How do you scale in python?


Scale Features

When your data has different values, and even different measurement units, it can be difficult to compare them. What is kilograms compared to meters? Or altitude compared to time?

The answer to this problem is scaling. We can scale data into new values that are easier to compare.

Take a look at the table below, it is the same data set that we used in the multiple regression chapter, but this time the volume column contains values in liters instead of cm3 (1.0 instead of 1000).

Car Model Volume Weight CO2

Toyota Aygo 1.0 790 99
Mitsubishi Space Star 1.2 1160 95
Skoda Citigo 1.0 929 95
Fiat 500 0.9 865 90
Mini Cooper 1.5 1140 105
VW Up! 1.0 929 105
Skoda Fabia 1.4 1109 90
Mercedes A-Class 1.5 1365 92
Ford Fiesta 1.5 1112 98
Audi A1 1.6 1150 99
Hyundai I20 1.1 980 99
Suzuki Swift 1.3 990 101
Ford Fiesta 1.0 1112 99
Honda Civic 1.6 1252 94
Hundai I30 1.6 1326 97
Opel Astra 1.6 1330 97
BMW 1 1.6 1365 99
Mazda 3 2.2 1280 104
Skoda Rapid 1.6 1119 104
Ford Focus 2.0 1328 105
Ford Mondeo 1.6 1584 94
Opel Insignia 2.0 1428 99
Mercedes C-Class 2.1 1365 99
Skoda Octavia 1.6 1415 99
Volvo S60 2.0 1415 99
Mercedes CLA 1.5 1465 102
Audi A4 2.0 1490 104
Audi A6 2.0 1725 114
Volvo V70 1.6 1523 109
BMW 5 2.0 1705 114
Mercedes E-Class 2.1 1605 115
Volvo XC70 2.0 1746 117
Ford B-Max 1.6 1235 104
BMW 2 1.6 1390 108
Opel Zafira 1.6 1405 109
Mercedes SLK 2.5 1395 120

It can be difficult to compare the volume 1.0 with the weight 790, but if we scale them both into comparable values, we can easily see how much one value is compared to the other.

There are different methods for scaling data, in this tutorial we will use a method called standardization.

The standardization method uses this formula:

z = (x - u) / s

Where z is the new value, x is the original value, u is the mean and s is the standard deviation.

If you take the weight column from the data set above, the first value is 790, and the scaled value will be:

(790 - 1292.23) / 238.74 = -2.1

If you take the volume column from the data set above, the first value is 1.0, and the scaled value will be:

(1.0 - 1.61) / 0.38 = -1.59

Now you can compare -2.1 with -1.59 instead of comparing 790 with 1.0.

You do not have to do this manually, the Python sklearn module has a method called StandardScaler() which returns a Scaler object with methods for transforming data sets.

Example

Scale all values in the Weight and Volume columns:

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()

df = pandas.read_csv("data.csv")

X = df[['Weight', 'Volume']]

scaledX = scale.fit_transform(X)

print(scaledX)

Result:

Note that the first two values are -2.1 and -1.59, which corresponds to our calculations:

[[-2.10389253 -1.59336644]
 [-0.55407235 -1.07190106]
 [-1.52166278 -1.59336644]
 [-1.78973979 -1.85409913]
 [-0.63784641 -0.28970299]
 [-1.52166278 -1.59336644]
 [-0.76769621 -0.55043568]
 [ 0.3046118  -0.28970299]
 [-0.7551301  -0.28970299]
 [-0.59595938 -0.0289703 ]
 [-1.30803892 -1.33263375]
 [-1.26615189 -0.81116837]
 [-0.7551301  -1.59336644]
 [-0.16871166 -0.0289703 ]
 [ 0.14125238 -0.0289703 ]
 [ 0.15800719 -0.0289703 ]
 [ 0.3046118  -0.0289703 ]
 [-0.05142797  1.53542584]
 [-0.72580918 -0.0289703 ]
 [ 0.14962979  1.01396046]
 [ 1.2219378  -0.0289703 ]
 [ 0.5685001   1.01396046]
 [ 0.3046118   1.27469315]
 [ 0.51404696 -0.0289703 ]
 [ 0.51404696  1.01396046]
 [ 0.72348212 -0.28970299]
 [ 0.8281997   1.01396046]
 [ 1.81254495  1.01396046]
 [ 0.96642691 -0.0289703 ]
 [ 1.72877089  1.01396046]
 [ 1.30990057  1.27469315]
 [ 1.90050772  1.01396046]
 [-0.23991961 -0.0289703 ]
 [ 0.40932938 -0.0289703 ]
 [ 0.47215993 -0.0289703 ]
 [ 0.4302729   2.31762392]]

Run example »



Predict CO2 Values

The task in the Multiple Regression chapter was to predict the CO2 emission from a car when you only knew its weight and volume.

When the data set is scaled, you will have to use the scale when you predict values:

Example

Predict the CO2 emission from a 1.3 liter car that weighs 2300 kilograms:

import pandas
from sklearn import linear_model
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()

df = pandas.read_csv("data.csv")

X = df[['Weight', 'Volume']]
y = df['CO2']

scaledX = scale.fit_transform(X)

regr = linear_model.LinearRegression()
regr.fit(scaledX, y)

scaled = scale.transform([[2300, 1.3]])

predictedCO2 = regr.predict([scaled[0]])
print(predictedCO2)

Result:

Run example »



How do you scale data in Python?

There are different methods for scaling data, in this tutorial we will use a method called standardization. Where z is the new value, x is the original value, u is the mean and s is the standard deviation. Now you can compare -2.1 with -1.59 instead of comparing 790 with 1.0.

What does scale mean in Python?

Feature Scaling or Standardization: It is a step of Data Pre Processing that is applied to independent variables or features of data. It basically helps to normalize the data within a particular range. Sometimes, it also helps in speeding up the calculations in an algorithm. Package Used: sklearn.preprocessing.

How do you normalize and scale data in Python?

Using MinMaxScaler() to Normalize Data in Python This is a more popular choice for normalizing datasets. You can see that the values in the output are between (0 and 1). MinMaxScaler also gives you the option to select feature range. By default, the range is set to (0,1).

How do you feature scaling?

The most common techniques of feature scaling are Normalization and Standardization. Normalization is used when we want to bound our values between two numbers, typically, between [0,1] or [-1,1]. While Standardization transforms the data to have zero mean and a variance of 1, they make our data unitless.