Detect and Quantify Missing Values with isna().sum() in Pandas

Question

Given a dataset, write a function that returns a summary DataFrame listing each column, its total count of missing values, and the percentage of missing values rounded to 2 decimal places.

Accepted Answer

Optimal Solution:
import pandas as pd

def missing_value_summary(df: pd.DataFrame) -> pd.DataFrame:
    missing_counts = df.isna().sum()
    missing_pct = (missing_counts / len(df) * 100).round(2)
    
    summary_df = pd.DataFrame({
        'missing_count': missing_counts,
        'missing_pct': missing_pct
    })
    return summary_df[summary_df['missing_count'] > 0].sort_values(
        by='missing_count', ascending=False
    )

Technical Explanation:
`df.isna().sum()` sums boolean True (1) values per column. Dividing by `len(df)` gives the proportional missing rate.

col_name	sample_missing
email	20 out of 100 missing
phone	5 out of 100 missing

column	missing_count	missing_pct
email	20	20
phone	5	5