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.

Practice

1. What does one-hot encoding do in data science?

easy

A. It groups data into clusters automatically.

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

C. It removes missing values from the dataset.

D. It converts categorical data into multiple binary columns.

Solution

1. Step 1: Understand categorical data representation

   One-hot encoding changes categories into separate columns with 1 or 0 to represent presence or absence.

2. Step 2: Differentiate from other data processing methods

   Normalization scales numbers, missing value removal cleans data, clustering groups data; none create binary columns for categories.

3. Final Answer:

   It converts categorical data into multiple binary columns. -> Option D

4. Quick Check:

   One-hot encoding = binary columns for categories [OK]

Hint: One-hot encoding = categories to binary columns [OK]

Common Mistakes:

• Confusing one-hot encoding with normalization
• Thinking it removes missing data
• Assuming it groups data into clusters

2. Which Python code correctly applies one-hot encoding using pandas?

easy

A. pd.get_dummies(df['color'])

B. pd.one_hot(df['color'])

C. df.encode_onehot('color')

D. pd.to_dummies(df['color'])

Solution

1. Step 1: Recall pandas function for one-hot encoding

   The correct function is pd.get_dummies() to convert categorical columns into dummy variables.

2. Step 2: Check the syntax correctness

   Only pd.get_dummies(df['color']) is valid pandas syntax; others are incorrect or non-existent functions.

3. Final Answer:

   pd.get_dummies(df['color']) -> Option A

4. Quick Check:

   Use pd.get_dummies() for one-hot encoding [OK]

Hint: Use pd.get_dummies() to one-hot encode columns [OK]

Common Mistakes:

• Using non-existent pandas functions
• Calling methods on DataFrame incorrectly
• Confusing function names like to_dummies

3. What is the output of this code?

import pandas as pd
df = pd.DataFrame({'fruit': ['apple', 'banana', 'apple']})
dummies = pd.get_dummies(df['fruit'])
print(dummies)

medium

A. A DataFrame with a single column 'fruit' containing numeric codes.

B. A DataFrame with columns 'apple' and 'banana' showing 1 or 0 for each row.

C. An error because get_dummies requires a DataFrame, not a Series.

D. A DataFrame with original 'fruit' column unchanged.

Solution

1. Step 1: Apply pd.get_dummies on the 'fruit' column

   This creates new columns for each unique fruit: 'apple' and 'banana', with 1 where the fruit matches and 0 otherwise.

2. Step 2: Understand the printed output

   The printed DataFrame shows rows with 1 or 0 under 'apple' and 'banana' columns corresponding to each original row.

3. Final Answer:

   A DataFrame with columns 'apple' and 'banana' showing 1 or 0 for each row. -> Option B

4. Quick Check:

   get_dummies creates binary columns for unique categories [OK]

Hint: get_dummies creates one column per category with 1/0 [OK]

Common Mistakes:

• Expecting numeric codes instead of binary columns
• Thinking get_dummies needs a DataFrame, not Series
• Assuming original column stays unchanged

4. Identify the error in this code snippet:

import pandas as pd
df = pd.DataFrame({'color': ['red', 'blue', 'green']})
dummies = pd.get_dummies(df.color, drop_first=True)
print(dummies)

medium

A. Error because get_dummies cannot be applied on a Series.

B. Error because drop_first is not a valid parameter for get_dummies.

C. No error; code correctly drops the first category column.

D. Error because df.color should be df['color'].

Solution

1. Step 1: Check get_dummies parameters

   The parameter drop_first=True is valid and drops the first category column to avoid multicollinearity.

2. Step 2: Verify syntax and input type

   Using df.color is valid shorthand for df['color'], and get_dummies works on Series.

3. Final Answer:

   No error; code correctly drops the first category column. -> Option C

4. Quick Check:

   drop_first=True is valid in get_dummies [OK]

Hint: drop_first=True drops one dummy column to avoid redundancy [OK]

Common Mistakes:

• Thinking drop_first is invalid parameter
• Confusing df.color with incorrect syntax
• Believing get_dummies only works on DataFrames

5. You have a DataFrame with a column 'city' containing many categories, including some rare ones. How can you apply one-hot encoding but group rare categories into a single 'Other' category before encoding?

hard

A. Replace rare categories with 'Other' in 'city' column, then apply pd.get_dummies().

B. Apply pd.get_dummies() directly; it automatically groups rare categories.

C. Use pd.get_dummies() with parameter group_rare=True.

D. Remove rare categories before applying pd.get_dummies().

Solution

1. Step 1: Identify rare categories and replace them

   Manually replace rare categories in 'city' column with a common label like 'Other' to group them.

2. Step 2: Apply one-hot encoding on the modified column

   Use pd.get_dummies() on the updated 'city' column to create binary columns including 'Other'.

3. Final Answer:

   Replace rare categories with 'Other' in 'city' column, then apply pd.get_dummies(). -> Option A

4. Quick Check:

   Group rare categories manually before encoding [OK]

Hint: Group rare categories manually before one-hot encoding [OK]

Common Mistakes:

• Expecting get_dummies to group rare categories automatically
• Using non-existent parameters like group_rare
• Removing rare categories instead of grouping