Dropping the row with NULL values
Drop Rows with Missing Data in 'name' ColumnIntuition
First thoughts: The aim is to ensure that the dataset contains valid values in critical columns like name.
# Drop Rows with Missing Data in 'name' Column

## Intuition
- **First thoughts**: The aim is to ensure that the dataset contains valid values in critical columns like `name`.
- **Reasoning**: Rows with missing `name` values might not be useful for analysis and should be removed to maintain data integrity.

## Approach
- **Library import**: Import the pandas library for data manipulation.
- **Function definition**: Define a function `dropMissingData` that takes a DataFrame as input.
- **Dropping rows**: Use the `dropna` method with `subset=['name']` to drop rows where the `name` column has null values.
- **Return result**: Return the updated DataFrame with rows containing null values in the `name` column removed.

## Complexity
- **Time complexity**: 
  - The operation to drop rows with null values in a specific column is $$O(n)$$, where $$n$$ is the number of rows in the DataFrame.
- **Space complexity**: 
  - The space complexity is $$O(1)$$, as it operates on the original DataFrame without requiring additional space proportional to the input size.

## Code
```python
import pandas as pd

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

['Pandas']