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.