[Documentation Update]: Add Solution to LeetCode Problem No 2883

dsa-solutions/lc-solutions/2800-2899/2883-Drop-Missing-Data.md

@@ -0,0 +1,91 @@
+---
+id: Drop-Missing-Data
+title: Drop Missing Data Solution
+sidebar_label: 2883 - Drop Missing Data
+tags:
+  - LeetCode
+  - Python
+description: "This is a solution to the Drop Missing Data problem on LeetCode."
+sidebar_position: 1
+---
+
+In this tutorial, we will solve the Drop Missing Data problem. We will provide the implementation of the solution in Python.
+
+## Problem Description
+
+```plaintext
+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:**
+
+```plaintext
+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
+
+```py
+import pandas as pd
+
+def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
+    return students[students['name'].notnull()]
+```
+
+### Complexity Analysis
+
+- **Time Complexity:** $O(n)$
+- **Space Complexity:** $O(n)$
+- **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.
+
+---
+
+<h2>Authors:</h2>
+
+<div style={{display: 'flex', flexWrap: 'wrap', justifyContent: 'space-between', gap: '10px'}}>
+{['avdhut-pailwan'].map(username => (
+ <Author key={username} username={username} />
+))}
+</div>