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IntuitionThe problem requires removing rows where the "name" column has missing (NaN) values. Pandas provides a built-in dropna() function, which efficiently re
# Intuition
The problem requires removing rows where the `"name"` column has missing (`NaN`) values. **Pandas** provides a built-in `dropna()` function, which efficiently removes these rows.

# Approach
1. Use `dropna(subset=["name"])`:
- The `subset=["name"]` parameter ensures that only rows where `"name"` is `NaN` are removed, while preserving other columns.
2. Return the cleaned DataFrame: The function returns the DataFrame without missing `"name"` values.

# Complexity
- Time complexity:
**O(n)** — The function iterates through n rows to check for missing values.

- Space complexity:
**O(1)** — The operation modifies the DataFrame in place without extra memory usage.

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

def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:

    return students.dropna(subset = "name")
    
```
['Pandas']