#0077

One-Hot Encode a Categorical Column

MediumML20 min20 XP

Problem

Given a list of category strings, return a list of dictionaries. Each dictionary should contain one key per sorted category in the form prefix_category, with 1 for the row category and 0 otherwise.

Why This Matters

Most ML models need numeric inputs. One-hot encoding is the first categorical encoding every beginner should understand deeply.

Function Signature

def one_hot(values, prefix):

Examples

Example 1

Inputvalues = ["red", "blue", "red"], prefix = "color"

Output[{"color_blue":0,"color_red":1}, {"color_blue":1,"color_red":0}, {"color_blue":0,"color_red":1}]

Each category becomes its own binary feature.

Constraints

Sort categories alphabetically.
Return one dictionary per input value.
Values are strings.

CodePython

def one_hot(values, prefix):
    """
    Input example: values = ["red", "blue", "red"], prefix = "color"
    Return: [{"color_blue":0,"color_red":1}, {"color_blue":1,"color_red":0}, {"color_blue":0,"color_red":1}]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

def one_hot(values, prefix):
    """
    Input example: values = ["red", "blue", "red"], prefix = "color"
    Return: [{"color_blue":0,"color_red":1}, {"color_blue":1,"color_red":0}, {"color_blue":0,"color_red":1}]
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # write your code here
    return []

Visible browser tests run here when available.

Testcases1 visible / 3 hidden categories

Two categories

Input["red", "blue", "red"], "color"

Expectedthree encoded rows

Sorted categories create color_blue then color_red.

Hidden Test Categories

single categoryempty inputthree categories