🚀1-row solution that beats 99999999,99%🚀
ApproachTo solve this problem I read pandas.DataFrame.dropna that remove missing valuesMain parameters:
axis:

0: Drop rows which contain missing values.
1: Dro
# 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.
- subset - only consider certain columns for identifying NA values

Goodbye, my friends!
<3

![image.png](https://assets.leetcode.com/users/images/4e312034-8c4d-463c-ad0d-8627d46238cd_1736964852.5444682.png)

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

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