🚀 Python3: Optimized `dropna()` Solution | ⏱ 316ms (🔥 Top 88.87%) | 📦 66.60MB (⚡ Beats 53.88%)
IntuitionTo efficiently clean the dataset, we need to remove rows where the "name" column contains missing or invalid data. Instead of manually checking values,
# Intuition  
To efficiently clean the dataset, we need to remove rows where the `"name"` column contains missing or invalid data. Instead of manually checking values, we leverage **`dropna()`** to remove `NaN` values directly, ensuring a more optimized solution.

# Approach  
1. Use `dropna(subset=["name"])` to remove rows where `"name"` is `NaN`.  
2. Filter out unwanted string representations of missing values (`"None"`, `"null"`, `"NaN"`) using `.isin()`.  
3. Return the cleaned DataFrame.

# Complexity  
- **Time complexity:** \(O(n)\) – `dropna()` and `isin()` both operate in linear time.  
- **Space complexity:** \(O(n)\) – A boolean mask is created for filtering.

# Code  
```python3 []
import pandas as pd

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    return students.dropna(subset=["name”])
```
['Pandas']