Is Fill Missing Data easy or hard?

Fill Missing Data is generally classified as an easy problem. The solution relies on straightforward array traversal and conditional updates rather than advanced algorithms or data structures.

Fill Missing Data Python/Java solution

In Python, iterate through the list and replace missing values using conditional checks or built-in utilities. In Java, loop through the array and update elements directly while maintaining the last valid value. Both implementations run in O(n) time and require constant extra space.

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

Perform a single linear scan through the array. For each element, check if it represents missing data. If it does, replace it using the defined rule (such as the last valid value encountered). Maintaining a variable that stores the most recent valid entry allows the entire operation to complete in O(n) time.

What is the best approach for Fill Missing Data?

The most reliable approach is iterating through the sequence and replacing missing entries using conditional logic. Track the last valid value or compute the replacement according to the problem rule while scanning the array. This method runs in O(n) time and uses O(1) extra space when updates are done in place.

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

Problems involving filling or propagating missing values appear in interviews at major tech companies, especially in data-processing or backend roles. Variations test your ability to iterate efficiently, maintain state during traversal, and handle edge cases in arrays or datasets.

What data structure is used in Fill Missing Data?

The problem primarily uses arrays or lists because the data must be scanned sequentially. A small number of variables are used to track the most recent valid value or replacement rule, so no complex data structures are required.

What is the time complexity of Fill Missing Data?

The standard solution processes each element exactly once, resulting in O(n) time complexity where n is the number of data points. Space complexity is typically O(1) when modifying the array directly, though some library-based implementations may use O(n) if they return a new structure.

Description Hints 1 Solution Video 0:56

2887. Fill Missing Data

Easy72.0% acceptance3 companies

DataFrame products
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| name        | object |
| quantity    | int    |
| price       | int    |
+-------------+--------+

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

The result format is in the following example.

Example 1:
Input:+-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | None     | 135   |
| WirelessEarbuds | None     | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
Output:
+-----------------+----------+-------+
| name            | quantity | price |
+-----------------+----------+-------+
| Wristwatch      | 0        | 135   |
| WirelessEarbuds | 0        | 821   |
| GolfClubs       | 779      | 9319  |
| Printer         | 849      | 3051  |
+-----------------+----------+-------+
Explanation: 
The quantity for Wristwatch and WirelessEarbuds are filled by 0.

Asked at 3 companies

Acko

Google Microsoft

Hints open one at a time

Hint 1

Consider using a build-in function in pandas library to fill the missing values of specified columns.

0:56

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1class Solution:
  def solve(self):
      pass


Starter codePython 3Spaces: 4

Examples

Case 1

Input

{"headers":{"products":["name","quantity","price"]},"rows":{"products":[["Wristwatch",null,135],["WirelessEarbuds",null,821],["GolfClubs",779,9319],["Printer",849,3051]]}}

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