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Drop Missing Data Solution

In this tutorial, we will solve the Drop Missing Data problem. We will provide the implementation of the solution in Python.

Problem Description​

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.

Examples​

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.

Constraints​

  • You have to solve using python pandas only.

Solution for Drop Missing Data​

import pandas as pd

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    return students[students['name'].notnull()]

Complexity Analysis​

  • Time Complexity: O(n)O(n)
  • Space Complexity: O(n)O(n)
  • Iterating over the DataFrame: The notnull() function on the 'name' column performs an O(n) operation, iterating through each row of the DataFrame, where n is the number of rows.
  • Boolean indexing: Usually an O(n) process, this involves building a new DataFrame from the boolean mask (the output of notnull()).
  • New DataFrame: To hold the filtered data, the function builds a new DataFrame. The number of rows with complete "name" values determines the size of this new DataFrame, and in the worst scenario, that number may reach n. As a result, O(n) is also the space complexity.

Authors:

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