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}

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

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 }

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

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.

Practice

1. What is the main purpose of scaling and normalization in data analysis?

easy

A. To convert data into categorical variables

B. To adjust data to a common scale for fair comparison

C. To remove missing values from the data

D. To increase the size of the dataset

Solution

1. Step 1: Understand the goal of scaling and normalization

   Scaling and normalization adjust data values so they can be compared fairly by putting them on the same scale.

2. Step 2: Eliminate unrelated options

   Increasing dataset size, removing missing values, or converting to categories are different data tasks, not scaling.

3. Final Answer:

   To adjust data to a common scale for fair comparison -> Option B

4. Quick Check:

   Scaling purpose = common scale adjustment [OK]

Hint: Scaling makes data comparable by using the same scale [OK]

Common Mistakes:

• Confusing scaling with data cleaning
• Thinking scaling changes data size
• Mixing scaling with encoding categorical data

2. Which of the following is the correct syntax to apply Min-Max scaling using scikit-learn in Python?

easy

A. from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data)

B. from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_data = scaler.fit(data)

C. import sklearn
scaled_data = sklearn.MinMaxScaler(data)

D. from sklearn.preprocessing import MinMaxScaler
scaled_data = MinMaxScaler(data)

Solution

1. Step 1: Recall correct Min-Max scaling syntax

   MinMaxScaler is imported, instantiated, then fit_transform is called on data.

2. Step 2: Check each option

   from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() scaled_data = scaler.fit_transform(data) matches correct usage; B uses StandardScaler and fit only; C and D misuse the class.

3. Final Answer:

   from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() scaled_data = scaler.fit_transform(data) -> Option A

4. Quick Check:

   MinMaxScaler usage = import, instantiate, fit_transform [OK]

Hint: MinMaxScaler needs fit_transform, not just fit [OK]

Common Mistakes:

• Using StandardScaler instead of MinMaxScaler
• Calling fit instead of fit_transform
• Incorrect import or instantiation syntax

3. Given the data array [10, 20, 30], what is the result after applying Min-Max scaling to range 0-1?

medium

A. [0.0, 0.5, 1.0]

B. [10, 20, 30]

C. [-1.0, 0.0, 1.0]

D. [0.33, 0.66, 1.0]

Solution

1. Step 1: Calculate min and max of data

   Minimum is 10, maximum is 30.

2. Step 2: Apply Min-Max formula to each value

   Formula: (x - min) / (max - min). For 10: (10-10)/(30-10)=0, for 20: (20-10)/20=0.5, for 30: (30-10)/20=1.0.

3. Final Answer:

   [0.0, 0.5, 1.0] -> Option A

4. Quick Check:

   Min-Max scaled values = [0, 0.5, 1] [OK]

Hint: Min-Max scales min to 0 and max to 1 [OK]

Common Mistakes:

• Forgetting to subtract min before dividing
• Confusing Min-Max with Standard scaling
• Incorrect division by max instead of (max-min)

4. Identify the error in this code snippet for standard scaling:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler
scaled = scaler.fit_transform(data)

medium

A. fit_transform method does not exist

B. StandardScaler should be imported from sklearn.preprocessing.preprocessing

C. Data variable is not defined

D. StandardScaler is not instantiated with parentheses

Solution

1. Step 1: Check StandardScaler instantiation

   StandardScaler must be called with parentheses to create an object: StandardScaler()

2. Step 2: Verify other parts

   fit_transform exists; data variable assumed defined; import path is correct.

3. Final Answer:

   StandardScaler is not instantiated with parentheses -> Option D

4. Quick Check:

   Instantiate classes with () [OK]

Hint: Remember to add () when creating scaler object [OK]

Common Mistakes:

• Forgetting parentheses when instantiating scaler
• Assuming fit_transform is missing
• Incorrect import paths

5. You have a dataset with features on very different scales. You want to prepare it for a machine learning model that assumes data centered around zero with unit variance. Which scaling method should you use and why?

hard

A. No scaling, because models handle different scales automatically

B. Min-Max scaling, because it rescales data to 0-1 range

C. Standard scaling, because it centers data to mean 0 and scales to unit variance

D. Log transformation, because it normalizes data distribution

Solution

1. Step 1: Understand model assumptions

   The model expects data centered at zero with variance 1, which matches standard scaling.

2. Step 2: Compare scaling methods

   Min-Max scales to 0-1, not centered; log transforms distribution shape but not centering; no scaling ignores assumptions.

3. Final Answer:

   Standard scaling, because it centers data to mean 0 and scales to unit variance -> Option C

4. Quick Check:

   Centering and scaling = StandardScaler [OK]

Hint: Use StandardScaler for zero mean and unit variance data [OK]

Common Mistakes:

• Choosing Min-Max when centering is needed
• Skipping scaling assuming model handles it
• Confusing log transform with scaling