How to convert categorical variables into numerical variables in Python?
DATA MUNGING DATA CLEANING PYTHON MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to convert categorical variables into numerical variables in Python?

How to convert categorical variables into numerical variables in Python?

This recipe helps you convert categorical variables into numerical variables in Python

0

Recipe Objective

Machine Learning Models can not work on categorical variables in the form of strings, so we need to change it into numerical form. We can assign numbers for each categories but it may not be that effective when difference between the categories can not be measured. This can be done by making new features according to the categories with bool values. For this we will be using dummy variables to do so.

This python source code does the following:
1. Creates dictionary and converts it into dataframe
2. Uses "get_dummies" function for the encoding
3. Concats the final encoded dataset into the final dataframe
4. Drops categorical variable column

So this is the recipe on how we can convert categorical variables into numerical variables in Python.

Step 1 - Import the library

import pandas as pd

We have only imported pandas this is reqired for dataset.

Step 2 - Setting up the Data

We have created a dictionary and passed it through the pd.DataFrame to create a dataframe with columns 'name', 'episodes', 'gender'. data = {'name': ['Sheldon', 'Penny', 'Amy', 'Penny', 'Raj', 'Sheldon'], 'episodes': [42, 24, 31, 29, 37, 40], 'gender': ['male', 'female', 'female', 'female', 'male', 'male']} df = pd.DataFrame(data, columns = ['name','episodes', 'gender']) print(df)

Step 3 - Making Dummy Variables and Printing the final Dataset

We can clearly observe that in the column 'gender' there are two categories male and female, so for that column we have to make dummies according to the categories. So we have passed that column in the function and stored it in df_gender. Finally we have added that columns in out original dataset. df_gender = pd.get_dummies(df['gender']) df_new = pd.concat([df, df_gender], axis=1) print(df_new) So the output comes as:

      name  episodes  gender
0  Sheldon        42    male
1    Penny        24  female
2      Amy        31  female
3    Penny        29  female
4      Raj        37    male
5  Sheldon        40    male

      name  episodes  gender  female  male
0  Sheldon        42    male       0     1
1    Penny        24  female       1     0
2      Amy        31  female       1     0
3    Penny        29  female       1     0
4      Raj        37    male       0     1
5  Sheldon        40    male       0     1

Relevant Projects

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Music Recommendation System Project using Python and R
Machine Learning Project - Work with KKBOX's Music Recommendation System dataset to build the best music recommendation engine.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

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.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

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.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.