named the transformer as "encoder"
  • For the estimator, we are passing an object of OneHotEncoder
  • And for columns, we can see from our dataset that the 3rd column is the city column that we want to transform. Since the index start at 0, we are passing value 2 here
  • Lastly for remainder we are passing "passthrough" as we want the transformer to return the remaining columns

Step 4 - Convert the categorical data with one line of code

Now that we have got the ColumnTransformer constructor ready, we just have to call the fit_transform method and pass the dataset to it to do the conversion

df = np.array(columnTransformer.fit_transform(df), dtype = np.str)

Step 5 - Lets look at our dataset now

Once we run the above code snippet, we will see that all States have been converted to numbers and added to the beginning.

For example :
Texas has been represented by 0,0,1
Delaware by 1,0, 0
Florida by 0,1,0.

[['0.0' '0.0' '1.0' '15000.0' '5000.0' '9999.0']
 ['1.0' '0.0' '0.0' '90000.0' '25000.0' '5555.0']
 ['0.0' '1.0' '0.0' '250000.0' '31000.0' '3333.0']
 ['0.0' '0.0' '1.0' '175000.0' '44000.0' '4444.0']
 ['1.0' '0.0' '0.0' '88000.0' '19700.0' '1111.0']
 ['0.0' '1.0' '0.0' '210000.0' '21111.0' '2222.0']]

Relevant Projects

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.