Remove Rows with Missing Values in the 'name' Column  
Intuition
The goal is to clean the data by removing rows where the 'name' column has missing values (NaN).
This ensures the dataset remains consistent for furth

## **Intuition**  
- The goal is to clean the data by removing rows where the **'name'** column has missing values (NaN).
- This ensures the dataset remains consistent for further analysis.  

---

## **Approach**  
1. **Use `dropna()`**:  
    - `dropna()` is a simple and efficient function in Pandas to remove rows with missing values.
2. **Specify the Column**:  
    - Use `subset=['name']` to ensure only the rows with missing values in the **'name'** column are removed, while preserving others.  



## **Complexity**  
- **Time Complexity:**  
  - \( O(n) \) → Linear time to scan and filter rows.  
- **Space Complexity:**  
  - \( O(1) \) → In-place operation if no copy is made, else \( O(n) \).  

---

## **Other Methods**  
 **Using Boolean Indexing**  
```python
return students[students['name'].notna()]
```
- **Time Complexity:** \( O(n) \)  
- **Space Complexity:** \( O(n) \)  

---
# Code
```pythondata []
import pandas as pd

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