Categorical Remapping with .map() and .replace() in Pandas

Question

Given a column `payment_code` with values `['UPI', 'CC', 'DC', 'COD']`, map them to full names `{'UPI': 'Unified Payments', 'CC': 'Credit Card', 'DC': 'Debit Card', 'COD': 'Cash on Delivery'}`.

Accepted Answer

Optimal Solution:
import pandas as pd

mapping = {
    'UPI': 'Unified Payments',
    'CC': 'Credit Card',
    'DC': 'Debit Card',
    'COD': 'Cash on Delivery'
}

# .replace() preserves values not found in dictionary
df['payment_name'] = df['payment_code'].replace(mapping)

# .map() turns values not found in dictionary into NaN

result_df = df[['payment_name']]

Technical Explanation:
`.replace(dict)` replaces matching keys while keeping unmapped keys intact. `.map(dict)` maps keys but converts unmapped keys into `NaN`.

payment_code
UPI
CC

payment_name
Unified Payments
Credit Card