One-hot encoding in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of One-hot Encoding with pandas get_dummies

What is the output DataFrame after applying pd.get_dummies to the 'Color' column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Color': ['Red', 'Blue', 'Green', 'Blue']})
dummies = pd.get_dummies(df['Color'])
print(dummies)

A

   Blue  Green  Red
0     0      0    1
1     1      0    0
2     0      1    0
3     1      0    0

B

   Red  Blue  Green
0    1     0      0
1    0     1      0
2    0     0      1
3    0     1      0

C

   Blue  Green  Red
0     1      0    0
1     0      1    0
2     0      0    1
3     0      1    0

D

   Green  Blue  Red
0      0     1    0
1      1     0    0
2      0     0    1
3      1     0    0

💡 Hint

Check the alphabetical order of the columns created by get_dummies.

pandas.get_dummies creates columns in alphabetical order of unique values. Here, 'Blue', 'Green', 'Red' columns appear in that order.

❓ data_output

intermediate

Number of Columns After One-hot Encoding

Given a DataFrame with a column 'Fruit' containing ['Apple', 'Banana', 'Apple', 'Orange', 'Banana'], how many columns will the one-hot encoded DataFrame have for the 'Fruit' column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Orange', 'Banana']})
dummies = pd.get_dummies(df['Fruit'])
print(dummies.shape[1])

A 4

B 5

C 3

D 2

💡 Hint

Count unique values in the 'Fruit' column.

One-hot encoding creates one column per unique category. There are 3 unique fruits: Apple, Banana, Orange.

🔧 Debug

advanced

Identify the Error in One-hot Encoding Code

What error will this code raise when trying to one-hot encode the 'Category' column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'Category': ['A', 'B', 'C']})
encoded = pd.get_dummies(df.Category, drop_first=True)
print(encoded)

A TypeError: get_dummies() got an unexpected keyword argument 'drop_firsts'

B No error, prints the encoded DataFrame

C NameError: name 'encoded' is not defined

D SyntaxError: unexpected EOF while parsing

💡 Hint

Check the parentheses in the function call.

The code is missing a closing parenthesis in the get_dummies call, causing a SyntaxError.

🚀 Application

advanced

Choosing One-hot Encoding for Machine Learning

Why is one-hot encoding preferred over label encoding for categorical variables in many machine learning models?

A Because one-hot encoding avoids implying an order or priority among categories.

B Because one-hot encoding reduces the number of features compared to label encoding.

C Because label encoding creates multiple columns which confuse models.

D Because label encoding is only used for numerical data, not categorical.

💡 Hint

Think about how models interpret numbers assigned to categories.

Label encoding assigns numbers that can imply order, which may mislead models. One-hot encoding treats categories independently.

🧠 Conceptual

expert

Effect of One-hot Encoding on Data Dimensionality

What is a common drawback of using one-hot encoding on a categorical feature with very high cardinality (many unique values)?

A It converts numerical features into categorical ones, which is undesirable.

B It causes the model to treat all categories as the same, reducing accuracy.

C It automatically removes rare categories, losing important information.

D It can cause the dataset to become very large and sparse, increasing memory and computation needs.

💡 Hint

Think about how many new columns are created for many unique categories.

One-hot encoding creates one column per unique category, so many categories create many columns, leading to sparse data and higher resource use.