Is Drop Missing Data easy or hard?

Drop Missing Data is classified as an easy problem. The solution requires a simple linear scan and a condition to skip rows containing missing values, making it a good practice exercise for beginners learning array or dataset processing.

Drop Missing Data Python/Java solution

In Python, the problem is often solved using a DataFrame operation such as dropna on the required column, which internally performs a linear scan. In JavaScript, a similar result can be achieved with the array filter method that returns only rows where the field is not null or undefined.

How to solve Drop Missing Data in O(n)?

Iterate through the dataset and check whether the target column contains a missing value such as null or None. If the value exists, keep the row; otherwise skip it. Because the dataset is scanned only once, the overall time complexity remains O(n).

What is the best approach for Drop Missing Data?

The filtering approach is typically the best solution. You scan the dataset once and keep only rows where the required field is not null or missing. This method runs in O(n) time and is easy to implement using built-in filtering or drop functions in languages like Python or JavaScript.

Is Drop Missing Data asked at Google/Amazon/Meta?

Problems involving filtering invalid or missing records appear frequently in interviews at large tech companies because they test clean data traversal and validation logic. While the exact problem title may vary, the underlying pattern of scanning and filtering rows is common in data processing questions.

What data structure is used in Drop Missing Data?

The problem typically operates on a table-like structure such as an array of records or a DataFrame. The solution mainly relies on sequential traversal and conditional checks rather than advanced data structures.

What is the time complexity of Drop Missing Data?

The standard solution runs in O(n) time because each row in the dataset must be checked exactly once for missing values. Space complexity is usually O(n) if a new filtered collection is created, or O(1) extra space if the dataset is modified in place.

Description Hints 1 Solution Video 3:34

2883. Drop Missing Data

Easy64.1% acceptance

DataFrame students
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| student_id  | int    |
| name        | object |
| age         | int    |
+-------------+--------+

There are some rows having missing values in the name column.

Write a solution to remove the rows with missing values.

The result format is in the following example.

Example 1:

Input:
+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 32         | Piper   | 5   |
| 217        | None    | 19  |
| 779        | Georgia | 20  |
| 849        | Willow  | 14  |
+------------+---------+-----+
Output:
+------------+---------+-----+
| student_id | name    | age |
+------------+---------+-----+
| 32         | Piper   | 5   |
| 779        | Georgia | 20  | 
| 849        | Willow  | 14  | 
+------------+---------+-----+
Explanation: 
Student with id 217 havs empty value in the name column, so it will be removed.

Hints open one at a time

Hint 1

Consider using a build-in function in pandas library to remove the rows with missing values based on specified data.

3:34

Still stuck?Read 2 approaches, or watch a walkthrough.

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1class Solution:
  def solve(self):
      pass


Starter codePython 3Spaces: 4

Examples

Case 1

Input

{"headers":{"students":["student_id","name","age"]},"rows":{"students":[[32,"Piper",5],[217,null,19],[779,"Georgia",20],[849,"Willow",14]]}}

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