Solve it with "dropna"
IntuitionApproachWe can use the dropna() method to remove rows where the name column contains None or NaN: dropna(subset=['name'])
subset=['name']: Only checks
# Intuition
<!-- Describe your first thoughts on how to solve this problem. --> In the students DataFrame, some rows have None  values in the name column, meaning that these students have missing names. We need to remove these rows to ensure that the name column only contains valid student names.

# Approach
<!-- Describe your approach to solving the problem. -->
We can use the dropna() method to remove rows where the name column contains None or NaN: dropna(subset=['name'])
- subset=['name']: Only checks for missing values in the name column without affecting other columns.
- dropna() by default removes rows with None or Nan
- return students.dropna(subset=['name']): Returns the DataFrame after removing the affected rows.

# Results
- Runtime: 382 ms
<!-- Add your time complexity here, e.g. $$O(n)$$ -->

- Memory: 66.85 MB
<!-- Add your space complexity here, e.g. $$O(n)$$ -->

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

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