Easy Explanation for beginners 💡
IntuitionOur goal is to remove rows where the name column is NaN.
Use dropna() to Remove Missing Valuesdropna(subset=['name']) removes rows where the name colum
# Intuition
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Our goal is to remove rows where the name column is NaN.
Use **dropna(**) to Remove Missing Values

**dropna(subset=['name'])** removes rows where the name column is missing.
The **subset=['name']** ensures that only the name column is checked for NaN, while other columns are ignored.

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

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