Find Missing Values by Column

Scenario

A customer table will feed a retention analysis. Before cleaning it, the team needs a concise view of missingness by column.

Problem statement

Calculate the number and percentage of missing values in each DataFrame column, ordered from most missing to least.

Your Task

Use Pandas missing-value detection rather than comparing every value manually.
Report both counts and rates.
Explain why a high missing rate does not automatically mean a column should be dropped.

Example data

import pandas as pd

customers = pd.DataFrame({
    'customer_id': [1, 2, 3, 4],
    'age': [31, None, 44, None],
    'city': ['Pune', 'Delhi', None, 'Pune'],
    'email': ['a@example.com', 'b@example.com', 'c@example.com', 'd@example.com'],
})

Related Concepts

Pandas isnaImputationMissing-not-at-random

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