Scaling and normalization concepts in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to scale the data using Min-Max scaling.

Data Analysis Python

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
data_scaled = scaler.[1](data)

Drag options to blanks, or click blank then click option'

Afit_transform

Btransform

Cfit

Dscale

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' without fitting first causes an error.

Using 'fit' alone does not transform the data.

MinMaxScaler requires fitting and transforming the data. The fit_transform method does both in one step.

2fill in blank

medium

Complete the code to normalize the data using L2 norm.

Data Analysis Python

from sklearn.preprocessing import Normalizer
normalizer = Normalizer(norm='[1]')
data_normalized = normalizer.fit_transform(data)

Drag options to blanks, or click blank then click option'

Anone

Bmax

Cl1

Dl2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'l1' norm changes the scaling method.

Using 'max' norm scales by the maximum value, not L2.

The L2 norm scales each sample so that the sum of squares equals 1.

3fill in blank

hard

Fix the error in the code to standardize the data.

Data Analysis Python

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data_standardized = scaler.[1](data)

Drag options to blanks, or click blank then click option'

Ascale

Btransform

Cfit_transform

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' without fitting causes an error.

Using 'fit' alone does not transform the data.

StandardScaler requires fitting and transforming the data. The fit_transform method does both steps together.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' includes words of length 3 or less.

Using 'word' instead of 'len(word)' gives the word itself, not its length.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words mapped to their lengths for words longer than 4 characters.

Data Analysis Python

result = { [1]: [2] for word in words if len(word) [3] 4 }

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Aword.upper()

Blen(word)

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word.lower()' instead of uppercase.

Using '<' or '<=' instead of '>' for filtering.

The dictionary comprehension creates keys as uppercase words and values as their lengths, only for words longer than 4 characters.