One Hot Encoding | Dummies

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1. One Hot Encoding | Dummies

1.1. That's how we do One Hot Encoding | Dummies

1.1.1. Related Resources:

One Hot encoding means splitting categorical variable into multiple binary variables.

“One hot” means at a time only one feature is Hot/Active out of all the split features.

In one hot encoding a categorical feature is split into as many features as there are categories in that feature.

In below example, COLOR is a categorical feature with 3 categories namely red, blue and green,

so new features that are formed from COLOR are COLOR_RED, COLOR_GREEN and COLOR_BLUE.

Now, if the value of color for an entry was red then COLOR_RED will be assigned value 1 and

COLOR_GREEN, COLOR_BLUE will be assigned value 0.

In Pandas categorical features can be one hot encoded by ‘get_dummies’ method.

# Imports 
import pandas as pd

# Let's create a dataframe
data = {'Color': ['Red', 'Red', 'Green', 'Blue', 'Red', 'Green'],
        'Shape': ['Circle', 'Square', 'Square', 'Triangle', 'Circle', 'Triangle'],
        'Value': [1, 1, 2, 1, 3, 3]}

df = pd.DataFrame(data)
df

	Color	Shape	Value
0	Red	Circle	1
1	Red	Square	1
2	Green	Square	2
3	Blue	Triangle	1
4	Red	Circle	3
5	Green	Triangle	3

# Color is a categorical feature having Red, Blue and Green as categories
# Shape is a categorical feature having Circle, Square, Triangle as categories

# Let's get one hot encoded features from Color and Shape
pd.get_dummies(df)

	Value	Color_Blue	Color_Green	Color_Red	Shape_Circle	Shape_Square	Shape_Triangle
0	1	0	0	1	1	0	0
1	1	0	0	1	0	1	0
2	2	0	1	0	0	1	0
3	1	1	0	0	0	0	1
4	3	0	0	1	1	0	0
5	3	0	1	0	0	0	1

That's how we do One Hot Encoding | Dummies

That’s all for this mini tutorial. To sum it up, we learned how to do One Hot Encoding | Dummies.

Hope it was easy, cool and simple to follow. Now it’s on you.