Fill Missing Data - Practice Coding Problems

Fill Missing Data - Problem

Databse Easy

You are given a DataFrame products with the following schema:

Column Name	Type
name	object
quantity	int
price	int

Write a solution to fill in the missing values as 0 in the quantity column.

Return the modified DataFrame with all missing quantity values replaced with 0.

Input & Output

Example 1 — Basic Missing Values

$ Input: products = [{'name': 'Apple', 'quantity': 10, 'price': 5}, {'name': 'Banana', 'quantity': null, 'price': 3}, {'name': 'Orange', 'quantity': 15, 'price': 4}]

› Output: [{'name': 'Apple', 'quantity': 10, 'price': 5}, {'name': 'Banana', 'quantity': 0, 'price': 3}, {'name': 'Orange', 'quantity': 15, 'price': 4}]

💡 Note: The Banana row has a missing quantity value (null), which gets replaced with 0. Other rows remain unchanged.

Example 2 — Multiple Missing Values

$ Input: products = [{'name': 'Laptop', 'quantity': null, 'price': 1000}, {'name': 'Mouse', 'quantity': 25, 'price': 20}, {'name': 'Keyboard', 'quantity': null, 'price': 50}]

› Output: [{'name': 'Laptop', 'quantity': 0, 'price': 1000}, {'name': 'Mouse', 'quantity': 25, 'price': 20}, {'name': 'Keyboard', 'quantity': 0, 'price': 50}]

💡 Note: Both Laptop and Keyboard have missing quantities, both get filled with 0. Mouse quantity remains unchanged.

Example 3 — No Missing Values

$ Input: products = [{'name': 'Phone', 'quantity': 100, 'price': 500}, {'name': 'Tablet', 'quantity': 50, 'price': 300}]

› Output: [{'name': 'Phone', 'quantity': 100, 'price': 500}, {'name': 'Tablet', 'quantity': 50, 'price': 300}]

💡 Note: No missing values in quantity column, so DataFrame remains exactly the same.

Constraints

1 ≤ products.length ≤ 1000
name is a non-empty string
quantity can be null or a non-negative integer
price is a positive integer

Visualization

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Understanding the Visualization

Input

DataFrame with missing quantity values (null/NaN)

Process

Apply fillna(0) to replace missing values

Output

Complete DataFrame with 0 for missing quantities

Key Takeaway

🎯 Key Insight: Use pandas fillna() for efficient vectorized missing value replacement - it's faster and cleaner than manual iteration

Asked in

N Netflix 25 S Spotify 20 U Uber 18 A Airbnb 15

The key insight is to use pandas' built-in fillna() method for efficient missing value replacement. Best approach is the optimized pandas method using products['quantity'].fillna(0). Time: O(n), Space: O(1)

Common Approaches

Approach	Time	Space	Notes
✓ Manual Row Iteration	O(n)	O(1)	Iterate through each row and manually check for missing quantity values
Pandas fillna() Method	O(n)	O(1)	Use pandas built-in fillna() method to efficiently replace missing values

Manual Row Iteration — Algorithm Steps

Iterate through all rows in the DataFrame
Check if quantity value is NaN for each row
Replace NaN values with 0
Return the modified DataFrame

Visualization

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Step-by-Step Walkthrough

Start

Begin with DataFrame containing missing values

Iterate

Check each row for missing quantity

Replace

Fill missing values with 0

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <cjson/cJSON.h>

cJSON* solution(cJSON* products) {
    // Manual iteration approach
    cJSON* item = NULL;
    cJSON_ArrayForEach(item, products) {
        cJSON* quantity = cJSON_GetObjectItem(item, "quantity");
        if (cJSON_IsNull(quantity) || quantity == NULL) {
            cJSON_SetNumberValue(cJSON_GetObjectItem(item, "quantity"), 0);
        }
    }
    return products;
}

int main() {
    char buffer[10000];
    fgets(buffer, sizeof(buffer), stdin);
    buffer[strcspn(buffer, "\n")] = 0;
    
    cJSON* products = cJSON_Parse(buffer);
    cJSON* result = solution(products);
    
    char* output = cJSON_PrintUnformatted(result);
    printf("%s\n", output);
    
    free(output);
    cJSON_Delete(products);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must visit each row once to check and potentially modify quantity

✓ Linear Growth

Space Complexity

O(1)

Modifying DataFrame in-place, no extra space needed

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Manual Row Iteration — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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