Fill Missing Data - Practice Coding Problems

Fill Missing Data - Problem

Database Easy

You are working with a DataFrame containing product information from an e-commerce inventory system. The DataFrame has three columns: name (product name), quantity (stock quantity), and price (product price).

Due to data collection issues, some entries in the quantity column are missing (represented as NaN or None values). Your task is to clean the data by filling all missing values in the quantity column with 0.

This is a common data preprocessing step in machine learning and data analysis workflows.

Column Name	Type
name	object
quantity	int
price	int

Goal: Return the DataFrame with all missing quantity values replaced with 0.

Input & Output

example_1.py — Basic Missing Values

$ Input: products = pd.DataFrame({ 'name': ['Apple', 'Banana', 'Orange'], 'quantity': [10, None, 5], 'price': [1, 2, 3] })

› Output: name quantity price 0 Apple 10 1 1 Banana 0 2 2 Orange 5 3

💡 Note: The missing value (None) in the second row's quantity column is replaced with 0, while other values remain unchanged.

example_2.py — Multiple Missing Values

$ Input: products = pd.DataFrame({ 'name': ['Apple', 'Banana', 'Orange', 'Mango'], 'quantity': [10, np.nan, 5, None], 'price': [1, 2, 3, 4] })

› Output: name quantity price 0 Apple 10 1 1 Banana 0 2 2 Orange 5 3 3 Mango 0 4

💡 Note: Both np.nan and None values in the quantity column are replaced with 0. The function handles different types of missing value representations.

example_3.py — No Missing Values

$ Input: products = pd.DataFrame({ 'name': ['Apple', 'Banana'], 'quantity': [10, 15], 'price': [1, 2] })

› Output: name quantity price 0 Apple 10 1 1 Banana 15 2

💡 Note: When there are no missing values in the quantity column, the DataFrame remains unchanged. This demonstrates the method works correctly for clean data.

Visualization

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Understanding the Visualization

Identify Missing Data

Scan the quantity column to locate NaN/None values

Apply fillna() Method

Use pandas vectorized operation to replace all missing values

Verify Data Types

Ensure quantity column maintains integer type after filling

Return Clean DataFrame

Output DataFrame with consistent, complete data

Key Takeaway

🎯 Key Insight: Using pandas' `fillna()` method is the most efficient approach because it performs vectorized operations at the C level, making it significantly faster than manual iteration for data cleaning tasks.

Time & Space Complexity

Time Complexity

⏱️

O(n)

Linear scan through the column, but highly optimized in C

✓ Linear Growth

Space Complexity

O(1)

Can modify in place or create copy efficiently

✓ Linear Space

Constraints

The DataFrame will always have exactly 3 columns: name, quantity, and price
1 ≤ number of rows ≤ 10⁵
Missing values in quantity column can be None, NaN, or other pandas null representations
Only the quantity column should be modified - other columns remain unchanged

Asked in

G Google 45 a Amazon 38 f Meta 32 ⊞ Microsoft 28

The optimal solution uses pandas' built-in fillna() method: products['quantity'].fillna(0). This leverages highly optimized C implementations for vectorized operations, making it significantly faster than manual iteration approaches. The method handles all types of missing values (NaN, None) automatically and is the industry standard for data cleaning tasks.

Common Approaches

Approach	Time	Space	Notes
✓ Brute Force (Row-by-Row Check)	O(n)	O(1)	Iterate through each row and manually check/replace missing values
Pandas fillna() (Optimal)	O(n)	O(1)	Use pandas built-in fillna() method for efficient bulk replacement

Brute Force (Row-by-Row Check) — Algorithm Steps

Iterate through each row in the DataFrame
Check if the 'quantity' value is NaN or None
If missing, replace with 0
Continue until all rows are processed

Visualization

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Step-by-Step Walkthrough

Start with DataFrame

Initialize with products data containing NaN values

Check Row 1

quantity = 10 (valid, no change needed)

Check Row 2

quantity = NaN (missing, replace with 0)

Check Row 3

quantity = 5 (valid, no change needed)

Complete

All rows processed, missing values filled

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_NAME_LEN 50
#define MAX_PRODUCTS 100

typedef struct {
    char name[MAX_NAME_LEN];
    int quantity;
    int price;
    int has_quantity; // 1 if quantity is valid, 0 if missing
} Product;

void fillnaProducts(Product* products, int count) {
    for (int i = 0; i < count; i++) {
        if (!products[i].has_quantity) {
            products[i].quantity = 0;
            products[i].has_quantity = 1;
        }
    }
}

int main() {
    Product products[] = {
        {"Apple", 10, 1, 1},
        {"Banana", 0, 2, 0},  // quantity missing
        {"Orange", 5, 3, 1}
    };
    
    int count = sizeof(products) / sizeof(products[0]);
    
    printf("Before filling:\n");
    for (int i = 0; i < count; i++) {
        printf("Name: %s, Quantity: %s, Price: %d\n", 
               products[i].name,
               products[i].has_quantity ? 
                   (char[]){products[i].quantity + '0', '\0'} : "NULL",
               products[i].price);
    }
    
    fillnaProducts(products, count);
    
    printf("\nAfter filling:\n");
    for (int i = 0; i < count; i++) {
        printf("Name: %s, Quantity: %d, Price: %d\n", 
               products[i].name, products[i].quantity, products[i].price);
    }
    
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Must check every row in the DataFrame

✓ Linear Growth

Space Complexity

O(1)

Only storing temporary variables, modifying in place

✓ Linear Space

Constraints

The DataFrame will always have exactly 3 columns: name, quantity, and price
1 ≤ number of rows ≤ 10⁵
Missing values in quantity column can be None, NaN, or other pandas null representations
Only the quantity column should be modified - other columns remain unchanged

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Input & Output

Visualization

Time & Space Complexity

Related Problems

Constraints

Common Approaches

Brute Force (Row-by-Row Check) — Algorithm Steps

Visualization

Code -

Time & Space Complexity

Constraints

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