Simple Solution Using Dropna()
ApproachTo solve this problem I read pandas.DataFrame.dropna that remove missing valuesMain parameters:
axis:
0: Drop rows which contain missing values.
1: Drop

# Approach
To solve this problem I read pandas.DataFrame.dropna that remove missing values

Main parameters:

- axis:
0: Drop rows which contain missing values.
1: Drop columns which contain missing value.
- how:
any: If any NA values are present, drop that row or column.
all: If all values are NA, drop that row or column.<!-- Describe your approach to solving the problem. 


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

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