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Data Analysis Pythondata~20 mins

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

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Challenge - 5 Problems
🎖️
Label Encoding Master
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Test your skills under time pressure!
❓ Predict Output
intermediate
2:00remaining
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]
BRaises a ValueError because 'yellow' was not seen during fit
C[0 1 2]
D[1 2 0]
Attempts:
2 left
💡 Hint

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

❓ data_output
intermediate
2:00remaining
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]
Attempts:
2 left
💡 Hint

LabelEncoder assigns labels in alphabetical order.

❓ visualization
advanced
3:00remaining
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()
AScatter plot of original fruit names vs encoded values
BLine plot showing encoded values over index
CBar chart with fruits on x-axis and counts of encoded labels on y-axis
DPie chart of encoded label counts
Attempts:
2 left
💡 Hint

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

🧠 Conceptual
advanced
1:30remaining
Understanding label encoding limitations

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

AIt introduces an unintended ordinal relationship between categories
BIt cannot handle numerical data
CIt always increases the dimensionality of the dataset
DIt requires categories to be sorted alphabetically
Attempts:
2 left
💡 Hint

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

🔧 Debug
expert
2:30remaining
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)
ATypeError: unorderable types: int() < str()
BValueError: y contains previously unseen labels
CNo error, prints encoded array
DAttributeError: 'int' object has no attribute 'lower'
Attempts:
2 left
💡 Hint

Consider how LabelEncoder sorts categories internally.