#0040

Fill Missing Values By Group Median

MediumPandas22 min15 XP

Problem

Given a DataFrame with city and income columns, fill missing income values with the median income of that same city.

Why This Matters

Real datasets often have missing values, and group-aware imputation is more realistic than one global fill value.

Function Signature

def fill_income_by_city_median(df):

Examples

Example 1

Inputcity A incomes [100, missing, 300], city B incomes [50, missing, 70]

Outputcity A missing becomes 200, city B missing becomes 60

Each missing value uses the median from its own city, not the whole dataset.

Constraints

Return a new DataFrame.
Keep the original row order.
Do not fill a city if all its incomes are missing.

CodePython

def fill_income_by_city_median(df):
    """
    Input example: city A incomes [100, missing, 300], city B incomes [50, missing, 70]
    Return: city A missing becomes 200, city B missing becomes 60
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # df has columns: city, income
    # write your code here
    return df

def fill_income_by_city_median(df):
    """
    Input example: city A incomes [100, missing, 300], city B incomes [50, missing, 70]
    Return: city A missing becomes 200, city B missing becomes 60
    Return the value from the function so the tests can compare it. Do not print unless the prompt asks.
    """
    # df has columns: city, income
    # write your code here
    return df

Visible browser tests run here when available.

Testcases1 visible / 4 hidden categories

Two city medians

Inputcity A and B incomes with missing values

Expectedmissing values filled by city median

transform returns one median value aligned to each original row.

Hidden Test Categories

City with no missing valuesCity with all missing valuesOdd and even group sizesOriginal DataFrame mutation check