Preprocessing

3 tasks. Write the code, press Check, and the page runs it against a real Python interpreter.

Back to the preprocessing chapter

Exercise 1

Scale the features and print the mean and standard deviation of each column afterwards.

Python needs scikit-learn, downloaded on first run

import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[25.0, 30000.0], [26.0, 31000.0], [55.0, 30500.0], [56.0, 31500.0]])
# scale, then print the column means and stds, rounded

import numpy as np
from sklearn.preprocessing import StandardScaler

X = np.array([[25.0, 30000.0], [26.0, 31000.0], [55.0, 30500.0], [56.0, 31500.0]])
scaled = StandardScaler().fit_transform(X)
print(scaled.mean(axis=0).round(2))
print(scaled.std(axis=0).round(2))

Exercise 2

Encode the colours so no false ordering is invented.

Python needs scikit-learn, downloaded on first run

import numpy as np
from sklearn.preprocessing import OrdinalEncoder

colours = np.array([["red"], ["green"], ["blue"]])
print(OrdinalEncoder().fit_transform(colours).shape)

import numpy as np
from sklearn.preprocessing import OneHotEncoder

colours = np.array([["red"], ["green"], ["blue"]])
print(OneHotEncoder(sparse_output=False).fit_transform(colours).shape)

Exercise 3

Fit the scaler on the training data only, then transform both. Print the transformed test value.

Python needs scikit-learn, downloaded on first run

import numpy as np
from sklearn.preprocessing import StandardScaler

train = np.array([[10.0], [20.0], [30.0]])
test = np.array([[40.0]])
# fit on train only, print the transformed test value rounded to two places

import numpy as np
from sklearn.preprocessing import StandardScaler

train = np.array([[10.0], [20.0], [30.0]])
test = np.array([[40.0]])

scaler = StandardScaler().fit(train)
print(scaler.transform(test).round(2).ravel())