Preprocessing - Categorical Data
Categorical Data
When your data has categories represented by strings, it will be difficult to use them to train machine learning models which often only accepts numeric data.
Instead of ignoring the categorical data and excluding the information from our model, you can tranform the data so it can be used in your models.
Take a look at the table below, it is the same data set that we used in themultiple regression chapter.
Example
cars = pd.read_csv('data.csv')
print(cars.to_string())
Result
Car Model Volume Weight CO2 0 Toyoty Aygo 1000 790 99 1 Mitsubishi Space Star 1200 1160 95 2 Skoda Citigo 1000 929 95 3 Fiat 500 900 865 90 4 Mini Cooper 1500 1140 105 5 VW Up! 1000 929 105 6 Skoda Fabia 1400 1109 90 7 Mercedes A-Class 1500 1365 92 8 Ford Fiesta 1500 1112 98 9 Audi A1 1600 1150 99 10 Hyundai I20 1100 980 99 11 Suzuki Swift 1300 990 101 12 Ford Fiesta 1000 1112 99 13 Honda Civic 1600 1252 94 14 Hundai I30 1600 1326 97 15 Opel Astra 1600 1330 97 16 BMW 1 1600 1365 99 17 Mazda 3 2200 1280 104 18 Skoda Rapid 1600 1119 104 19 Ford Focus 2000 1328 105 20 Ford Mondeo 1600 1584 94 21 Opel Insignia 2000 1428 99 22 Mercedes C-Class 2100 1365 99 23 Skoda Octavia 1600 1415 99 24 Volvo S60 2000 1415 99 25 Mercedes CLA 1500 1465 102 26 Audi A4 2000 1490 104 27 Audi A6 2000 1725 114 28 Volvo V70 1600 1523 109 29 BMW 5 2000 1705 114 30 Mercedes E-Class 2100 1605 115 31 Volvo XC70 2000 1746 117 32 Ford B-Max 1600 1235 104 33 BMW 216 1600 1390 108 34 Opel Zafira 1600 1405 109 35 Mercedes SLK 2500 1395 120
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In the multiple regression chapter, we tried to predict the CO2 emitted based on the volume of the engine and the weight of the car but we excluded information about the car brand and model.
The information about the car brand or the car model might help us make a better prediction of the CO2 emitted.
One Hot Encoding
We cannot make use of the Car or Model column in our data since they are not numeric. A linear relationship between a categorical variable, Car or Model, and a numeric variable, CO2, cannot be determined.
To fix this issue, we must have a numeric representation of the categorical variable. One way to do this is to have a column representing each group in the category.
For each column, the values will be 1 or 0 where 1 represents the inclusion of the group and 0 represents the exclusion. This transformation is called one hot encoding.
You do not have to do this manually, the Python Pandas module has a function that calledget_dummies() which does one hot encoding.
Learn about the Pandas module in ourPandas Tutorial.
Example
One Hot Encode the Car column:
cars = pd.read_csv('data.csv')
ohe_cars = pd.get_dummies(cars[['Car']])
print(ohe_cars.to_string())
Result
Car_Audi Car_BMW Car_Fiat Car_Ford Car_Honda Car_Hundai Car_Hyundai Car_Mazda Car_Mercedes Car_Mini Car_Mitsubishi Car_Opel Car_Skoda Car_Suzuki Car_Toyoty Car_VW Car_Volvo 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 6 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 7 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 8 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 9 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 12 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 13 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 16 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 18 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 19 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 20 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 21 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 22 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 25 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 26 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 28 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 29 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 32 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 33 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 35 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
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Results
A column was created for every car brand in the Car column.
Predict CO2
We can use this additional information alongside the volume and weight to predict CO2
To combine the information, we can use theconcat() function from pandas.
First we will need to import a couple modules.
We will start with importing the Pandas.
import pandas
The pandas module allows us to read csv files and manipulate DataFrame objects:
cars = pandas.read_csv("data.csv")
It also allows us to create the dummy variables:
ohe_cars = pandas.get_dummies(cars[['Car']])
Then we must select the independent variables (X) and add the dummy variables columnwise.
Also store the dependent variable in y.
X = pandas.concat([cars[['Volume', 'Weight']], ohe_cars], axis=1)
y = cars['CO2']
We also need to import a method from sklearn to create a linear model
Learn aboutlinear regression.
from sklearn import linear_model
Now we can fit the data to a linear regression:
regr = linear_model.LinearRegression()
regr.fit(X,y)
Finally we can predict the CO2 emissions based on the car's weight, volume, and manufacturer.
##predict the CO2 emission of a VW where the weight is 2300kg, and the volume is 1300cm3:
predictedCO2 = regr.predict([[2300, 1300,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]])
Example
from sklearn import linear_model
cars = pandas.read_csv("data.csv")
ohe_cars = pandas.get_dummies(cars[['Car']])
X = pandas.concat([cars[['Volume', 'Weight']], ohe_cars], axis=1)
y = cars['CO2']
regr = linear_model.LinearRegression()
regr.fit(X,y)
##predict the CO2 emission of a VW where the weight is 2300kg, and the volume is 1300cm3:
predictedCO2 = regr.predict([[2300, 1300,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]])
print(predictedCO2)
Result
[122.45153299]
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We now have a coefficient for the volume, the weight, and each car brand in the data set
Dummifying
It is not necessary to create one column for each group in your category. The information can be retained using 1 column less than the number of groups you have.
For example, you have a column representing colors and in that column, you have two colors, red and blue.
Example
colors = pd.DataFrame({'color': ['blue', 'red']})
print(colors)
Result
color 0 blue 1 red
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You can create 1 column called red where 1 represents red and 0 represents not red, which means it is blue.
To do this, we can use the same function that we used for one hot encoding, get_dummies, and then drop one of the columns. There is an argument, drop_first, which allows us to exclude the first column from the resulting table.
Example
colors = pd.DataFrame({'color': ['blue', 'red']})
dummies = pd.get_dummies(colors, drop_first=True)
print(dummies)
Result
color_red 0 0 1 1
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What if you have more than 2 groups? How can the multiple groups be represented by 1 less column?
Let's say we have three colors this time, red, blue and green. When we get_dummies while dropping the first column, we get the following table.
Example
colors = pd.DataFrame({'color': ['blue', 'red', 'green']})
dummies = pd.get_dummies(colors, drop_first=True)
dummies['color'] = colors['color']
print(dummies)
Result
color_green color_red color 0 0 0 blue 1 0 1 red 2 1 0 green
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