Using .map() and .replace()

Welcome to **Day 119**. Today we learn how to "Translate" categories.

The Mapping Pattern

Instead of a big `CASE` statement (SQL) or many `IF`s (Python), we use a dictionary.

status_map = {

'S': 'Shipped',

'P': 'Pending',

'C': 'Cancelled'

}

df['status_full'] = df['status_code'].map(status_map)

.replace()

`.replace()` is similar but works for specific values across the whole DataFrame.

# Replace multiple errors at once

df = df.replace({'Error': 0, 'Incomplete': -1})

Why .map() is faster

Behind the scenes, `.map()` is highly optimized. It's the standard way to turn "Status Codes" into "Human Readable Labels" in a production pipeline.

Your Task for Today

Create a mapping dictionary for 'US States' (e.g., {'NY': 'New York'}) and apply it to a column using `.map()`.

*Day 120: Ranking and Binning Data (pd.cut).*