Time Complexity = O(N) , Space Complexity = O(1) 😲💥⚡ || Easy Pandas Builtin Method Solution ✌
IntuitionThe problem requires removing rows from a DataFrame where the "name" column contains missing (NaN) values.
Pandas provides the dropna() function, which
# Intuition
The problem requires removing rows from a DataFrame where the "name" column contains missing (NaN) values.  
Pandas provides the `dropna()` function, which allows us to efficiently drop rows with missing data in a specified column.

# Approach
1. Use `dropna(subset=["name"], inplace=True)` to remove rows where the "name" column has NaN values.
2. Return the modified DataFrame.

# Complexity
- **Time complexity:**  
  - `dropna()` scans the "name" column, which takes **O(N)** where **N** is the number of rows.
  - **Overall:** $$O(N)$$.

- **Space complexity:**  
  - Since `inplace=True` modifies the DataFrame in place, no additional space is used except for temporary operations.
  - **Overall:** $$O(1)$$.


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

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