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
    }).reset_index().rename(columns={'index': 'column'})

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.

customer_id	name	email	phone
1	Aarav	aarav@example.com	9820011111
2	Bhavna	NULL	9820022222
3	Chirag	chirag@example.com	9820033333
4	Divya	divya@example.com	9820044444
5	Esha	NULL	9820055555
6	Farhan	farhan@example.com	9820066666
7	Gauri	gauri@example.com	9820077777
8	Harsh	NULL	9820088888
9	Isha	isha@example.com	9820099999
10	Jatin	jatin@example.com	NULL
11	Kavya	kavya@example.com	9820011112
12	Lakshay	NULL	9820011113
13	Meera	meera@example.com	9820011114
14	Naveen	naveen@example.com	9820011115
15	Omkar	omkar@example.com	9820011116
16	Priya	priya@example.com	9820011117
17	Qasim	qasim@example.com	9820011118
18	Riya	riya@example.com	9820011119
19	Sahil	sahil@example.com	9820011120
20	Tara	tara@example.com	9820011121