Efficient Row Removal for Missing Names | Python solution | Time O(n) | Space O(m)
ApproachThe solution uses Pandas' built-in dropna() function to remove rows with missing data:

Use dropna() with subset parameter:

Specify 'name' column in su
# Approach

The solution uses Pandas' built-in dropna() function to remove rows with missing data:

1. Use dropna() with subset parameter:
   - Specify 'name' column in subset
   - This ensures only rows with missing names are dropped
   - Other columns with missing data are preserved

2. Return modified DataFrame:
   - The operation creates a new DataFrame without the dropped rows
   - Original column order and data types are maintained

This approach efficiently handles the problem by leveraging Pandas' optimized data manipulation functions.

# Complexity

- Time complexity: $$O(n)$$
  Where n is the number of rows in the DataFrame. The dropna() function needs to check each row once for the specified column.

- Space complexity: $$O(m)$$
  Where m is the number of rows with non-missing names. The new DataFrame contains all rows from the original except those with missing names, potentially using less space than the input.

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

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

```
['Pandas']