Drop Missing Data - Practice Coding Problems

Drop Missing Data - Problem

Databse Easy

You are given a DataFrame called students with the following schema:

Column Name	Type
student_id	int
name	object
age	int

Some rows in the DataFrame have missing values in the name column (represented as null or NaN).

Write a solution to remove all rows that contain missing values in the name column and return the cleaned DataFrame.

Input & Output

Example 1 — Basic Missing Data

$ Input: students = [{"student_id": 1, "name": "Alice", "age": 20}, {"student_id": 2, "name": null, "age": 21}]

› Output: [{"student_id": 1, "name": "Alice", "age": 20}]

💡 Note: Row with student_id=2 has missing name (null), so it's removed. Only Alice's record remains.

Example 2 — Multiple Missing Names

$ Input: students = [{"student_id": 1, "name": "Bob", "age": 22}, {"student_id": 2, "name": null, "age": 23}, {"student_id": 3, "name": "Charlie", "age": 24}]

› Output: [{"student_id": 1, "name": "Bob", "age": 22}, {"student_id": 3, "name": "Charlie", "age": 24}]

💡 Note: Student with ID 2 has missing name, so removed. Bob and Charlie remain.

Example 3 — No Missing Data

$ Input: students = [{"student_id": 1, "name": "David", "age": 25}, {"student_id": 2, "name": "Eve", "age": 26}]

› Output: [{"student_id": 1, "name": "David", "age": 25}, {"student_id": 2, "name": "Eve", "age": 26}]

💡 Note: All students have valid names, so no rows are removed.

Constraints

1 ≤ students.length ≤ 1000
Each row contains student_id (int), name (string or null), age (int)
Missing values in name column are represented as null

Visualization

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Understanding the Visualization

Input DataFrame

Original data with some missing names

Filter Missing

Remove rows where name is null

Clean Output

DataFrame with complete records only

Key Takeaway

🎯 Key Insight: Use pandas dropna() method to efficiently filter out incomplete records

Asked in

M Meta 15 N Netflix 12

The key insight is to use pandas' built-in dropna() method with the subset parameter to efficiently remove rows with missing values in the name column. Best approach is the optimized method using students.dropna(subset=['name']). Time: O(n), Space: O(n)

Common Approaches

Approach	Time	Space	Notes
✓ Manual Row-by-Row Filtering	O(n)	O(n)	Iterate through each row and check if name is missing
Pandas dropna() Method	O(n)	O(n)	Use pandas built-in dropna() method for efficient filtering

Manual Row-by-Row Filtering — Algorithm Steps

Iterate through each row in the DataFrame
Check if the name value is null/NaN
Keep only rows where name is not missing

Visualization

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Step-by-Step Walkthrough

Check Each Row

Iterate through DataFrame rows

Filter Missing

Keep rows where name is not null

Clean Result

Return filtered DataFrame

Code -

solution.c — C

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

char* solution(const char* students_json) {
    cJSON *students = cJSON_Parse(students_json);
    cJSON *filtered = cJSON_CreateArray();
    
    cJSON *student;
    cJSON_ArrayForEach(student, students) {
        cJSON *name = cJSON_GetObjectItem(student, "name");
        if (name && !cJSON_IsNull(name)) {
            cJSON_AddItemToArray(filtered, cJSON_Duplicate(student, 1));
        }
    }
    
    char *result = cJSON_PrintUnformatted(filtered);
    cJSON_Delete(students);
    cJSON_Delete(filtered);
    return result;
}

int main() {
    char input[10000];
    fgets(input, sizeof(input), stdin);
    input[strcspn(input, "\n")] = 0;
    
    char* result = solution(input);
    printf("%s\n", result);
    free(result);
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n)

Need to check each row once where n is number of rows

✓ Linear Growth

Space Complexity

O(n)

Create new DataFrame to store filtered results

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Manual Row-by-Row Filtering — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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