Label encoding in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LabelEncoder class from sklearn.

Data Analysis Python

from sklearn.preprocessing import [1]

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

AMinMaxScaler

BStandardScaler

COneHotEncoder

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong class like StandardScaler or OneHotEncoder.

Forgetting to import from sklearn.preprocessing.

The LabelEncoder class is used to convert categorical labels into numbers. It is found in sklearn.preprocessing.

2fill in blank

medium

Complete the code to create a LabelEncoder object named 'encoder'.

Data Analysis Python

encoder = [1]()

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

ALabelEncoder

BStandardScaler

COneHotEncoder

DMinMaxScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to create an instance of a scaler or one-hot encoder instead.

Forgetting the parentheses to call the class.

To use label encoding, you create an instance of LabelEncoder by calling it like a function.

3fill in blank

hard

Fix the error in the code to fit the encoder to the list of labels 'colors'.

Data Analysis Python

encoder.[1](colors)

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

Afit

Bfit_transform

Cencode

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting the encoder.

Using a non-existent method like encode.

The fit method learns the unique labels from the data. transform applies the encoding. fit_transform does both at once.

4fill in blank

hard

Fill both blanks to encode the list 'colors' and store the result in 'encoded_colors'.

Data Analysis Python

encoded_colors = encoder.[1](colors)
print([2])

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

Afit_transform

Bencoded_colors

Ccolors

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first.

Printing the original list instead of the encoded one.

fit_transform fits the encoder and transforms the data in one step. The result is stored in encoded_colors, which we print.

5fill in blank

hard

Fill all three blanks to create a label encoder, fit it to 'fruits', and transform 'fruits' into 'encoded_fruits'.

Data Analysis Python

encoder = [1]()
encoder.[2](fruits)
encoded_fruits = encoder.[3](fruits)

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

ALabelEncoder

Bfit

Ctransform

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform and then transform again.

Not fitting the encoder before transforming.

First, create the encoder with LabelEncoder(). Then fit it to learn labels with fit. Finally, transform the data with transform.

Practice

1. What does label encoding do in data science?

easy

A. It removes missing values from the dataset.

B. It normalizes numerical data to a 0-1 scale.

C. It converts categories into numbers for computer use.

D. It splits data into training and testing sets.

Solution

1. Step 1: Understand the purpose of label encoding

   Label encoding changes text categories into numbers so computers can process them.

2. Step 2: Compare with other options

   Other options describe different data tasks, not label encoding.

3. Final Answer:

   It converts categories into numbers for computer use. -> Option C

4. Quick Check:

   Label encoding = categories to numbers [OK]

Hint: Label encoding = categories to numbers [OK]

Common Mistakes:

• Confusing label encoding with normalization
• Thinking it removes missing data
• Mixing it up with data splitting

2. Which of the following is the correct way to import LabelEncoder from scikit-learn in Python?

easy

A. import LabelEncoder from sklearn

B. import LabelEncoder from sklearn.preprocessing

C. from sklearn import LabelEncoder

D. from sklearn.preprocessing import LabelEncoder

Solution

1. Step 1: Recall correct import syntax

   In Python, the correct way is 'from module import class'.

2. Step 2: Match with options

   from sklearn.preprocessing import LabelEncoder matches 'from sklearn.preprocessing import LabelEncoder'. Others have wrong syntax.

3. Final Answer:

   from sklearn.preprocessing import LabelEncoder -> Option D

4. Quick Check:

   Correct import = from sklearn.preprocessing import LabelEncoder [OK]

Hint: Use 'from module import class' syntax for imports [OK]

Common Mistakes:

• Using 'import class from module' which is invalid
• Trying to import directly from sklearn without submodule
• Confusing import order

3. What will be the output of this code?

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
categories = ['dog', 'cat', 'bird', 'cat']
encoded = le.fit_transform(categories)
print(list(encoded))

medium

A. [0, 1, 2, 1]

B. [2, 1, 0, 1]

C. [1, 0, 2, 0]

D. [1, 2, 0, 2]

Solution

1. Step 1: Understand label encoding order

   LabelEncoder assigns numbers based on alphabetical order: 'bird' = 0, 'cat' = 1, 'dog' = 2.

2. Step 2: Map categories to numbers

   Input list: ['dog', 'cat', 'bird', 'cat'] maps to [2, 1, 0, 1].

3. Final Answer:

   [2, 1, 0, 1] -> Option B

4. Quick Check:

   Alphabetical order encoding = [2,1,0,1] [OK]

Hint: LabelEncoder assigns numbers alphabetically starting at 0 [OK]

Common Mistakes:

• Assuming encoding starts at 1 instead of 0
• Ignoring alphabetical order
• Mixing up category positions

4. Identify the error in this code snippet:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
categories = ['apple', 'banana', 'apple']
encoded = le.transform(categories)
print(encoded)

medium

A. LabelEncoder must be fitted before transform is called.

B. The list 'categories' contains duplicates which is not allowed.

C. LabelEncoder cannot encode strings, only numbers.

D. The print statement syntax is incorrect.

Solution

1. Step 1: Check LabelEncoder usage

   LabelEncoder requires calling fit() or fit_transform() before transform().

2. Step 2: Identify missing fit step

   The code calls transform() without fitting, causing an error.

3. Final Answer:

   LabelEncoder must be fitted before transform is called. -> Option A

4. Quick Check:

   fit() before transform() is required [OK]

Hint: Always fit LabelEncoder before transform [OK]

Common Mistakes:

• Calling transform without fitting first
• Thinking duplicates cause error
• Believing LabelEncoder only works on numbers

5. You have a dataset with a column 'Color' containing ['Red', 'Blue', 'Green', 'Blue', 'Red']. You want to encode it using LabelEncoder but keep track of the original categories. Which code snippet correctly fits and transforms the data and then shows the mapping from labels to original categories?

hard

A. from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['Red', 'Blue', 'Green', 'Blue', 'Red']
encoded = le.fit_transform(colors)
mapping = dict(zip(range(len(le.classes_)), le.classes_))
print(encoded, mapping)

B. from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['Red', 'Blue', 'Green', 'Blue', 'Red']
encoded = le.transform(colors)
mapping = dict(zip(range(len(le.classes_)), le.classes_))
print(encoded, mapping)

C. from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['Red', 'Blue', 'Green', 'Blue', 'Red']
encoded = le.fit(colors)
mapping = dict(zip(le.classes_, range(len(le.classes_))))
print(encoded, mapping)

D. from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['Red', 'Blue', 'Green', 'Blue', 'Red']
encoded = le.fit_transform(colors)
mapping = dict(zip(le.classes_, range(len(le.classes_))))
print(encoded, mapping)

Solution

1. Step 1: Fit and transform the data correctly

   Use fit_transform() to fit and encode in one step.

2. Step 2: Create mapping from encoded numbers to original categories

   le.classes_ holds categories sorted alphabetically; zip with range(len(le.classes_)) maps numbers to categories.

3. Step 3: Check each option

   from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.fit_transform(colors) mapping = dict(zip(range(len(le.classes_)), le.classes_)) print(encoded, mapping) fits and transforms correctly and maps numbers to categories. from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.fit_transform(colors) mapping = dict(zip(le.classes_, range(len(le.classes_)))) print(encoded, mapping) reverses mapping. from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.transform(colors) mapping = dict(zip(range(len(le.classes_)), le.classes_)) print(encoded, mapping) calls transform without fit. from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.fit(colors) mapping = dict(zip(le.classes_, range(len(le.classes_)))) print(encoded, mapping) uses fit() without transform.

4. Final Answer:

   from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.fit_transform(colors) mapping = dict(zip(range(len(le.classes_)), le.classes_)) print(encoded, mapping) -> Option A

5. Quick Check:

   fit_transform + mapping numbers to classes = from sklearn.preprocessing import LabelEncoder le = LabelEncoder() colors = ['Red', 'Blue', 'Green', 'Blue', 'Red'] encoded = le.fit_transform(colors) mapping = dict(zip(range(len(le.classes_)), le.classes_)) print(encoded, mapping) [OK]

Hint: Use fit_transform and map numbers to le.classes_ [OK]

Common Mistakes:

• Calling transform without fitting first
• Using fit() without transform() or fit_transform()
• Mixing mapping direction (numbers to categories vs categories to numbers)