One-hot encoding in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is one-hot encoding in data science?

One-hot encoding is a way to turn categories into numbers by creating new columns for each category. Each column shows 1 if the category is present, and 0 if not.

beginner

Why do we use one-hot encoding before machine learning?

Machine learning models need numbers, not words. One-hot encoding changes categories into numbers so models can understand and learn from them.

beginner

Which Python library is commonly used for one-hot encoding?

Pandas is commonly used for one-hot encoding with its function called get_dummies().

beginner

What does the output look like after one-hot encoding a column with values ['red', 'blue', 'red']?

You get two new columns: one for 'red' and one for 'blue'. Rows with 'red' have 1 in the 'red' column and 0 in 'blue'. Rows with 'blue' have 1 in 'blue' and 0 in 'red'.

intermediate

What is a potential downside of one-hot encoding?

It can create many new columns if there are many categories, which can make the data big and slow to work with.

What does one-hot encoding do to a categorical column?

A Creates new columns for each category with 1 or 0 values

B Replaces categories with their length

C Sorts categories alphabetically

D Removes categories with low frequency

One-hot encoding creates new columns for each category and marks presence with 1 and absence with 0.

Which pandas function is used for one-hot encoding?

A pd.fillna()

B pd.to_numeric()

C pd.merge()

D pd.get_dummies()

pd.get_dummies() converts categorical variables into one-hot encoded columns.

If a column has 4 unique categories, how many new columns will one-hot encoding create?

A 1

B 3

C 4

D 2

One-hot encoding creates one new column for each unique category.

Why might one-hot encoding increase data size?

A Because it adds many new columns for categories

B Because it duplicates rows

C Because it changes data types to strings

D Because it removes missing values

One-hot encoding adds a new column for each category, increasing the number of columns.

Which type of data is one-hot encoding mainly used for?

A Time series data

B Categorical data

C Numerical data

D Text data

One-hot encoding is used to convert categorical data into numerical form.

Explain what one-hot encoding is and why it is useful in data science.

Think about how computers understand data and why categories need to be numbers.

Describe a simple example of one-hot encoding with a small list of colors.

Imagine you have colors like red, blue, and green.

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