Often in statistics, the datasets we’re working with include .

These are variables that take on names or labels. Examples include:

• Marital status (“married”, “single”, “divorced”)
• Smoking status (“smoker”, “non-smoker”)
• Eye color (“blue”, “green”, “hazel”)
• Level of education (e.g. “high school”,  “Bachelor’s degree”, “Master’s degree”)

When fitting machine learning algorithms (like , , , etc.), we often convert categorical variables to dummy variables, which are numeric variables that are used to represent categorical data.

For example, suppose we have a dataset that contains the categorical variable Gender. To use this variable as a predictor in a regression model, we would first need to convert it to a dummy variable.

To create this dummy variable, we can choose one of the values (“Male”) to represent 0 and the other value (“Female”) to represent 1:

How to Create Dummy Variables in Pandas

To create dummy variables for a variable in a pandas DataFrame, we can use the function, which uses the following basic syntax:

pandas.get_dummies(data, prefix=None, columns=None, drop_first=False)

where:

• data: The name of the pandas DataFrame
• prefix: A string to append to the front of the new dummy variable column
• columns: The name of the column(s) to convert to a dummy variable
• drop_first: Whether or not to drop the first dummy variable column

The following examples show how to use this function in practice.

Example 1: Create a Single Dummy Variable

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'income': [45, 48, 54, 57, 65, 69, 78],
                   'age': [23, 25, 24, 29, 38, 36, 40],
                   'gender': ['M', 'F', 'M', 'F', 'F', 'F', 'M']})

#view DataFrame
df

        income	age	gender
0	45	23	M
1	48	25	F
2	54	24	M
3	57	29	F
4	65	38	F
5	69	36	F
6	78	40	M

We can use the pd.get_dummies() function to turn gender into a dummy variable:

#convert gender to dummy variable
pd.get_dummies(df, columns=['gender'], drop_first=True)

	income	age	gender_M
0	45	23	1
1	48	25	0
2	54	24	1
3	57	29	0
4	65	38	0
5	69	36	0
6	78	40	1

The gender column is now a dummy variable where:

• A value of 0 represents “Female”
• A value of 1 represents “Male”

Example 2: Create Multiple Dummy Variables

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'income': [45, 48, 54, 57, 65, 69, 78],
                   'age': [23, 25, 24, 29, 38, 36, 40],
                   'gender': ['M', 'F', 'M', 'F', 'F', 'F', 'M'],
                   'college': ['Y', 'N', 'N', 'N', 'Y', 'Y', 'Y']})

#view DataFrame
df

	income	age	gender	college
0	45	23	M	Y
1	48	25	F	N
2	54	24	M	N
3	57	29	F	N
4	65	38	F	Y
5	69	36	F	Y
6	78	40	M	Y

We can use the pd.get_dummies() function to convert gender and college both into dummy variables:

#convert gender to dummy variable
pd.get_dummies(df, columns=['gender', 'college'], drop_first=True)


        income	age	gender_M  college_Y
0	45	23	1	  1
1	48	25	0	  0
2	54	24	1	  0
3	57	29	0	  0
4	65	38	0	  1
5	69	36	0	  1
6	78	40	1	  1

The gender column is now a dummy variable where:

• A value of 0 represents “Female”
• A value of 1 represents “Male”

And the college column is now a dummy variable where:

• A value of 0 represents “No” college
• A value of 1 represents “Yes” college