Handle Missing Data

Option 1: Drop all rows with missing data

This solution removes any row in the dataset that contains at least one missing value.

Python
def handle_missing_data(data: pd.DataFrame) -> pd.DataFrame:
    # Drop all rows with missing data
    return data.dropna()

Option 2: Drop all rows with missing GPA

This approach removes rows where the gpa value is missing, while retaining rows with missing values in other columns.

Python
def handle_missing_data(data: pd.DataFrame) -> pd.DataFrame:
    # Drop all rows with missing GPA
    return data.dropna(subset=['gpa'])

Option 3: Replace missing values

In this solution:

1. GPA Replacement: Missing gpa values are filled with the mean GPA of the dataset.
2. Credits Replacement: Missing credits_completed values are filled with the mean credits for students in the same year.
3. Year Replacement: Missing year values are filled with the most common year in the dataset.

Python
def handle_missing_data(data: pd.DataFrame) -> pd.DataFrame:
    # Replace missing GPA with mean GPA
    mean_gpa = data['gpa'].mean()
    data['gpa'] = data['gpa'].fillna(mean_gpa)

    # Replace missing credits_completed with mean credits_completed for the same year
    for year in data['year'].dropna().unique():
        mean_credits = data[data['year'] == year]['credits_completed'].mean()
        data.loc[(data['year'] == year) & (data['credits_completed'].isna()), 'credits_completed'] = mean_credits

    # Replace missing year with the most common year
    most_common_year = data['year'].mode()[0]
    data['year'] = data['year'].fillna(most_common_year)

    return data