Label encoding in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of label encoding with unseen category

What is the output of this code snippet using LabelEncoder from sklearn.preprocessing?

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
categories = ['red', 'green', 'blue']
le.fit(categories)
encoded = le.transform(['green', 'blue', 'yellow'])
print(encoded)

Data Analysis Python

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
categories = ['red', 'green', 'blue']
le.fit(categories)
encoded = le.transform(['green', 'blue', 'yellow'])
print(encoded)

A [1 2 3]

B Raises a ValueError because 'yellow' was not seen during fit

C [0 1 2]

D [1 2 0]

💡 Hint

Think about what happens if you try to transform a category that the encoder did not learn.

The LabelEncoder only knows categories it was fit on. Trying to transform an unseen category like 'yellow' causes a ValueError.

❓ data_output

intermediate

Resulting encoded array from label encoding

Given this code, what is the printed output?

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['yellow', 'red', 'blue', 'red', 'yellow']
le.fit(colors)
encoded = le.transform(colors)
print(encoded)

Data Analysis Python

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
colors = ['yellow', 'red', 'blue', 'red', 'yellow']
le.fit(colors)
encoded = le.transform(colors)
print(encoded)

A [0 2 1 2 0]

B [0 1 2 1 0]

C [1 2 0 2 1]

D [2 1 0 1 2]

💡 Hint

LabelEncoder assigns labels in alphabetical order.

The categories sorted alphabetically are ['blue', 'red', 'yellow'], so 'blue' → 0, 'red' → 1, 'yellow' → 2.

❓ visualization

advanced

Visualizing label encoding effect on categorical data

You have a DataFrame with a column 'Fruit' containing ['Apple', 'Banana', 'Apple', 'Cherry', 'Banana']. You apply label encoding to this column. Which plot best shows the encoded values distribution?

Data Analysis Python

import pandas as pd
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt

df = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Cherry', 'Banana']})
le = LabelEncoder()
df['Fruit_encoded'] = le.fit_transform(df['Fruit'])

plt.bar(le.classes_, df['Fruit_encoded'].value_counts().sort_index())
plt.xlabel('Fruit')
plt.ylabel('Encoded Value Count')
plt.title('Count of Encoded Fruit Labels')
plt.show()

A Scatter plot of original fruit names vs encoded values

B Line plot showing encoded values over index

C Bar chart with fruits on x-axis and counts of encoded labels on y-axis

D Pie chart of encoded label counts

💡 Hint

Think about how to show counts of each encoded label clearly.

A bar chart with fruit names on the x-axis and counts of their encoded labels on the y-axis clearly shows the distribution of encoded categories.

🧠 Conceptual

advanced

Understanding label encoding limitations

Which of the following is a key limitation of label encoding when used on categorical features for machine learning?

A It introduces an unintended ordinal relationship between categories

B It cannot handle numerical data

C It always increases the dimensionality of the dataset

D It requires categories to be sorted alphabetically

💡 Hint

Think about what the numbers assigned by label encoding imply to some algorithms.

Label encoding assigns integer values to categories, which can make some algorithms think there is an order or ranking, even if none exists.

🔧 Debug

expert

Debugging label encoding with mixed data types

What error does this code raise?

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data = ['cat', 1, 'dog', 2]
le.fit(data)
encoded = le.transform(data)
print(encoded)

Data Analysis Python

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data = ['cat', 1, 'dog', 2]
le.fit(data)
encoded = le.transform(data)
print(encoded)

A TypeError: unorderable types: int() < str()

B ValueError: y contains previously unseen labels

C No error, prints encoded array

D AttributeError: 'int' object has no attribute 'lower'

💡 Hint

Consider how LabelEncoder sorts categories internally.

LabelEncoder tries to sort the categories, but Python 3 cannot compare strings and integers, causing a TypeError.

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)